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https://www.cnet.com/news/toshibas-industrial-ar-for-windows-is-more-google-glass-than-hololens/

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Sarah Tew/CNET

I don't get to wear a hard hat to work all that often. This one has a heads-up display on the side. It reminds me of using Google Glass. Or, many other single-lens types of AR that use heads-up displays instead of any holographic effects.

In essence: it feels unexciting. But it's not designed to be wild and crazy: it's designed to work properly. Fast video calls to show what someone is seeing in the field. Checking instructions. Getting annotated notes. Reporting photos and video back to offsite bosses to document what's being worked on.

Toshiba's newest smart glass for Windows is for enterprise and field work, not everyday consumers. It's practical AR. Toshiba's solution aims to work with Windows 10, instead of a stand-alone or mobile platform. And it made me realize that AR for mission-critical fields is a totally different proposition than AR for everyone else.

The Toshiba dynaEdge AR Smart Glasses are an $1899 bundle that includes Toshiba's compact Windows 10 Pro clip-on PC (the dynaEdge DE-100 Mobile PC) and a head-mounted clip-on display (the dynaEdge AR 100 Head Mounted Display) made by Vuzix, a company that's already made tons of similar smartglass products. The PC part debuted last year, but the glasses part is new.

Sarah Tew/CNET

The adjustable display comes with frames that sit on top of regular glasses, but can also slot into a glasses-like pair of work goggles and, yes, a hard hat. It slots into accessories easily, sort of like the controllers on the Nintendo Switch.

Toshiba's dynaEdge is a super-small PC, but less powerful than Intel's VR-capable NUC. It's got an Intel Core M processor, enough to drive the Smart Glasses' single display. Toshiba's AR doesn't do much, though, by design. A simple d-pad on the PC's body handles navigation through menus, and there's also a touchpad on the arm of the head-mounted HUD. You can start a Skype call, take photos and video, look up documents to display, and run programs that guide wearers to scan QR codes in factory-type settings. That's about it.

Sarah Tew/CNET

Notably absent is any voice-based AI or any voice transcription. Namely, because errors made via AI could result in mission-critical errors in the field. In that sense, workplace AR is a lot different than experimental, always-in-beta consumer AR. It's about helping and being instantly available, like a phone. The Smart Glasses were simple to use, unimpressive-looking, and I was able to get started intuitively.

And the "easy" part is exactly what most AR is usually missing. I'm not a field worker, I don't work in factories, and I have no idea if Toshiba's proposal is any better than other solutions like Epson's Moverio glasses, Microsoft's , Daqri's AR headwear, or even Google Glass (which is still around). But if an industrial workplace was all-Windows, maybe it's worth looking into.

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Toshiba's Windows 10 AR glasses is augmented reality for the workplace

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I don't get to wear a hard hat to work all that often. This one has a heads-up display on the side. It reminds me of using Google Glass. Or, many other single-lens types of AR that use heads-up displays instead of any holographic effects. In essence: it feels unexciting.

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"We are in the Age of the Customer. If you look at AI, it is really advanced algorithms plus big data to give actionable intelligence. This improves how companies relate to customers and improve the customer experience."
above quote from article below
http://www.businesstimes.com.sg/the-raffles-conversation/future-perfect

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GEORGE Colony, the founder, chairman and CEO of research agency Forrester, is worried about computer programmes that can beat grandmasters of chess and of the Chinese game of Go, without breaking a sweat.

But he is not the least concerned about these super-smart programmes taking over the

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THE BUSINESS TIMES The Raffles Conversation - GEORGE Colony, the founder, chairman and CEO of research agency Forrester, is worried about computer programmes that can beat grandmasters of chess and of the Chinese game of Go, without breaking a sweat.. Read more at The Business Times.

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http://www.bbc.com/future/story/20180126-meet-the-motorbike-racing-robot

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The Thunderhill Raceway in California's Sacramento Valley is soaked in the oil of motorsport history.

It is home to the longest automobile race in the United States, known as the 25 Hours of Thunderhill, and last September, it was host to a very different kind of race: a race between man and machine.

In the distance, a motorbike driver took the curves of the course like any other professional rider. It was only up close that reality hit the spectator: the rider wasn't human. It was a blue humanoid robot that looked like it had stepped straight out of a screenshot from the computer game Halo.

Halo is set in an interstellar war between humanity and a bunch of aliens called the Covenant; you get the picture. When this robot - Motobot - stops driving, you feel like it could climb off the bike and hunt you down as well - but it can't, yet.

Motobot 2.0 is a fully autonomous motorcycle-riding robot that was specially designed to drive around a racetrack at high speed on a Yamaha YZF-R1M, the same kind of motorbike that racing legend Valentino Rossi rides. Human operators can specify how aggressively Motobot should race on a scale of zero to 100% and it does the rest. This process is roughly parallel to how a racing team might discuss strategy with a human rider. The bike itself looks like the classic modern aerodynamic racing bike that overtakes you on the motorway.

In September, the team that developed Motobot 2.0 achieved one of its goals when the robot successfully hit speeds exceeding 200km/h (124 mph) on the race track - 50 km/h faster than its predecessor Motobot 1.0. Unfortunately, or fortunately, depending on your point of view, this still fell short of the lap time of Valentino Rossi - which they were trying to beat - by about 30 seconds.

We had several major crashes, two were catastrophic - Hiroshi Saijou

Rossi is one of the most successful motorcycle racers of all time, with nine Grand Prix World Championships to his name. His best lap time on the course was a very quick 85 seconds. Motobot tried to break Rossi's record a second time in October - and failed again.

"We had several major crashes, two were catastrophic," says Hiroshi Saijou, CEO and managing director at Yamaha Motor Ventures & Laboratory Silicon Valley, and the lead on the Motobot project. "Nobody was injured, and both happened in safe, to human, controlled circumstances. Several times we've had low-speed incidents as we were developing and testing Motobot. This is all part of the price of pushing the boundaries of technology."

Motobot started life in 2014 as a 'moonshot' project for Japanese motorcycle manufacturer Yamaha. Moonshots are those "it would be great if they worked but..." projects: ambitious, exploratory and ground-breaking but with little hope of short-term profitability.

Yamaha's initial concept was a "humanoid robot that can ride a motorcycle autonomously" and the company teamed up with SRI International to achieve its vision. SRI, the Stanford Research Institute, as it was originally known, was founded in 1946 to be the cutting edge of innovation in Silicon Valley. The institute has been responsible for the development of such projects as Apple assistant Siri, the computer mouse and humanoid robots such as Proxi, which is designed to assist humans after a natural disaster. Back in 1966 they built the first mobile robot with the ability to perceive and reason about its surroundings.

"Why a motorbike?" ponders Hiroshi Saijou. "Because it is very difficult to do, and it had never been done before."

"The significance of the 200km/h goal was that it requires extremely high-speed prevision computing. Calculations must take place at 1/1000 of a second - and a minor mistake would be amplified and impossible for Motobot to recover from.

"Most human riders do not have the experience to ride at this speed. So, we set this speed as a good enough target to show that Motobot's abilities were superior to human's. Beating Rossi would have been clear evidence that Motobot can perform beyond human capabilities."

For Brian Foster, robotics engineer and Motobot project lead at SRI International, another goal of the project was to "learn what makes a great rider.

"How riders sense the limits of traction, optimise power output of a bike and recover from exceeding the bike's limits without crashing," he says. "Using an unmodified bike was key to this and set the playing field for evaluating the robot versus the human competition."

This rule meant that the designers then had to deal with constraints on all sorts of things like geometry, the size of the actuators that control the robot's movements, where sensors were placed and more factors that wouldn't have been an issue in a purpose-built vehicle. Yes, Motobot was physically attached to the bike but its hand was still required to grip and twist the throttle.

On the other hand, the robot didn't need to use cameras or lasers to navigate like an autonomous car would because it wasn't a public road. It could use technologies like the simpler GPS and IMUs (inertial measurement unit), that are often used to control such things as drones and satellites.

There were, however, plenty of challenges for the engineers to face before the robot could ride a bike very fast around the track without crashing it.

"Our first big challenge was the balance controller," says Foster. "Motobot had to be taught how to balance the bike at lean angles from zero to over 50 degrees, at speeds between 5km/h and over 200 km/h. And it had to be able to change bank angles rapidly and precisely. The control algorithms to do this were constantly refined as we got close to our final high-performance version.

"Similarly, the path-following algorithm had to work well at high-speed straights, sweeping turns, hairpin turns, strong acceleration, and strong deceleration. Developing a controller that was adaptive to such a wide range of extreme conditions was a huge challenge.

"From my perspective though, the biggest challenge was identifying performance limits without crashing," Foster adds. "To improve the algorithms, we had to constantly push it to the limit to see where improvements were needed. If we pushed too hard, we could crash and lose everything. If we didn't push hard enough, we wouldn't learn enough, and our progress would be too slow. It was a constant risk balance exercise."

We went back and forth discussing what the limits to the competition should be - Stephen Morfey

To try to reduce the risk, Foster and his team would bring Motobot and the bike into the lab, where they ran a very sophisticated simulation whereby the robot would apply brakes and shift gears as if it were racing on the track. The sensors would then feed the data back into the simulation hundreds of times per second.

"Ultimately, nothing perfectly replicates the real world, so we still needed a lot of track time and had to manage the risks that come with that," Foster says.

Hiroshi Saijou thinks the "cost to learn" is the reason why we didn't see any depressing headlines about AI beating another human world champion.

"The most significant one is the cost - not only money but time and resources - to learn," he says. "AI for a board game, such as AlphaGo, can learn how to play and how to win pretty quickly since there is no risk of it getting damaged. I believe that there were millions of failures before it eventually won over a human champion.

"For Motobot, the learning cost is way more expensive and repairs take a long time. So, we needed to take extraordinary care each time we did a trial."

Perhaps Motobot needs a jetpack to beat Rossi.

"We went back and forth discussing what the limits to the competition should be," says Stephen Morfey, a roboticist and now director of Morfey Design, a robot design consultancy. He was the lead mechanical designer on the Motobot project in its first phase and worked on other humanoid robots for SRI International like the walking bot Durus. "Jet thrusters weren't allowed, but it could be aerodynamically shaped. We decided that physically attaching Motobot to the bike wasn't cheating because its hands had to grip the handlebars."

"At the start of the project, controlling Motobot was like playing a video game," he adds. "You set the speed and told it the direction you wanted it to go. By the end, after I had left, it was autonomous."

It would have been much easier to beat Rossi, he thinks, by designing from scratch a very fast autonomous two-wheeled vehicle. "No, we didn't beat Rossi. Why not? Because it is a hard problem," he says. "There are hundreds of different variables that you must consider. In principle, you can get a robot to optimise all this stuff, but in practice, it is much harder."

While the failure of Motobot to beat Rossi's time may have dented the pride of the engineering team involved, important lessons were learned.

The future of Motobot, it seems, might be on two legs. Motobot is different from most humanoid robots because it doesn't walk... yet. But future versions might be able to walk up to the bike and get on it.

A kind of retrofitted autonomy, applicable to modern day problems, may have been made possible through their research and experiments. For example, in coming years, developing nations could use humanoid robots like Motobot to operate the perfectly good tractors and diggers that would have been replaced with new and expensive autonomous versions.

SRI International is already working with Chilean mining company Enaex to develop a rather freaky-looking remotely operated robot called Robominer that has the head, two arms and torso of a humanoid robot on four wheels.

Would Hiroshi Saijou classify Motobot a success? "It is still on the way. We have learned a lot in the last three years and will use this knowledge in our products in the future. It has huge potential for us to get real success in our business," he says. "What we learned is so unique that it would have been hard to get without Motobot. We are actively working on Motobot 3.0. Please stay tuned."

Rossi, you have been warned.

If you liked this story, sign up for the weekly bbc.com features newsletter, called "If You Only Read 6 Things This Week". A handpicked selection of stories from BBC Future, Earth, Culture, Capital, and Travel, delivered to your inbox every Friday.

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The Thunderhill Raceway in California's Sacramento Valley is soaked in the oil of motorsport history. It is home to the longest automobile race in the United States, known as the 25 Hours of Thunderhill, and last September, it was host to a very different kind of race: a race between man and machine.

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https://www.facebook.com/PhilosophyMttrs/posts/1249242801841831

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If you want to run the world, study a 'useless' subject, don't pick philosophy, because it turns out that Philosophy Matters ...

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Post by PhilosophyMttrs.

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https://cloud.google.com/automl/

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Cloud AutoML - Custom Machine Learning Models | Google Cloud Platform

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Cloud AutoML helps you easily train high quality custom machine learning models with limited machine learning expertise needed.

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https://www.theverge.com/2018/1/17/16901126/google-cloud-ai-services-automl

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Offering off-the-peg AI services like image and speech recognition is a key part of Google's pitch to customers as a cloud computing provider. The company is now taking its AI-easy approach one step further with Cloud AutoML, a new tool that will let users train their own custom machine learning algorithms from scratch, without having to write a single line of code.

You might have heard of Google's AutoML initiative before now. It was announced at the company's I/O conference last year, and is focused on creating machine learning software that can design machine learning software, a hot area of research in the AI community. (The basic premise is simple: you make different algorithms compete with one another, pick the winners, and then make them compete. Rinse and repeat.) Cloud AutoML isn't working with tools as sophisticated as this, but it does aim to solve the same underlying problem of making AI less painful to code.

Cloud AutoML does this by offering users a simple graphical interface for training their own machine learning model. So far, the service is limited to image recognition, letting users drag and drop a set of pictures, and then watching as the software starts picking out recurrent elements or items. Urban Outfitters, for example, is testing how Cloud AutoML might be used to identify items of clothing in their catalog, so users can filter by certain characteristics.

In a blog post, Google's chief scientist of cloud AI, Fei-Fei Li, said: "Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. There's a very limited number of people that can create advanced machine learning models. [...] While Google has offered pre-trained machine learning models via APIs that perform specific tasks, there's still a long road ahead if we want to bring AI to everyone."

Google certainly has a sterling reputation when it comes to artificial intelligence, but it remains to be seen if tools like Cloud AutoML can help it take business away from competitors like Microsoft's Azure and market leader Amazon Web Services. Both these companies also offer their own pre-trained AI tools as well as tools for building and customizing models. Google has been very successful at getting its AI smarts to help consumers, but can it convince businesses to sign up, too?

You can see what Cloud AutoML looks like below:

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Google's new cloud service lets you train your own AI tools, no coding knowledge required

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Offering off-the-peg AI services like image and speech recognition is a key part of Google's pitch to customers as a cloud computing provider. The company is now taking its AI-easy approach one step further with Cloud AutoML, a new tool that will let users train their own custom machine learning algorithms from scratch, without having to write a single line of code.

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https://www.channelnewsasia.com/news/technology/google-launches-cloud-automl-wants-ai-available-for-all-9870320

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The first service to be released is Cloud AutoML Vision, which Google says makes it "faster and easier" to create custom machine learning models for image recognition.

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SINGAPORE: Imagine allowing zoos to automate the tagging of animal images, which could lead to better conservation efforts. Or Disney annotating its products so they are more searchable for consumers shopping online.

Those are some real-world implementations of Google's Cloud AutoML technology, which it launched globally on Wednesday (Jan 17).

According to a blog post by the tech titan's Cloud AI head of R&D Jia Li and chief scientist at Cloud AI Fei-Fei Li, only a handful of businesses in the world have access to the talent and budgets needed to "fully appreciate the advancements of machine learning and artificial intelligence".

They also acknowledged that while Google has offered pre-trained machine learning models via application programming interface (APIs) that perform specific tasks, "there's still a long road ahead if we want to bring AI to everyone".

Cloud AutoML was launched to "close this gap" as it helps businesses with limited machine learning expertise to start building their own custom models using "advanced techniques" like learning2learn and transfer learning.

"We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of," the Google executives wrote.

On Cloud AutoML Vision specifically, they said its drag-and-drop interface helps users easily upload images, train and manage models and then deploy these trained models directly on Google Cloud.

Early results using this technology to classify popular public datasets like ImageNet have shown "more accurate results with fewer misclassifications" than generic machine learning APIs, they added.

Disney and the Zoological Society of London (ZSL) are among the companies partnering Google to be early users of this technology.

According to Ms Sophie Maxwell, conservation technology lead at ZSL, a key requirement of its mission to conserve animals and habitats worldwide is to track their populations, learn more about their distribution, and understand the impact of humans on these species.

To do so, ZSL has deployed a series of camera traps in the wild that takes pictures of animals using heat or motion triggers.

"The millions of images captured by these devices are then manually analysed and annotated with the relevant species such as elephants, lions and giraffes, etc, which is a labour-intensive and expensive process," Ms Maxwell said in the blog post.

As such, the organisation is working with the CloudML team to automate the tagging of these images, which she said would cut costs, enable wider scale deployments and help them gain a deeper understanding of how to conserve the world's wildlife effectively.

As for Disney, the technology is being used to build models to annotate its products with Disney characters, product categories and colours.

"These annotations are being integrated into our search engine to enhance the impact on guest experience through more relevant search results, expedited discovery and product recommendations on shopDisney," said Mr Mike White, CTO and SVP for Disney consumer products and interactive media.

Google said those interested in trying out AutoML Vision, including Singapore companies, can request for access via its dedicated website.

Once the company is given access, they can use their data and train the model for free, senior director of product management for Google Cloud AI Rajen Sheth told Channel NewsAsia in an email.

However, for a more accurate model, Google will charge companies according to what computing resources they consume, as it does on Google Cloud. It will also charge users for the number of times they access the model, just like how it charges for APIs, he explained.

"We will work directly with the first set of customers to understand the use cases they have for model prediction," Mr Sheth said. "That is something we want to make sure we are working with our customers to understand what's the best pricing for prediction."

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Google launches Cloud AutoML, wants AI available for all businesses

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The first service to be released is Cloud AutoML Vision, which Google says makes it "faster and easier" to create custom machine learning models for image recognition. Share on Facebook Share on Twitter Share via E-Mail SINGAPORE: Imagine allowing zoos to automate the tagging of animal images, which could lead to better conservation efforts.

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http://www.bbc.com/news/technology-42619807http://www.bbc.com/news/technology-42619807

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In Las Vegas this week you can learn a lot about the exciting potential of artificial intelligence. You can also be left wondering whether AI is a triumph of marketing, yet to deliver real improvements to the economy and the way we live.

One of my first stops here was at a University of Las Vegas robotics lab. Scientists there were working on projects ranging from drones to virtual reality, but they were also collaborating with the team behind one of the stars of the robot world,

Sophia is a humanoid robot with a face that strays dangerously close to that "Uncanny Valley" where machines look just a little too human for comfort. She achieved fame and some notoriety when Saudi Arabia named her its first robot citizen, a stunt which saw many question whether machines were now being given freedoms denied to Saudi women.

Sophia is the work of Hanson Robotics, an American firm with a base in Hong Kong. We had come to see her take her first halting steps on legs which are the work of this university lab.

Then it was time for a handshake and a chat, with Sophia responding remarkably articulately to my questions - though I should say she'd had advance notice of what I'd ask.

In many ways, this a hugely impressive project combining expertise in robotics, speech recognition and machine learning developed by American and Chinese scientists.

But when I ask what practical purpose she serves, Hanson Robotics founder David Hanson is somewhat vague, mentioning work with autistic children and a role as a work of art.

He is, however, a man with a startling ambition to use Sophia as a platform to achieve the holy grail, Artificial General Intelligence where machines can outperform humans at any intellectual task.

He admits she is, in many ways, about as intelligent as an amoeba at present, but is hopeful that as she interacts with humans she will learn and grow. "Our aspiration is to bring the machines to life," he tells me.

It's a vision that will be met with scepticism by scientists who think the humanoid path is the wrong route for AI, and with fear from those nervous about killer robots.

Elsewhere we find more immediate attempts to turn AI into saleable products. Voice-controlled devices are everywhere, with Amazon's Alexa now facing a more sustained challenge from the Google and its Assistant. The two giants seem convinced that our homes will soon echo to the sound of people yelling orders at everything from the light switches to the microwave.

And out on the Las Vegas streets the battle to prove self-driving cars are imminent is in full swing. I hitched a ride in an autonomous taxi, a collaboration between Uber rival Lyft and the technology firm Aptiv which has bought up AI teams from both Carnegie Mellon and MIT.

The autonomous driving mode was impressive, keeping a close eye on pedestrians, but as soon as we arrived at Caesars Palace the human driver had to take over - the casinos are still cautious about allowing this kind of technology on their land.

In downtown Las Vegas, I then hopped into a more futuristic vehicle, a taxi with no driver, no steering wheel or pedals. It made its way around a block of streets set aside for autonomous vehicles, but it was not the smoothest ride, with the vehicle lurching to a halt at any hint of an obstacle. Still, Navya the French transport company behind this project, insists its Autonom taxi will be in service in cities in the United States and Australia very soon,

But the most confident statement about artificial intelligence at the conference was made by Baidu. Its AI supremo Qi Lu took to a stage to explain to a Las Vegas audience what his company did - "China's Google" - and why his country was going to close the gap with its American rivals. It was all about sheer scale - China had far more people, producing far more data and there was what he described as a "friendly policy environment".

That presumably means controversial technologies such as facial recognition can be deployed more quickly, creating a virtuous circle where ever more data makes the AI systems improve ever faster.

But as this data gold rush continues and the hype around AI gets ever louder, it is worth remembering the great paradox - in a time of great technological change productivity growth has ground to a halt.

Ever waited ages at an airport to "drop" your bag after thinking you'd done the work by checking in online? Or stood at a hotel reception desk as endless pieces of data are entered into a computer and thought wistfully of the days when you signed a form and were handed a key?

Then you have experienced that productivity paradox and may be sceptical that smart machines learning from vast amounts of data will make our lives more simple and productive.

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In Las Vegas this week you can learn a lot about the exciting potential of artificial intelligence. You can also be left wondering whether AI is a triumph of marketing, yet to deliver real improvements to the economy and the way we live.

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http://www.bbc.com/news/technology-42574569

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All aboard the (self-driving) bus - next stop, CES: Las Vegas' annual gigantic tech fest.

About 4,000 companies - many of them start-ups - are arriving in town this weekend. Over the coming days, they will reveal new products, secure orders and hopefully provide a taste of the future at the trade fair.

The event has its roots in consumer gadgets, but now sprawls into fields including artificial intelligence, automobiles, medicine, marketing and even agriculture.

Most of the big technology brands in attendance will have something new to brag about. But increasingly, they hold flagship products back for stand-alone events.

In recent years much of the excitement has instead been delivered by smaller, lesser-known companies for whom CES presents a "break-out" opportunity.

Below is a sample of what to expect, including several exclusive hands-on videos with some of the new tech:

Artificial intelligence

If one firm could be said to have "won" last year's expo, it was Amazon.

From fridges to cars, watches to robots, device-makers fell over themselves to support or build in its Alexa smart helper, leaving Google's Assistant trailing in its wake.

New cooking controls have just been added to the AI's capabilities, so watch out for a flurry of Alexa-connected microwaves this time round.

Google is, however, still in the fight.

Earlier this week, LG revealed its latest top-end TVs will feature the Assistant.

And this year, the search giant has booked a CES booth to show off its AI, as well as splurging on a new paint job for the Las Vegas monorail to promote it.

While the battle to secure the most tie-ups will likely be intense, one trend to look out for is products that provide the best of both worlds.

GE has pre-announced a ceiling light that supports both companies' virtual assistants, and HTC already sells a smartphone that answers to both AIs' wake words.

"We believe customer choice is important, and that multiple AIs can be complementary of each other," an Amazon spokesman told the BBC.

Elsewhere, expect "AI-enhanced" to be the buzz phrase of the moment, even if it's not always clear what that means.

"AI has become an overused term - often it just refers to there being a voice component or related cloud service," commented Simon Bryant from the consultancy Futuresource.

He added that the key question to ask was whether the promise of artificial intelligence meant a device or service would become better over time.

So, to take one example, when HiRide Suspensions promises to show off a smart bicycle suspension system that uses AI, does it mean its electronics will learn to deliver a smoother ride over time, or merely that they have developed algorithms that would always deal with the same bump in the road in the same way?

The Italian start-up's pre-CES materials are unclear on the matter.

Health

The so-called wellness market could prove to be CES' most vibrant sector this year.

One recent forecast suggested the world's annual healthcare spend will be nearly $9tn by the end of the decade. Both the big brands and start-ups believe there's an opportunity to disrupt a sector currently dominated by specialists.

Doubtless, not all the claims being made at CES will stand up to scrutiny, but it's heartening to see new tech trying to do something truly useful.

For example, Israel's ICI Vision is in town to promote a pair of prototype glasses designed to tackle blind spots caused by retinal diseases.

It is trying to combine small cameras, eye-tracking software and projection tech to direct views onto the healthy parts of the back of a patient's eye.

Samsung is taking a different approach to visual impairments with Relumino - glasses that use a smartphone to process the wearer's view.

An app adds contrast, draws outlines and makes other colour changes to the view to make it clearer before floating the altered image into the eyewear's display.

Several firms are seeking to treat undesired behaviour via vibration-based "haptic" feedback.

They include Keen - a smart bracelet that buzzes if it detects the wearer pulling their hair or picking their skin - and Snore Circle - an eye-mask that vibrates at different levels of intensity to nudge the owner into a different position if they make noises at night.

New parents also appear to be a favoured target for the latest health tech.

Stand-out launches include Me.Mum, a smartphone camera attachment whose maker claims it can detect mould-like particles in a woman's saliva that signal when she is at her most fertile.

China's Tuoxiao will be demoing a smart stethoscope designed for use with infants that sends heart and lung readings to the cloud for analysis to determine if pneumonia might be present.

Meanwhile, two European start-ups are seeking to help women strengthen their pelvic floor muscles to combat bladder leaks caused by childbirth.

Fizimed's solution involves exercising with a force-sensing silicone device that provides feedback via an app.

Lifesense Group's offering centres on smart underwear that tracks the reduction of urine loss over time in order to motivate its owner to keep exercising.

Smart home

The surprise success of smart speakers has meant that the smart home and wider "internet-of-things" category is finally taking off.

A flood of water use-tracking, temperature-adjusting, humidity-measuring, pollution-detecting gizmos will be on show, as well as dozens of smart locks - even though consumers remain suspicious about letting apps control access to their homes.

There are, however, some participants seeking to break out from the crowd by taking a different approach.

Crownstone is promoting a system in which a home's lights and plug sockets automatically react to a resident's presence based on them having a Bluetooth-broadcasting wearable or smartphone on them, rather than waiting to be given a command.

Miliboo will be showing off a smart sofa that not only wirelessly charges your handset or tablet but also keeps track of how long you've been sat in front of the TV while monitoring your posture.

Smarter homes will also be pitched as being safer homes.

Several companies will show off fall-detectors to warn if elderly residents have taken a tumble, and a safe that sends a smartphone alert if it detects it has been tampered with will also debut.

But perhaps most the extreme system we've spotted so far comes from Cherry Labs.

The Silicon Valley-based firm uses cameras and audio sensors to keep track of which family members and pets are in which room at what time.

It then collates a twice-daily log of their activities for the person in charge.

On the flip side, if the idea of being put under surveillance makes you feel queasy, Cone of Silence promises to prevent your smart speakers being able to eavesdrop on you - accidentally or otherwise.

It works by generating a white noise signal specifically designed to overload the speakers' microphone arrays.

Televisions

LG has already shown off an outsized 8K TV - with 16 times as many pixels as a 1080p "full HD" screen - ahead of CES.

But with a distinct lack of content for the super hi-vision format available, expect the focus to remain on 4K for the time being.

Manufacturers may instead try to convince enthusiasts to upgrade by boosting the maximum brightness levels of their screens, which has the benefit of delivering superior high dynamic range (HDR) images. As a result, glints of sunlight off water can be more startling, and shadows can reveal more detail.

Several brands will likely add support for Samsung's HDR10+ standard, which is designed to prevent details in the brightest parts of the image being blown out on on some screens - a problem the rival Dolby Vision format already tackles.

And some of the higher-end sets may also introduce support for the new HDMI 2.1 specification, which can handle higher data rates and potentially allow screens to be sent 4K video at up to 120 frames per second.

But the big question is whether Samsung will unveil a "micro-LED" TV.

The technology uses tiny components that emit their own light rather than relying on a backlight.

This allows micro-LEDs to deliver the kind of deep blacks currently restricted to the OLED displays that LG specialises in.

But micro-LEDs should also be capable of brighter output than OLED, making it a superior choice for HDR content.

Those with a good memory may recall that Sony showed off a Crystal LED TV in 2012 based on the same technology.

That never went into mass production because it proved too expensive to make. We'll soon find out whether Samsung has found a way to keep costs down.

Transportation

The rise of electric-powered cars and self-driving technologies have seen automobiles take on a greater role at CES.

This year, Ford's new chief executive, Jim Hackett, is delivering one of the event's high-profile keynote presentations, where company bosses often like to make a splash with headline-grabbing reveals.

Hyundai has promised to debut a hydrogen fuel cell-powered sports utility vehicle (SUV).

And Nissan says it will demonstrate a bonkers-sounding brain-to-vehicle interface. It involves using a brainwave-reading headset to anticipate when steering wheel turns or accelerator pedal presses are about to be made.

The idea is that cars can use this information to "enhance" their response as well as source more data for autonomous driving research.

In general, however, the big automakers tend to hold their most significant news back until the Detroit Auto Show, which begins the day after CES ends this year.

That gives smaller rivals a chance to grab attention.

This year, Chinese electric car start-up Byton will be premiering a model with a gigantic dashboard touchscreen, which it plans to put into production in 2019.

From the US, Fisker will formally reveal a $129,000 (£95,125) luxury electric sports car with a 400 mile (643km) range and fast-charging capabilities.

France's Navya will reveal details about a plan to deploy "robo-taxis across the world.

Switzerland's concept vehicle designer Rinspeed will show off Snap - a modular design in which a passenger pod detaches from its skateboard-like underside.

Faraday Future returns, despite its cash struggles, to host an invite-only update about its self-proclaimed Tesla-killer, the FF91.

And Electra Meccanica may have the oddest electric car on show - a three-wheeler that looks like a Reliant Robin in reverse.

It would be wrong, however, to think the only transport options on show will be cars.

Yamaha intends to demo a self-driving motorcycle prototype racing at speeds above 120mph.

And Surefly has been given permission to fly a sci-fi inspired passenger octacopter.

The plan is for the drone to eventually autonomously carry two passengers to their destination.

But a pilot will be involved for the CES test.

Drones

There will be plenty of smaller drones too at CES.

Market-leader DJI is attending, but has not unveiled major new product lines at the show in past years.

It will, however, be aware that several rivals have pre-announced what look like copycats of its popular fold-up Mavic Pro model, and many of its customers are hoping it will fire back with a second-generation version.

What's fascinating about several of the other new drones is how distinctive they are.

"The reason that we're seeing drone-makers specialising in different areas is that the technology has become extremely commoditised, so to succeed you need a specific use case of your own," commented tech consultant Ben Wood from CCS Insight.

One example is Nuaviation's Hyperlift 200E, which is designed to carry objects weighing up to 200lb (91kg) at high speeds, and is being pitched as a delivery tool for the construction industry.

Another is the Sirius CX-180, which features two powerful LED lamps, and is designed for use in night-time search-and-rescue missions.

There's also a model with a video screen designed to be used by desk-bound security guards, and a drone that's been engineered to fly around warehouses scanning the shelves to check the inventory.

But perhaps the award for most novel use of a quadcopter should go to SwellPro's Splash Drone 3.

It is designed to help fishing expeditions catch tuna, sharks and other large sea life by dropping bait from above.

Smartphones and PCs

Recent revelations about decades-old flaws with processor chips threaten to cast a shadow over new computer launches at CES.

Even so, Qualcomm will likely be trumpeting the battery-life benefits of powering Windows 10 PCs with its smartphone chips, which became possible after Microsoft added support for ARM's architecture.

Meanwhile, Intel will be promoting the virtue of new processor modules that integrate AMD's graphics hardware, which it hopes will prove attractive to gamers.

When it comes to handsets, there are rumours that Samsung might unveil a Galaxy X model with a foldable display.

But if that fails to make an appearance, there will still be a new phone from China's Vivo that carries out fingerprint scans by getting its users to tap the display.

This makes it possible to offer an "all-screen" device without forcing fingerprint readings to be done on the rear.

And for those who prefer their tech to be more retro, London's Planet Computers will be showing off Gemini.

The clamshell handheld resembles the long-retired Psion computer but gives it an Android twist, as you can see below.

Virtual and augmented reality

The Oculus Rift and HTC Vive virtual reality headsets have now been on sale for nearly two years, and sales haven't been stellar.

Sony's PlayStation VR has fared a bit better, but what many are hoping will turbo-charge demand is the emergence of standalone headsets that don't need to be linked to a PC or powered by a phone.

The move could make them much easier to deploy in schools and marketing campaigns, for example, and help them break out of a gaming niche.

At CES, Austrian start-up Exchimp will gatecrash the market with its solution.

Lenovo is also rumoured to be ready to show off its Mirage Solo model. And HTC could announce that it's ready an international launch for its all-in-one Vive Focus, which is already on sale in China.

Much of the other VR hardware news out of the show is likely to be about prototype controls for the technology, with various attempts to put sensors and robotic gloves on people's hands or to recognise their gestures via cameras and sound wave sensors.

Ultimately, many believe augmented reality - in which graphics are mixed together with real-world views - has more potential.

But while there are several firms showing off AR apps and components designed for use in reality-mixing smart glasses, it seems unlikely a market-ready high-quality headset will emerge for at least another year or two.

However, for those just wanting a taste of the tech there are a few solutions on show, including the cardboard-based fold-up Aryzon into which you slot a phone.

Other oddities

There should be more robots than ever before at this year's event. LG and Honda are among the bigger firms showing off new models, which include a luggage-carrying hotel droid and a face-pulling "compassionate" companion.

How practical any of them are is another matter.

Nonetheless, we're promised innovations including a bot that plays Scrabble, two that fold clothes and a third designed to be cuddled at night to help send its user to sleep.

Other tech of questionable merit includes:

Aveine - a smart wine aerator that uses the internet to identify a bottle of wine to judge just the right amount of air to pump into it to improve the taste

Blimp - an online marketplace where you can rent your front door or even your t-shirt out to marketers for micro-advertising campaigns

Short Edition - a terminal that prints out short stories and poems for people to read in waiting rooms as a higher-cost alternative to providing magazines

Volt Case - a smartphone case equipped with an electric stun gun that can only be triggered if you unlock it with a fingerprint

But while it's easy to sneer at the many seemingly obvious misfires, CES also presents an opportunity to be entertained and impressed by all the imagination and effort involved.

So, to end on a positive note here are some other innovations that caught our eye as having potential if they can fulfil their promise:

Shapescale - weight scales that also provide 3D body scans so you can see how exercise changes your shape over time

D Free - a sensor system that claims to be able to anticipate when elderly patients are likely to want to go to the toilet, so that their carers can get them there in time

Biowatch - a vein-reading module for smartwatches straps that identifies the wearer and avoids them having to type in a password code

Emojime - headphones fitted with a brainwave scanner that animate emoji symbols on their outside to illustrate the wearer's mood

We'll be keeping you across all the big announcements and many of the other remarkable reveals at our CES 2018 index and you can also follow the BBC team attending the expo via this Twitter list.

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All aboard the (self-driving) bus - next stop, CES: Las Vegas' annual gigantic tech fest. About 4,000 companies - many of them start-ups - are arriving in town this weekend. Over the coming days, they will reveal new products, secure orders and hopefully provide a taste of the future at the trade fair.

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http://www.pnas.org/content/114/50/13108.fullhttp://www.pnas.org/content/114/50/13108.full

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Proceedings of the National Academy of Sciences

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Significance

We show that socioeconomic attributes such as income, race, education, and voting patterns can be inferred from cars detected in Google Street View images using deep learning. Our model works by discovering associations between cars and people. For example, if the number of sedans in a city is higher than the number of pickup trucks, that city is likely to vote for a Democrat in the next presidential election (88% chance); if not, then the city is likely to vote for a Republican (82% chance).

Abstract

The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.

For thousands of years, rulers and policymakers have surveyed national populations to collect demographic statistics. In the United States, the most detailed such study is the American Community Survey (ACS), which is performed by the US Census Bureau at a cost of $250 million per year ( 1). Each year, ACS reports demographic results for all cities and counties with a population of 65,000 or more ( 2). However, due to the labor-intensive data-gathering process, smaller regions are interrogated less frequently, and data for geographical areas with less than 65,000 inhabitants are typically presented with a lag of y. Although the ACS represents a vast improvement over the earlier, decennial census ( 3), this lag can nonetheless impede effective policymaking. Thus, the development of complementary approaches would be desirable.

In recent years, computational methods have emerged as a promising tool for tackling difficult problems in social science. For instance, Antenucci et al. ( 4) have predicted unemployment rates from Twitter; Michel et al. ( 5) have analyzed culture using large quantities of text from books; and Blumenstock et al. ( 6) used mobile phone metadata to predict poverty rates in Rwanda. These results suggest that socioeconomic studies, too, might be facilitated by computational methods, with the ultimate potential of analyzing demographic trends in great detail, in real time, and at a fraction of the cost.

Recently, Naik et al. ( 7) used publicly available imagery to quantify people's subjective perceptions of a neighborhood's physical appearance. They then showed that changes in these perceptions correlate with changes in socioeconomic variables ( 8). Our work explores a related theme: whether socioeconomic statistics can be inferred from objective characteristics of images from a neighborhood.

Here, we show that it is possible to determine socioeconomic statistics and political preferences in the US population by combining publicly available data with machine-learning methods. Our procedure, designed to build upon and complement the ACS, uses labor-intensive survey data for a handful of cities to train a model that can create nationwide demographic estimates. This approach allows for estimation of demographic variables with high spatial resolution and reduced lag time.

Specifically, we analyze 50 million images taken by Google Street View cars as they drove through 200 cities, neighborhood-by-neighborhood and street-by-street. In Google Street View images, only the exteriors of houses, landscaping, and vehicles on the street can be observed. Of these objects, vehicles are among the most personalized expressions of American culture: Over 90% of American households own a motor vehicle ( 9), and their choice of automobile is influenced by disparate demographic factors including household needs, personal preferences, and economic wherewithal ( 10). (Note that, in principle, other factors such as spacing between houses, number of stories, and extent of shrubbery could also be integrated into such models.) Such street scenes are a natural data type to explore: They already cover much of the United States, and the emergence of self-driving cars will bring about a large increase in the frequency with which different locations are sampled.

We demonstrate that, by deploying a machine vision framework based on deep learning-specifically, Convolutional Neural Networks (CNN)-it is possible to not only recognize vehicles in a complex street scene but also to reliably determine a wide range of vehicle characteristics, including make, model, and year. Whereas many challenging tasks in machine vision (such as photo tagging) are easy for humans, the fine-grained object recognition task we perform here is one that few people could accomplish for even a handful of images. Differences between cars can be imperceptible to an untrained person; for instance, some car models can have subtle changes in tail lights (e.g., 2007 Honda Accord vs. 2008 Honda Accord) or grilles (e.g., 2001 Ford F-150 Supercrew LL vs. 2011 Ford F-150 Supercrew SVT). Nevertheless, our system is able to classify automobiles into one of 2,657 categories, taking s per vehicle image to do so. While it classified the automobiles in million images in wk, a human expert, assuming s per image, would take more than y to perform the same task. Using the classified motor vehicles in each neighborhood, we infer a wide range of demographic statistics, socioeconomic attributes, and political preferences of its residents.

In the first step of our analysis, we collected million Google Street View images from 3,068 zip codes and 39,286 voting precincts spanning US cities ( Fig. 1). Using these images and annotated photos of cars, our object recognition algorithm [a "Deformable Part Model" (DPM) ( 11)] learned to automatically localize motor vehicles on the street ( 12) (see Materials and Methods). This model took advantage of a gold-standard dataset we generated by asking humans (both laypeople, recruited using Amazon Mechanical Turk, and car experts recruited through Craigslist) to identify cars in Google Street View scenes.

We successfully detected million distinct vehicles, comprising of all of the vehicles in the cities we studied and of all vehicles in the United States. After localizing each vehicle, we deployed CNN ( 13, 14), the most successful deep learning algorithm to date for object classification, to determine the make, model, body type, and year of each vehicle ( Fig. 1). Using our human-annotated gold standard images, we trained the CNN to distinguish between different types of cars. Specifically, we were able to classify each vehicle into one of 2,657 fine-grained categories, which form a nearly exhaustive list of all visually distinct automobiles sold in the United States since 1990 ( Fig. 1). For instance, our models accurately identified cars (identifying of such vehicles in the test data), vans (), minivans (), SUVs (), and pickup trucks (). See SI Appendix, Fig. S1.

Using the resulting motor vehicle data, we estimate demographic statistics and voter preferences as follows. For each geographical region we examined (city, zip code, or precinct), we count the number of vehicles of each make and model that were identified in images from that region. We also include additional features such as aggregate counts for various vehicle types (trucks, vans, SUVs, etc.), the average price and fuel efficiency, and the overall density of vehicles in the region (see Materials and Methods).

We then partitioned our dataset, by county, into two subsets ( Fig. 2). The first is a "training set," comprising all regions that lie mostly in a county whose name starts with "A," "B," or "C" (such as Ada County, Baldwin County, Cabarrus County, etc.). This training set encompasses of the cities, of the zip codes, and of the precincts in our data. The second is a "test set," comprising all regions in counties starting with the letters "D" through "Z" (such as Dakota County, Maricopa County, Yolo County). We used the test set to evaluate the model that resulted from the training process.

Using ACS and presidential election voting data for regions in our training set, we train a logistic regression model to estimate race and education levels and a ridge regression model to estimate income and voter preferences on the basis of the collection of vehicles seen in a region. This simple linear model is sufficient to identify positive and negative associations between the presence of specific vehicles (such as Hondas) and particular demographics (i.e., the percentage of Asians) or voter preferences (i.e., Democrat).

Our model detects strong associations between vehicle distribution and disparate socioeconomic factors. For instance, several studies have shown that people of Asian descent are more likely to drive Asian cars ( 15), a result we observe here as well: The two brands that most strongly indicate an Asian neighborhood are Hondas and Toyotas. Cars manufactured by Chrysler, Buick, and Oldsmobile are positively associated with African American neighborhoods, which is again consistent with existing research ( 16). And vehicles like pickup trucks, Volkswagens, and Aston Martins are indicative of mostly Caucasian neighborhoods. See SI Appendix, Fig. S2.

In some cases, the resulting associations can be easily applied in practice. For example, the vehicular feature that was most strongly associated with Democratic precincts was sedans, whereas Republican precincts were most strongly associated with extended-cab pickup trucks (a truck with rear-seat access). We found that by driving through a city while counting sedans and pickup trucks, it is possible to reliably determine whether the city voted Democratic or Republican: If there are more sedans, it probably voted Democrat ( chance), and if there are more pickup trucks, it probably voted Republican ( chance). See .

As a result, it is possible to apply the associations extracted from our training set to vehicle data from our test set regions to generate estimates of demographic statistics and voter preferences, achieving high spatial resolution in over cities. To be clear, no ACS or voting data for any region in the test set were used to create the estimates for the test set.

To confirm the accuracy of our demographic estimates, we began by comparing them with actual ACS data, city-by-city, across all test set cities. We found a strong correlation between our results and ACS values for every demographic statistic we examined. (The values for the correlations were as follows: median household income, ; percentage of Asians, ; percentage of Blacks, ; percentage of Whites, ; percentage of people with a graduate degree, ; percentage of people with a bachelor's degree, ; percentage of people with some college degree, ; percentage of people with a high school degree, ; percentage of people with less than a high school degree, ). See SI Appendix, Figs. S3-S5. Taken together, these results show our ability to estimate demographic parameters, as assessed by the ACS, using the automated identification of vehicles in Google Street View data.

Although our city-level estimates serve as a proof-of-principle, zip code-level ACS data provide a much more fine-grained portrait of constituencies. To investigate the accuracy of our methods at zip code resolution, we compared our zip code-by-zip code estimates to those generated by the ACS, confirming a close correspondence between our findings and ACS values. For instance, when we looked closely at the data for Seattle, we found that our estimates of the percentage of people in each zip code who were Caucasian closely matched the values obtained by the ACS (, ). The results for Asians (, ) and African Americans (, ) were similar. Overall, our estimates accurately determined that Seattle, Washington is Caucasian, with African Americans mostly residing in a few Southern zip codes (). As another example, we estimated educational background in Milwaukee, Wisconsin zip codes, accurately determining the fraction of the population with less than a high school degree (, ), with a bachelor's degree (, ), and with postgraduate education (, ). We also accurately determined the overall concentration of highly educated inhabitants near the city's northeast border (). Similarly, our income estimates closely match those of the ACS in Tampa, Florida (, ). The lowest income zip code, at the southern tip, is readily apparent.

While the ACS does not collect voter preference data, our automated machine-learning procedure can infer such preferences using associations between vehicles and the voters that surround them. To confirm the accuracy of our voter preference estimates, we began by comparing them with the voting results of the 2008 presidential election, city-by-city, across all 165 test set cities. We found a very strong correlation between our estimates and actual voter preferences (, ). See SI Appendix, Fig. S5. These results confirm the ability of our approach to accurately estimate voter behavior.

While city-level data provide a general picture, precinct-level voter preferences identify patterns within a particular city. By comparing our precinct-by-precinct estimates to the 2008 presidential election results, we found that our estimates continued to closely match the ground truth data. For instance, in Milwaukee, Wisconsin, a very Democratic city with 311 precincts, we correctly classify precincts [ accuracy ()]. Most notably, we accurately determine that there are a few Republican precincts in the South, West, and Northeastern borders of the city. Similarly, in Gilbert, Arizona, a Republican city, we correctly classify out of precincts ( accuracy), identifying one out of the two small Democratic precincts in the city (). And in Birmingham, Alabama, a city that is Republican, we correctly classify out of the precincts ( accuracy). Overall, there was a strong correlation between our estimates and actual electoral outcomes at the single-precinct level (, ).

These results illustrate the ability of our machine-learning algorithm to accurately estimate both demographic statistics and voter preferences using a large database of Google Street View images. They also suggest that our demographic estimates are accurate at higher spatial resolutions than those available for yearly ACS data. Using our approach, zip code- or precinct-level survey data collected for a few cities can be used to automatically provide up-to-date demographic information for many American cities.

Thus, we find that the application of fully automated computer vision methods to publicly available street scenes can inexpensively determine social, economic, and political patterns in neighborhoods across America. By collecting surveys for a few cities-the type of data routinely obtained via ACS-and inferring data for other regions using our model, we can quickly determine demographic patterns.

As self-driving cars with onboard cameras become increasingly widespread, the type of data we use-footage of neighborhoods from vehicle-mounted cameras-are likely to become increasingly ubiquitous. For instance, Tesla vehicles currently take as many images as were studied here every single day. It is also important to note that similar data can be obtained, albeit at a slower pace, using low-tech methods: for instance, by walking around a target neighborhood with a camera and a notepad. Thus, street scenes stand in contrast to the massive textual corpora presently used in many computational social science studies, which can be constrained by privacy and copyright concerns that prevent individual researchers from obtaining the raw data underlying published analyses.

The automated method we present could be substantially improved by expanding our object recognition beyond vehicles ( 17, 18) and incorporating global image features ( 7, 19- 21). For instance, our experiments show that global image features extracted from CNN can also be used to infer demographics. But this approach requires more data-at least 50% of our dataset-rather than the 12% to 15% we use using our current method (seeSI Appendix). The model could also be improved by integrating other types of data, such as satellite images ( 22), social networks ( 4), and economic data pertaining to local consumer behavior in particular geographic regions. Nevertheless, there are many characteristics that our methodology-which relies on publicly available data-may not be able to infer (seeSI Appendix). For instance, the age of children in a neighborhood can be estimated with moderate accuracy (), while the percentage of farmers in a neighborhood was not successfully inferred using our method ().

Although automated methods could be powerful resources for both researchers and policymakers, their progress will raise important ethical concerns; it is clear that public data should not be used to compromise reasonable privacy expectations of individual citizens, and this will be a central concern moving forward. In the future, such automated methods could lead to estimates that are accurately updated in real time, dramatically improving upon the time resolution of a manual survey.

Materials and Methods

Here, we describe our methodology for data collection, car detection, car classification, and demographic inference. Some of these methods were partially developed in an earlier paper ( 12), which served as a proof of concept focusing on a limited set of predictions (e.g., per capita carbon emission, Massachusetts Department of Vehicles registration data, income segregation). Our work builds on these methods to show that income, race, education levels, and voting patterns can be predicted from cars in Google Street View images. In the sections below, we discuss our dataset and methodology in more detail.

Acknowledgments

We thank Neal Jean, Stefano Ermon, and Marshall Burke for helpful suggestions and edits; everyone who worked on annotating our car dataset for their dedication; and our friends and family and the entire Stanford Vision lab, especially Brendan Marten, Serena Yeung, and Selome Tewoderos for their support, input, and encouragement. This research is partially supported by NSF Grant IIS-1115493, the Stanford DARE fellowship (to T.G.), and NVIDIA (through donated GPUs).

Footnotes

Author contributions: T.G., J.K., J.D., E.L.A., and L.F.-F. designed research; T.G., J.K., Y.W., D.C., J.D., E.L.A., and L.F.-F. performed research; T.G. and J.K. contributed new reagents/analytic tools; T.G., J.K., Y.W., D.C., J.D., E.L.A., and L.F.-F. analyzed data; and T.G., J.K., E.L.A., and L.F.-F. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1700035114/-/DCSupplemental.

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Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States

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We show that socioeconomic attributes such as income, race, education, and voting patterns can be inferred from cars detected in Google Street View images using deep learning. Our model works by discovering associations between cars and people.

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http://www.businesstimes.com.sg/technology/google-street-view-can-predict-how-you-will-vote

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New York

WHAT vehicle is most strongly associated with Republican voting districts? Extended-cab pickup trucks. For Democratic districts? Sedans.

Those conclusions may not be particularly surprising. After all, market researchers and political analysts have studied such things for

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Google Street View can predict how you will vote

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New York WHAT vehicle is most strongly associated with Republican voting districts? Extended-cab pickup trucks. For Democratic districts? Sedans. Those conclusions may not be particularly surprising. After all, market researchers and political analysts have studied such things for

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https://spectrum.ieee.org/cars-that-think/transportation/human-factors/deep-learning-and-google-street-view-can-predict-neighborhood-politics-from-parked-carshttps://spectrum.ieee.org/cars-that-think/transportation/human-factors/deep-learning-and-google-street-view-can-predict-neighborhood-politics-from-parked-cars

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IEEE Spectrum: Technology, Engineering, and Science News

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Photo: James Leynse

It's likely that your car says something about you. The make and model, whether it's foreign or domestic, and how expensive it is can provide information about who owns it. This doesn't work for everyone, of course, but over a large enough population, the statistics can be fairly reliable indicators. The United States spends a quarter billion dollars collecting socioeconomic information by hand through community surveys every year, but if there were a big enough database of what types of cars can be found in which neighborhoods, that data collection could be done more affordably, more frequently, and cover much larger areas. And there is a big database of neighborhood street pictures, in the form of Google Street View imagery.

Researchers from Stanford University have applied deep learning-based computer vision techniques to 50 million images across 200 regions to identify 22 million cars, which is roughly 8 percent of all automobiles in the United States. Based on the types of cars and their locations, the researchers estimated the income, race, education, and voting patterns of the people living in those areas. The results they derived from pictures are impressively accurate.

In principle, using a convolutional neural network (CNN) to identify cars in a street view image seems like a straightforward problem. However, in order to accurately estimate demographic statistics, it was necessary to know the make, model, year, and trim level for each vehicle. Many vehicles don't change a whole heck of a lot from year to year, so to train the CNN, the researchers relied on both Mechanial Turk random humans, as well as car experts that they recruited on Craigslist. Ultimately, the CNN was trained well enough to classify vehicles in street view images into one of 2,657 categories, accounting for nearly every single visually distinct car, truck, and van sold in the United States since 1990. The CNN managed to chew through all 50 million images in two weeks with an accuracy of around 90 percent-a task which would have taken a trained human over 15 years to complete.

Once all the automobile data were collected, the researchers took demographic survey results and 2008 presidential election results for some sample areas and trained a relatively simple regression model to identify positive and negative associations between vehicles, demographics, and voting preferences. What they came up with will either surprise you very much, or not at all:

Our model detects strong associations between vehicle distribution and disparate socioeconomic factors. For instance, several studies have shown that people of Asian descent are more likely to drive Asian cars, a result we observe here as well: The two brands that most strongly indicate an Asian neighborhood are Hondas and Toyotas. Cars manufactured by Chrysler, Buick, and Oldsmobile are positively associated with African American neighborhoods, which is again consistent with existing research. And vehicles like pickup trucks, Volkswagens, and Aston Martins are indicative of mostly Caucasian neighborhoods.
In some cases, the resulting associations can be easily applied in practice. For example, the vehicular feature that was most strongly associated with Democratic precincts was sedans, whereas Republican precincts were most strongly associated with extended-cab pickup trucks (a truck with rear-seat access). We found that by driving through a city while counting sedans and pickup trucks, it is possible to reliably determine whether the city voted Democratic or Republican: If there are more sedans, it probably voted Democrat (88% chance), and if there are more pickup trucks, it probably voted Republican (82% chance).

This approach also works for all kinds of other characteristics, including income and education level. We should stress that these are general trends, of course, and if you drive one of the vehicles mentioned above but are not in the correlated demographic group, you don't need to run out and buy a new car (although the statistics do kind of suggest that somehow it might make you feel more comfortable for some reason).

Anyway, the researchers say that this method provides a way to "inexpensively determine social, economic, and political patterns in neighborhoods across America." And since it works anywhere that Google's Street View cars have been, it works pretty much everywhere, and we can expect that the data will continue to increase in quality, quantity, and temporal frequency as cars collect more and more data about the world around them. The general methodology here isn't just restricted to automobile identification, either. Similar correlations could be drawn from things like spacing between houses, number of stories of houses, and "extent of shrubbery," whatever that means.

The paper, "Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States," is available in full from PNAS.

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Deep Learning and Google Street View Can Predict Neighborhood Politics from Parked Cars

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It's likely that your car says something about you. The make and model, whether it's foreign or domestic, and how expensive it is can provide information about who owns it. This doesn't work for everyone, of course, but over a large enough population, the statistics can be fairly reliable indicators.

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https://www.wired.com/2017/03/google-street-views-window-americans-vote-look-cars/https://www.wired.com/2017/03/google-street-views-window-americans-vote-look-cars/

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Led by Fei-Fei Li, the director of the Stanford University artificial intelligence lab and a newly minted Google employee, a team of academics recently explored a new way of tracking socioeconomic trends across the US. Rather than knocking on doors and asking questions, they pulled more than 50 million photos from Google Street View and fed them into neural networks. The results were promising. Simply by identifying the make, model, and year of automobiles appearing in the photos, the researchers said, their tech could accurately estimate the income, race, education, and voting patterns of citizens in particular precincts.

If the number of sedans on a short stretch of road exceeded the number pickup trucks, for instance, they found that a city was 88 percent likely to vote for a Democrat during the next presidential election. If pickups exceeded sedans, a city was 82 percent likely vote Republican. "Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time," the researchers write in a recently-released paper detailing this study.

Machines will paint the more accurate picture of how humans think, live, and spend.

Fei-Fei and her colleagues declined to discuss their project because the paper is still under peer review. But their work reflects a much larger effort to gain more insight into broad societal and economic trends through new sources of data, crowdsourcing, and machine learning. In the years to come, machines-not statisticians-will paint the more accurate picture of how humans think, live, and spend.

At a San Francisco startup called Premise, machines parse data collected by an army of people spread across the world, building real-time consumer price indexes. A Palo Alto startup, Orbital Insight, uses artificial intelligence to analyze photos taken by satellites, identifying economic trends from what it finds. And various other researchers have predicted unemployment rates and poverty using everything from Twitter to cellphone metadata.

Fei-Fei and her collaborators see their methods as a replacement for the American Community Survey, a $250 million-a-year study conducted by the US Census Bureau that identifies a vast array of American demographic trends. Online data and machine learning, the researchers say, will reduce the cost of door-to-door demographic studies like these while providing greater accuracy. Door-to-door surveys, after all, don't operate in real time. They're out of date before they're finished.

The methods outlined in Fei-Fei's study still require some on-the-ground data gathering to establish a baseline from which AI-powered techniques can extrapolate. But most of the process is automated. Well-trained neural networks can recognize the make, model, and year of cars in photos with much greater efficiency than humans. As described in the paper, the system needs only one-fifth of a second to sort a vehicle into any of 2,657 categories.

But if Street View photos offer one kind of insight, the view from space offers another path to automated forecasting. Orbital Insight now tracks 250,000 parking lots outside 96 retail chains across the country and uses the number of cars in lots as an indicator of company health. This quarter, for instance, the number of cars in JCPenney lots fell 10 percent. Not surprisingly, perhaps, the retailer just announced the closure of about 130 stores amid declining sales. Premise, meanwhile, pays a network of people across the developing world to collect economic data on the ground-the price of canned coffee in a particular town, for instance, or the freshness of the lettuce on sale in another. Using machine learning techniques similar to those used to analyze Street View and satellite images, the company can then looks for price patterns.

Apply these methods across multiple retailers and multiple industries, and you have what starts to look like an unprecedented collection of economic indicators. Machines can detect patterns that humans can't, or at least with much greater speed and accuracy. As they get smarter, the promise is that these automated forecasts will provide a foundation not just for better economic planning but a better democracy. In a political climate beset by deniers of facts, the hope remains that better information will yield better decisions by the people with the power to make them.

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Google Street View's Window into How Americans Vote (Look at the Cars)

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Led by Fei-Fei Li, the director of the Stanford University artificial intelligence lab and a newly minted Google employee, a team of academics recently explored a new way of tracking socioeconomic trends across the US. Rather than knocking on doors and asking questions, they pulled more than 50 million photos from Google Street View and fed them into neural networks.

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https://www.epson.com.sg/moverio-augmented-reality#ga3ItAfGO9x5Ciay.97

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Introduction Experience MOVERIO Smart Glasses Smart Headset Developers Case Studies Contact Us

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https://www.nytimes.com/2017/12/27/business/the-robots-are-coming-and-sweden-is-fine.html?hp&action=click&pgtype=Homepage&clickSource=story-heading&module=photo-spot-region&region=top-news&WT.nav=top-news

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Eighty percent of Swedes express positive views about robots and artificial intelligence, according to a survey this year by the European Commission. By contrast, a survey by the Pew Research Center found that 72 percent of Americans were "worried" about a future in which robots and computers substitute for humans.

In the United States, where most people depend on employers for health insurance, losing a job can trigger a descent to catastrophic depths. It makes workers reluctant to leave jobs to forge potentially more lucrative careers. It makes unions inclined to protect jobs above all else.

Yet in Sweden and the rest of Scandinavia, governments provide health care along with free education. They pay generous unemployment benefits, while employers finance extensive job training programs. Unions generally embrace automation as a competitive advantage that makes jobs more secure.

Making the United States more like Scandinavia would entail costs that collide with the tax-cutting fervor that has dominated American politics in recent decades.

Sweden, Denmark and Finland all spend more than 27 percent of their annual economic output on government services to help jobless people and other vulnerable groups, according to data from the Organization for Economic Cooperation and Development. The United States devotes less than 20 percent of its economy to such programs.

Public social spending as a share of G.D.P.

For Swedish businesses, these outlays yield a key dividend: Employees have proved receptive to absorbing new technology.

This is especially crucial in mining, a major industry in Sweden. Wages are high, with pay and working conditions set through national contracts negotiated by unions and employers' associations. Boliden's mines have some of the world's lowest-grade ore, meaning it contains minute quantities of valuable minerals. The prices are set by global markets.

"We have every reason not to be competitive," says Boliden's chief executive, Lennart Evrell.

The only way for the company to ensure profit is to continually increase efficiency. This is why Mr. Persson and his co-workers in the control room will soon be operating as many as four loaders at once via joysticks.

The company is pressing ahead with plans to deploy self-driving trucks, testing a system with AB Volvo, the Swedish automotive giant, at a mine in the town of Kristineberg. There, Boliden has expanded annual production to close to 600,000 tons from about 350,000 tons three decades ago - while the work force has remained about 200.

"If we don't move forward with the technology and making money, well, then we are out of business," says Magnus Westerlund, 35, vice chairman of a local union chapter representing laborers at two Boliden mines. "You don't need a degree in math to do the calculation."

At the mine below the frigid pine forests in Garpenberg, 110 miles northwest of Stockholm, Mr. Persson and his co-workers earn about 500,000 krona per year (nearly $60,000). They get five weeks of vacation. Under Swedish law, when a child arrives, the parents have 480 days of family leave to apportion between them. No robot is going to change any of that, Mr. Persson says.

"It's a Swedish kind of thinking," says Erik Lundstrom, a 41-year-old father of two who works alongside Mr. Persson. "If you do something for the company, the company gives something back."

Daunting Job Projections

That proposition now confronts a formidable test. No one knows how many jobs are threatened by robots and other forms of automation, but projections suggest a potential shock.

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The Robots Are Coming, and Sweden Is Fine

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Eighty percent of Swedes express positive views about robots and artificial intelligence, according to a survey this year by the European Commission. By contrast, a survey by the Pew Research Center found that 72 percent of Americans were "worried" about a future in which robots and computers substitute for humans.

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http://www.straitstimes.com/business/banking/dbs-to-invest-20m-over-five-years-to-transform-its-10000-employees-into-digital

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SINGAPORE - DBS Bank announced on Monday (Aug 21) it is investing S$20 million over the next five years in a broad-based programme to equip its 10,000 Singapore-based employees with skills in digital banking and emerging technologies.

Singapore's biggest bank said the move supports Prime Minister Lee Hsien Loong's call at Sunday's National Day Rally to do more to make Singapore a Smart Nation.

Specifically, it supports the Monetary Authority of Singapore's vision to create a Smart Financial Centre where technology is used pervasively in the financial industry to increase efficiency, create opportunities, better manage risks and improve lives, said the bank.

The programme is designed to encourage large-scale participation and digital adoption by all bank employees, and comprises several aspects:

Artificial intelligence-powered e-learning, which can be accessed 24/7

Experiential learning, such as intrapreneur programmes and hackathons

Grants and scholarships

Innovative learning spaces

In the area of artificial intelligence-powered e-learning, for example, DBS said it has launched a new cloud-based learning management system called DBS Horizon. It uses artificial intelligence to make personalised course recommendations and help employees collaborate, create communities of interest and engage in mobile education anytime, anywhere across the bank.

DBS employees are given the opportunity to go on paid sabbaticals to work on prototypes and start their own businesses. The bank also runs accelerator programmes, which provide mentorship and funding to help intrapreneurs grow their concepts into prototypes. In addition, more than 2,000 DBS staff have participated in human-centred design workshops and hackathons, and conducted more than 1,000 experiments since 2015.

Said Lee Yan Hong, DBS Bank's group head of human resources: "As the financial sector evolves, the profile of jobs will change and we are committed to helping our people adapt and embrace digital transformation through innovative and immersive continuous learning programmes.

"An agile and innovative workforce will also make Singapore's Smart Nation ambition a reality and, along with this, we are making significant investments to groom a generation of digital bankers, strengthening Singapore's talent pool."

The bank also offers scholarships to employees to further their skills in emerging technologies, such as data and analytics, design thinking, and automation. They are then encouraged to share their learning with their peers, creating a culture of peer-to-peer learning. To date, 130 scholars have shared their knowledge with more than 1,000 of their peers.

DBS has also invested in innovative learning spaces for its employees, including the DBS Academy and DBS Asia X, its latest innovation hub. Spanning 40,000 sq ft, the DBS Academy conducts more than 10,000 training sessions each year and provides employees access to a wide selection of courses, such as scenario-based leadership development programmes and digital master classes, said the bank.

DBS Asia X is a purpose-built innovation facility for DBS employees to come together, design and develop iconic customer journeys and work in close collaboration with start-ups and the broader FinTech community.

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DBS to invest $20m over five years to transform 10,000 employees into 'digital workforce'

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SINGAPORE - DBS Bank announced on Monday (Aug 21) it is investing S$20 million over the next five years in a broad-based programme to equip its 10,000 Singapore-based employees with skills in digital banking and emerging technologies.

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http://www.businesstimes.com.sg/banking-finance/stanchart-spore-to-retrain-over-3000-staff-in-three-years

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Standard Chartered Bank in Singapore has launched a plan to re-skill and re-train more than 3,000 employees in three years to prepare them for future roles amid a changing financial industry.

The plan builds on an existing programme, SkillsFuture@sc, which StanChart launched last year to identify and source for training that would equip its people for the future.

Under this programme, employees who work for Standard Chartered's franchise in Singapore can sign up for any of over 100 bank-sponsored courses, on top of taking up a SkillsFuture course using the government credit of S$500 awarded to Singaporeans aged 25 and above.

In 2017, more than 800 employees have enrolled for the courses under SkillsFuture@sc.

For the next three years, StanChart aims to do more by introducing a structured re-skilling plan, targeting to re-train more than 3,000 employees by 2020.

The plan is to identify around 1,000 employees each year who fall under two main categories - employees whose roles may be at risk in the future and employees who would benefit from an expansion of skillset and growth. They will be encouraged to enroll for the courses, and upon completion, be able to apply for a different role within the bank.

Charlotte Thng, StanChart Singapore's head of human resources, said: "The best thing we can do for our employees is to help them be prepared for their future roles."

As machines and technology change job scopes within the financial industry, the bank is focusing on training that enhances people's technological skills in data analytics, block chain, data mining and artificial intelligence, she added.

"We need to better understand technology and know how to use it to help us be better and faster in our work."

The second area of focus is to help employees be even more 'human' in their jobs and provide the extra human touch which machines can't replace, she said.

The training will be in areas such as customer interaction, human centred design and project management.

"The key is to enhance our strengths and be better at what makes us different from machines."

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StanChart S'pore to retrain over 3,000 staff in three years

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THE BUSINESS TIMES Banking & Finance - Standard Chartered Bank in Singapore has launched a plan to re-skill and re-train more than 3,000 employees in three years to prepare them for future roles amid a changing financial industry.. Read more at The Business Times.

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http://www.channelnewsasia.com/news/singapore/hackers-could-guess-your-smartphone-pin-with-sensor-data-ntu-9526162

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SINGAPORE: The data collected from the sensors on your smartphone could be used to hack into your device, researchers at the Nanyang Technological University announced on Tuesday (Dec 26). This, they said, highlights a "significant flaw" in smartphone security.

According to their research published in Cryptology ePrint Archive, hackers can guess the security PIN of a smartphone and unlock it just by using data from the device's physical sensors such as accelerometer, gyroscope and ambient light sensor, an NTU news release said.

The sensors are openly available for all apps to access, and require no permissions to be given by the phone user, representing a potential security vulnerability, it added.

"Using a combination of information gathered from six different sensors found in smartphones and state-of-the-art machine learning and deep learning algorithms, the researchers succeeded in unlocking Android smartphones with a 99.5 per cent accuracy within only three tries, when tackling a phone that had one of the 50 most common PIN numbers," the release said.

The previous best phone-cracking success rate was 74 per cent for the 50 most common pin numbers, but NTU's technique can be used to guess all 10,000 possible combinations of four-digit PINs, it added.

CRACKING THE CODE
The researchers also told Channel NewsAsia in a follow-up email that their method can be applied to PINs that go beyond four digits.

The researchers, led by NTU senior research scientist Dr Shivam Bhasin, used sensors in a smartphone to model which number had been pressed by its users, based on how the phone was tilted and how much light is blocked by the thumb of fingers. The sensors were the accelerometer, gyroscope, magnetometer, proximity sensor, barometer, and ambient light sensor.

With data collected from three people, who entered a total of 210 random four-digit pin numbers on the phone, the custom application developed by the researchers recorded the relevant sensor reactions. The app, with its deep learning algorithm, was then able to give different weightings of importance to each of the sensors, depending on how sensitive each was to different numbers being pressed.

That helped to eliminate factors which it judges to be less important and increased the success rate for PIN retrieval.

"So while a malicious application may not be able to correctly guess a PIN immediately after installation, using machine learning, it could collect data from thousands of users over time from each of their phones to learn their PIN entry pattern and then launch an attack later when the success rate is much higher," the researchers said.

They added that the method has only been tested on the Android platform, but "to to the best of their knowledge", the sensor data is available to any app that wants to access it on both Android and iOS.

Professor Gan Chee Lip, director of the Temasek Laboratories at NTU, said: "Along with the potential for leaking passwords, we are concerned that access to phone sensor information could reveal far too much about a user's behaviour. This has significant privacy implications that both individuals and enterprises should pay urgent attention to."

The researchers did not reply directly when asked if Google was notified of the finding. They did note that the research was published online in an open archive since Dec 6 and they have submitted the paper to a cybersecurity conference that will be held next year.

To keep mobile devices secure, Dr Bhasin advised users to have passcodes of more than four digits, coupled with other authentication methods like one-time passwords, two-factor authentications, and fingerprint or facial recognition.

The researchers also said their findings suggest that sensor data should be protected, like how GPS data is currently, in order to further safeguard user privacy.

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Hackers could guess your smartphone PIN with sensor data: NTU researchers

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SINGAPORE: The data collected from the sensors on your smartphone could be used to hack into your device, researchers at the Nanyang Technological University announced on Tuesday (Dec 26). This, they said, highlights a "significant flaw" in smartphone security.

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http://www.channelnewsasia.com/news/technology/how-did-google-s-digital-assistant-pick-up-singlish-9509590

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SINGAPORE: In a fit of pique, with no compromise in sight, a heated debate over fruit came to an unlikely denouement: "OK Google, what's your favourite fruit?"

Unflustered, the digital assistant answers, a little playfully: "I'm crazy about durians. The smell doesn't get to me."

That scenario may or may not have taken place, but it showed the amount of thought put into a conversation about a local favourite, the durian, for the Singaporean Assistant to understand the local context and nuances, explained Mr Deniz Binay, product manager of Google Assistant.

"With the Assistant, we want you to be able to have a really natural conversation with Google to get things done in your world. And to be a truly helpful 'kaki' (friend), your Assistant needs to understand your world, and that includes your language, local culture and customs," Mr Binay told Channel NewsAsia in an email.

"That's why the Assistant is built to recognise local accents, as well as offer unique responses to questions that relate to local culture and customs - from local food, landmarks and even some Singlish terms."

The customised-for-Singapore Google Assistant was launched on Nov 2 by the search giant, and it announced on Dec 15 that the digital assistant will be rolled out to phones running Android 5.0, also known as Lollipop.

Q: How did you teach the Singaporean Assistant its local nuances?
In the email interview, Mr Binay shed more light on how the Google Assistant picked up Singlish, and the development effort behind it.

A: In general, the responses you get from Google Assistant are a combination of algorithmically generated answers, like in Google Search, and responses written by a team of professional writers. With machine learning techniques, like natural language processing and voice recognition, the Google Assistant can learn to understand a new language.

But bringing the Google Assistant to a new language is much more than translation and language understanding.
Q: So there's a team of professional writers scripting Google Assistant's responses? How does this work?
It also means making sure that it knows the things, people and places that matter to people and that it understands local culture. That's where our team of professional writers come in: They work very closely with our teams in Singapore and teach the Assistant things like names of local places, businesses and festivals, so you can say: "Ok Google, how do I get to Little India?", "Where is the nearest NTUC?" or "When is Hari Raya Puasa next year?"

A: We wanted to make sure that you can have a really natural conversation with the Google Assistant, that's why we built in a personality.

The team that works on the Google Assistant's personality has a wide range of people with varying background, including folks with experience working at Pixar, the Onion, Nintendo, etc. We've researched the personality and artificial intelligence space and thought through how Google's personality should manifest in a digital assistant - and we think the Google Assistant reflects the values and spirit of Google itself: Helpful, humble, and a little playful.

An example here might be the question: "What's your favourite fruit?"

In this case, it isn't Google Assistant pretending it's a person or that it eats fruits. But we also aren't ignoring the request from the user to have some fun. So you'll get something like: "I'm crazy about durians. The smell doesn't get to me" - answering the question, but not in a way that doesn't fit the personality.
Q: How many Singaporeans were roped in to help develop the Singaporean Assistant?

Q. How accurate is Singaporean Assistant in voice recognition now?
A: We got help from dozens of Singaporean Googlers, as well as external volunteers from our local guides program, who helped us test the Singaporean Assistant over a period of several months.

A: We don't have any numbers to share for Singapore but as vice president of product management Rishi Chandra said during the Made By Google event in October, we've been investing in speech recognition and natural language understanding for more than a decade and are pretty confident that we have one of the best voice recognition in the world.

In the last year, we've answered more than 100 million questions on the Google Assistant. To understand voices from users of different age groups, genders and accents, we've trained the Google Assistant at a scale that only Google could, with more than 50 million voice samples in hundreds of different ambient environments.

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How did Google's digital assistant pick up Singlish?

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SINGAPORE: In a fit of pique, with no compromise in sight, a heated debate over fruit came to an unlikely denouement: "OK Google, what's your favourite fruit?" Unflustered, the digital assistant answers, a little playfully: "I'm crazy about durians. The smell doesn't get to me."

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http://news.nus.edu.sg/highlights/new-ai-centre-suzhou?utm_source=corpsite&utm_medium=blog&utm_campaign=First+NUS+AI+Centre+in+China

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NUS and the Suzhou Industrial Park Administrative Committee (SIPAC) will establish a new centre in Suzhou to spur research and commercialisation in artificial intelligence (AI).

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http://www.nus.edu.sg/nusbulletin/faculty-of-science/undergraduate-education/degree-requirements/bachelor-of-sciencebachelor-of-science-hons-programme-requirements-b-sc-b-sc-hons/data-science-and-analytics/
http://www.comp.nus.edu.sg/programmes/ug/ba/
http://msba.nus.edu/
http://executive-education.nus.edu/programmes/31-leading-with-big-data-analytics-machine-learning
http://digitalsenior.sg/data-science-analytics-courses-singapore/
http://www.straitstimes.com/singapore/education/nus-launches-three-new-part-time-degrees-in-business-analytics-cybersecurity-and
https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/tdwi-operationalizing-embedding-analytics-for-action-108112.pdf
https://www.indeed.com.sg/Scientist,-Data-Analytics-jobs?vjk=1194d1b9e81b8d35

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Data Science and Analytics | NUS Bulletin

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The four-year direct Honours programme in Data Science and Analytics (DSA) is designed to prepare graduates who are ready to acquire, manage and explore data that will inspire changes around the world. Singapore is a financial hub, with key industries focusing on biomedical sciences, health care, manufacturing, e-commerce and sustainable energy, among others.

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http://www.straitstimes.com/asia/east-asia/spore-firm-in-ai-venture-with-top-beijing-hospital

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A Singapore start-up and a top Chinese hospital in Beijing have set up what they say is the world's first artificial intelligence (AI) research centre for neurology.

The centre aims to develop technologies that, for example, ensure efficient and consistent diagnosis of brain tumours and vascular diseases, and help patients recover faster through the use of robots.

Hanalytics, which focuses on AI applications in healthcare, will work with Beijing Tiantan Hospital, known for its neurosurgery expertise, to develop applications in diagnosis, prevention, prognosis as well as patient rehabilitation using AI.

Beijing Tiantan Hospital vice-president Wang Yongjun and Hanalytics chief executive Raymond Moh inked the cooperation agreement in Beijing yesterday.

"We hope the skills of hospitals and doctors can gradually become more uniform through the help of AI," said Professor Wang, who is also chief physician at the hospital's neurology department.

"In time to come, we won't have the situation where patients get a lousy medical outcome just because they went to a bad hospital, or they can only get a good one if they go to a good hospital. AI can help make up for this," he said.

Prof Wang said the centre will harness AI to develop new technologies covering areas including brain tumours, vascular diseases, gene protein analysis, patient rehabilitation and clinical treatment of cerebrovascular disease. It has already started developing medical imaging AI to assist doctors in their diagnosis of brain tumours.

AI'S ROLE
In time to come, we won't have the situation where patients get a lousy medical outcome just because they went to a bad hospital, or they can only get a good one if they go to a good hospital. AI can help make up for this.
BEIJING TIANTAN HOSPITAL VICE-PRESIDENT WANG YONGJUN

In the next three to four months, the centre hopes to roll out a system where AI software could be installed in magnetic resonance imaging (MRI) scanners to generate reports seconds after patients receive an MRI examination.

"Some 90 per cent of our patients come from outside of Beijing. They normally have to wait one day for the report as our doctors need time to read the images and write the reports. With AI, the doctors will only need to double check the machine-generated report. It can cut the workload by half and have higher consistency," said Prof Wang.

Mr Moh said the Beijing hospital has neurology experts with top quality skills and they treat a wide range of medical conditions that are considered rare even in a large country like China.

Getting the machines to apply deep learning can achieve excellent results in diagnosis accuracy and consistency, he added.

The collaboration will also benefit Singapore's scientists.

"We have a lack of data scientists in Singapore. And even with the small number that we have, we will need a platform for them to apply their skills after we train them. So this collaboration can give our Singapore talent a good platform to receive training overseas, and later transfer the learning back to Singapore," said Mr Moh, one of five founders of the fledgling company which was started this year.

Hanalytics will send 20 scientists to Beijing for one year to work with the medical specialists at the new research centre.

Prof Wang said the collaboration is an exclusive one where Hanalytics will work only with Beijing Tiantan Hospital in developing AI in neurology.

"We can only grow big and strong if we focus on doing just one thing. And we especially like Hanalytics for its international perspective.

"We are not satisfied to be the top hospital in China in the field of neurology and neurosurgery. We want to bring our team onto the global stage,'' he said.

Besides improving medical outcomes, AI can also help lower healthcare costs. Management consultancy McKinsey estimates that AI could help save up to US$300 billion (S$403 billion) a year in healthcare services in the United States, or about 0.7 per cent of the country's gross domestic product.

China has been quick to embrace AI in the healthcare sector, with strong backing from the government.

AI is used in areas such as remote care services, assisting diagnosis and treatment as well as improving health management through wearable devices, said the National Health and Family Planning Commission in a report by the state-run China Daily.

The private sector has also been active in developing AI applications in healthcare, with at least 131 companies, mostly in Beijing, Shanghai and Shenzhen, involved in the field, according to a report by research firm Yiou Intelligence.

All three Chinese tech giants - Baidu, Alibaba and Tencent - have also made notable investments in this area.

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Singapore firm in AI venture with top Beijing hospital

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A Singapore start-up and a top Chinese hospital in Beijing have set up what they say is the world's first artificial intelligence (AI) research centre for neurology. The centre aims to develop technologies that, for example, ensure efficient and consistent diagnosis of brain tumours and vascular diseases, and help patients recover faster through the use of robots.

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https://www.cnet.com/products/magic-leap-one/preview/

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Magic Leap One's creator talks about mixed reality's future

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The first product from the secretive, Google-funded augmented reality startup has finally been revealed. Founder and CEO Rony Abovitz shares some key insights about Magic Leap One.

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http://www.scmp.com/business/china-business/article/2112443/every-company-future-will-need-embrace-digital

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Every company in future will need to embrace digital

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There is no one road to digital transformation. But firms must start down the path if they intend to become the disrupters and not the disrupted

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https://www.project-syndicate.org/commentary/china-digital-age-innovation-by-kai-fu-lee-and-jonathan-woetzel-2017-12

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From retraining those displaced by automation to preventing the rise of tech monopolies, the shift into the digital age is rife with challenges. But the readiness of China's government to embrace the digital age, pursue supportive policies, and avoid excessive regulation has already placed the country at a significant advantage.

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China, the Digital Giant by Kai-fu Lee & Jonathan Woetzel

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From retraining those displaced by automation to preventing the rise of tech monopolies, the shift into the digital age is rife with challenges. But the readiness of China's government to embrace the digital age, pursue supportive policies, and avoid excessive regulation has already placed the country at a significant advantage.

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For several years, rumors suggested that Apple would follow its mixed reality AR/VR headset with a set of lighter weight augmented reality "Apple Glasses" that would launch sometime around 2024.

Rumors have shifted and the Apple Glasses project is on hold with no word on when we might see the product. This guide aggregates everything that we know about the Apple Glasses so far, but be warned that this is a project that Apple may have permanently shelved.

Apple Glasses Design

The Apple Glasses are supposed to be more of an everyday wearable product than the headset, and they will resemble traditional glasses.

The glasses are said to look similar to regular glasses, with lenses that have built-in displays that can be interacted with using gestures.

Apple was rumored to be planning to use Sony's microOLED displays that feature an ultra-fast response rate, ultra-high contrast, a wide color gamut, high luminance, low reflectance, and integrated drivers for a thin and light design.

Leaker Jon Prosser claimed that the glasses will look similar to Ray-Ban Wayfarers or the glasses that Tim Cook wears, while Bloomberg said a prototype Apple developed resembles high-end sunglasses with thick frames that house the battery and chips.

iPhone Reliance

Apple analyst Ming-Chi Kuo suggested the Apple Glasses would be marketed as an iPhone accessory, serving as a connected display while offloading computing, networking, and positioning to the ‌iPhone‌.

Cost

Apple was said to be planning to sell the glasses for somewhere around $499, with prescription lenses available for an additional cost.

Development Delay

According to Bloomberg's Mark Gurman, Apple has postponed development on the Apple Glasses because of technical difficulties. Apple has not been able to develop the hardware required for a lightweight wearable with the power of an ‌iPhone‌ and enough battery life to run it.

Work on the device has been scaled back, but has not been stopped entirely. Apple is said to see the Apple Glasses as a product that could one day replace the ‌iPhone‌.

With work on the Apple Glasses postponed, there is no word on when Apple might be ready to launch the product.

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For several years, rumors suggested that Apple would follow its mixed reality AR/VR headset with a set of lighter weight augmented reality "Apple Glasses" that would launch sometime around 2024. Rumors have shifted and the Apple Glasses project is on hold with no word on when we might see the product.

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http://www.independent.co.uk/life-style/gadgets-and-tech/features/apple-iphone-tim-cook-interview-features-new-augmented-reality-ar-arkit-a7993566.html

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Earlier this year, video emerged of a new iPhone feature, long before it was released. It showed the phone creating a magical portal in the middle of a city street. And now that's arrived.

Apple's new technology, named ARKit, seemed to arrive at the perfect time. It works by imposing bright virtual objects into the real world - at a time when reality has never seemed darker.

The feature is the kind of world-changing technology that's on a par with the introduction of the iPhone 10 years ago, Tim Cook tells The Independent. And Apple is standing at the front of it, he says - as another, perhaps more literal, aspect of its long-standing mission to make the world better and a bit more magic.

Because for all the discussion of AR and VR, reality isn't really being escaped so much as improved. And Apple isn't running away from reality either - during our interview, Cook speaks passionately about his belief that the real world is getting better, as well as the virtual one.

Despite a focus on new technology, he makes clear that Apple is a company that will not stay quiet and will fight for human rights across the world. And he is optimistic that the world is gradually improving - an optimism that is driven by a sense of history, he says.

But today, Mr Cook - voice soft with Alabama politeness, wearing blue jeans - is demonstrating his plan for augmented reality to take over the world. Or, rather, show how it already has.

Augmented reality works by examining, mapping and understanding the real world and then laying virtual objects on top of it. Apple's brand new ARKit works by doing that first bit - it uses the iPhone to get a highly detailed picture of the phone and its surroundings, and serving that up to developers so they can put virtual objects on top of it.

One particularly popular example since ARKit launched, for instance, is IKEA's new app. That allows people to stand in their living room and look at a virtual recreation of the sofa they want to buy - and then they can check how it fits, move around it, look how it goes with the wallpaper, and so on.

Cook claims that shopping will be changed "entirely" by augmented reality, and says that he doesn't think "anything will be untouched". It brings together the two difficult forms of shopping today, both brick and mortar and online, in a way that could let people see the entirety of a dress by walking around it, as if they were in a shop, but from the comfort of their own iPhone.

Similar apps are being made across education, business and navigation, as well as shopping. Cook was joined by developers who showed The Independent their new software, which included hiking apps that overlaid information about the landscape, educational apps that did the same for the solar system, as well as games that could be virtually played on a real tabletop.

And Apple believes it will be huge. Cook says that it will end up not simply being used by some kinds of people but for "everyone"; like the App Store when it launched, it might not be huge right now, but AR will go on to have the same "dramatic" climb to take over the world that did.

Cook - whose responses have the same incredibly thoughtful, intentional quality as his company's products; the sentences are designed with the precision of iPhones - was speaking as part of Apple's launch of ARKit. That is a framework in the iOS software that powers its iPhones, but it has been accompanied by sensors inside the iPhone X and 8, as well as those that went before.

But he was also notably humble about what that actually is: Apple has created not the augmented reality apps themselves, but a vast framework of sensors and software so developers can build their own.

"The way that you get lots of great ideas is for us to do the heavy lifting of the complexity of locational things and software, and put those in the operating system," says Cook. "And then you have all the developers that are able to put their energy into their passion."

Every developer that creates apps for the iPhone can now use those features, and with an ease that has allowed them to pull together stunning virtual worlds in just a few weeks. That means that Apple can "plant a lot of seeds", says Cook, and since there's 15 million or so developers in the world then at least some of those seeds will grow into the stunning, flowering app ecosystem of the future.

And it's all those things that show why exactly Apple is feeling so confident about its lead in augmented reality.

"Our competitors are trying to mimic what we've done," says Greg Joswiack, Apple's vice president for iOS, iPad and iPhone marketing. "But they just don't have that scale we bring to it."

Android is bigger, but fragmented into different parts so that Google can't be sure how long it will take for new features to make phones. And while Apple and Samsung battle for the top spot in the phone market, each of Apple's individual new phones sell far more than their rival's.

What's more, the fact that only really Apple makes both the software and hardware for its phones puts it in a uniquely strong position - in both AR and elsewhere. Google has been doing this with its Pixel line of phones, and has integrated good virtual reality features, but those phones don't sell in large numbers.

That gives Apple an especially strong position because its competitors "don't control the hardware and software", Cook says. "It goes to what Apple is about - the integration of those two things, with the App Store on the server side. I think it's going to be hard for other folks."

The comparison to the App Store is a key one for Cook. He described the slight scepticism it was greeted with at its launch - and the speed with and scale on which those people were proved wrong.

"Think back to 2008, when the App Store went live. There was the initial round of apps and people looked at them and said, 'this is not anything, mobile apps are not going to take off'.

"And then step by step things start to move. And it is sort of a curve, it was just exponential - and now you couldn't imagine your life without apps. Your health is on one app, your financials, your shopping, your news, your entertainment - it's everything.

"AR is like that. It will be that dramatic."

He then goes on to compare it to another technology that was pioneered by Apple, and which went on to transform the entire world.

"People initially didn't think multi-touch was very profound," he says. "But you think about how we all interface with software today, and we do it by touch. The point-and-click and buttons that we all used for so long are fading.

"And you couldn't imagine now interfacing in that kind of way. So I think it's like that - it's that big."

For now, ARKit lives in iOS and so can only be used on the iPhone and iPad. But an obvious question is where Apple goes from here: will there be a time in the future that Apple makes products where AR isn't a feature but is the entire product?

Because AR is "horizontal", he says, it can apply across the range. But that's limited by the fact that understanding the world requires an awful lot of technology to work well, technology that at the moment can only be crammed into advanced devices like the iPhone or iPad.

"The products themselves have to have a lot of processing power, and a fair amount of different sensor technology in order to do this locational stuff," says Cook. "So having it on iPhone changes the game for developers, because instantly they had hundreds of millions of potential customers.

"If it were on a different device then you would never have a commercial opportunity, and without the commercial opportunity you'd never have 15 million people that say, 'I want to design my passion with AR'."

By putting it on iPhone, Apple was able to "instantly overnight become the largest AR platform", Cook says. And all without actually requiring anyone to buy anything, if they already have an iPhone.

But there's no doubt that new, AR-focused products will come, from someone else if not Apple - the most obvious application of this technology is in glasses. The idea of eyewear that includes computers was undermined quite a little by Google Glass, and the "glassholes" that wore them, but once you start using augmented reality it's clear that it would work brilliantly if it were strapped to your face.

"There are rumours and stuff about companies working on those - we obviously don't talk about what we're working on," Cook says. (Patents have shown that Apple is at least looking into such wearables, and reports have suggested that the ARKit introduction on the iPhone is partly a way to help build that interest and ecosystem.)

"But today I can tell you the technology itself doesn't exist to do that in a quality way. The display technology required, as well as putting enough stuff around your face - there's huge challenges with that.

"The field of view, the quality of the display itself, it's not there yet," he says. And as with all of its products, Apple will only ship something if it feels it can do it "in a quality way".

Life would be so much easier if we just treated everybody with dignity and respect. You think about all the problems in the world - half of them would be solved with just that!

"We don't give a rat's about being first, we want to be the best, and give people a great experience," he says. "But now anything you would se on the market any time soon would not be something any of us would be satisfied with. Nor do I think the vast majority of people would be satisfied."

He doesn't deny that future is coming. "Most technology challenges can be solved, but it's a matter of how long," he says - before moving on to talk about the fact that the achievement or possibilities of the phone shouldn't be underestimated.

Whether or not glasses come, Apple will have succeeded in giving hundreds of millions of people a piece of augmented reality hardware - without them even knowing it. If Apple or anyone else does go on to make a piece of augmented reality hardware, it's clear that the world will be far more ready than they would have been if the iGlasses had appeared alongside the iPhone X last month. There's already a huge number of apps, and a vast amount of people who have now seen the possibilities of AR for the first time.

I asked Cook whether he saw Apple's AirPods - the wireless earphones that also allow their wearer to talk to Siri and hear directions - as a kind of augmented reality technology. He didn't, but said that he can "envision audio becoming a key part of the AR experience", referencing a game we had played that was soundtracked by the beautiful and dynamic twinkling of a Japanese rock garden.

The Apple CEO is speaking to The Independent in London about the development of the ARKit products. But he used his trip to Europe to take in other visits - including an emotional trip to a cemetery in Normandy, to mark the lives of soldiers that were lost and destroyed there.

"From a personal point of view, I have a deep connection with men and women that served in the military. I came from a military family. My father fought in the Korean war, my brother served in the air force. Many people in the extended family - there's a deep belief in serving country.

"So that's a huge link for me personally. What happened there in particular was the key turning point, in that awful war."

A tweet he posted after appeared to indicate not only the importance of that moment but the way that it echoes into our current time - amid the rise of the far right and dissatisfaction around the world. "At Normandy, honouring those who made the ultimate sacrifice," he posted, before quoting a message written on a chapel there: "Think not only upon their passing. Remember the glory of their spirit".

That quotation - and its reminder of the continuing importance of the spirit of those soldiers - seems to have taken on new edges in the past few months. Cook says that he hadn't taken the trip to make any "current day statements", but as a reflection of his "love of people that served and gratefulness that they did".

But, he says: "You think about what happened in that war and what people were fighting for, and it goes to the basics of human dignity."

"Today at Apple we still fight for this, and advocate for human rights, and we believe that every generation has a responsibility to enlarge the definition, not move inward.

"And so you can see, we've been very clear and straight, we don't believe in being silent, we think silence is sort of the ultimate consent."

Cook has certainly not been quiet about some of the recent decisions of Donald Trump and the US government. He objected forcefully and publicly to the suggestion that the US could end protections for Dreamers and kick people out of the country, and is known to have called the president and told him not to pull out of the Paris agreement on climate change - while ultimately failing to stop that decision.

But amid those worrying events, does he still believe that the progress of the world is towards more respect for more people?

"I think that history sort of ebbs and flows at time, but the arc always goes in a certain direction. And I think that will happen now as it happened in the 1960s and 1970s, and has in a lot of ways continued to happen.

"Sometimes I think being in the midst of it, it doesn't feel like it. But looking back, particularly for me, I saw the way that African-Americans were treated in the 1960s and into the 1970s - and still today in too many places. But then arguably the laws also not only allowed it but facilitated discrimination.

"So I've seen massive improvement. And my optimism stems from that history - I do think that is the arc across the world. What each of us has to do is do everything we can to hit the accelerator key.

"Life would be so much easier if we just treated everybody with dignity and respect. You think about all the problems in the world - half of them would be solved with just that! Life would be so much better."

As Cook talks about the importance of speaking out, his commitment to improving the real world is clearly undimmed. His and Apple's work isn't limited only to the political, and has also taken in commitments to power its stores and data centres sustainably alongside new concentration on improving the diversity and accessibility of both its products and its business.

But while it might not be so pressing, Cook is a little more able to change the entire world with ARKit. It does that literally, by laying magical and strange virtual objects - or domestic ones - onto the real world.

But it does it in a broader way, too, and one that explains Cook and Apple's focus on the applications of ARKit and other technology in education. Augmented reality apps are already allowing children to fly through virtual stars and mountains - and perhaps encourage them to change the world beneath.

Reuse content

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Apple thinks it's about to change your world with its new iPhone feature, and it could be right

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Earlier this year, video emerged of a new iPhone feature, long before it was released. It showed the phone creating a magical portal in the middle of a city street. And now that's arrived. Apple's new technology, named ARKit, seemed to arrive at the perfect time.

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http://deadline.com/2017/11/apple-tim-cook-augmented-reality-fourth-quarter-2017-earnings-1202200710/

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Add Apple CEO Tim Cook to the list of Silicon Valley technologists who believe in the transformative power of augmented reality, saying it's going to "change everything."

Augmented reality - that technology that overlays virtual images and information onto the real world, like a magical television chyron - long has seemed the stuff of science fiction. But since incorporating AR into Apple's iOS 11 mobile operating system in September, developers have released more than 1,000 apps that harness the technology.

It's the same sort of brisk adoption Apple previously saw in 2008, with the introduction of the App Store.

"Simply put, we believe augmented reality is going to change the way we use technology forever," Cook said during today's fourth-quarter earnings call with analysts. "We're already seeing things that will transform the way you work, play, connect and learn."

Cook offered examples of how AR is being used to elevate prosaic experiences, like the ability to place a virtual version of a couch in the living room before making a furniture purchase, or attending a live sporting event and seeing stats displayed on the field. Its applications for elementary and secondary education, he said, make him yearn to return to the classroom.

"Augmented reality is going to change everything," Cook said.

Apple said today it ended fiscal 2017 with record September quarter revenue of $52.6 billion, a 12% jump over last year. Sales were buoyed by a return to growth in China, thanks to brisk sales of the iPhone, iPad and Mac, as well as to gains in emerging markets. The company posted per-share earnings of $2.07, or $10.71 billion.

Cook said the iPhone 8 and 8 Plus, released in September, "instantly became our two most popular iPhone models." The 10th anniversary edition iPhone X, which boasts such advanced features as face recognition (used to unlock the device) and studio-quality photo lighting effects, went on sale in Australia during the earnings call.

The venerable Mac notebook achieved record annual revenue, thanks to an update that happened in June - just in time for the back-to-school season. Apple sold 5.4 million Macs during the September quarter.

Even the iPad, which has been in decline, saw a return to growth, with 10.3 million tablets sold.

The much-maligned Apple Watch, now in its third edition, saw unit sales jump by 50% (though Apple still refuses to release unit sales figures). The addition of cellular connectivity - which allows runners to hit the road and get calls without lugging their cellphone - has proven "a game-changer" for consumers, Cook said.

Apple's wearable computing products - which include the Apple Watch and AirPod wireless earphones - are up 75% year-over-year.

Apple's services set a quarterly record revenue of $8.5 billion, up 34% from a year ago. That figure was bolstered by a favorable one-time revenue adjustment of $640 million, notes CFO Luca Maestri. But that wasn't the whole story.

The App Store set a record. Apple Music saw a 75% jump in paid subscribers from a year ago (though the company did not release updated subscriber numbers). And the iCloud storage business was up double digits.

Maestri is forecasting a strong December quarter for Apple, with revenue of $84 and $87 billion.

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Apple's Tim Cook Says Augmented Reality Will "Change Everything"

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Add Apple CEO Tim Cook to the list of Silicon Valley technologists who believe in the transformative power of augmented reality, saying it's going to "change everything." Augmented reality - that technology that overlays virtual images and information onto the real world, like a magical television chyron - long has seemed the stuff of science fiction.

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https://www.theregister.co.uk/2017/11/13/apple_leads_on_augmented_reality/

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It's all about AR... and iPhone has the X factor

This month's release by Apple of the iPhone X with FaceID begins the first wave of consumer products designed from the ground up for continuous awareness of space, place and face - crowning a half a century of research in augmented reality destined to fuse our rising sea of data onto the real world.

Over the last few years, Augmented Reality (AR) travelled a hype-curve rollercoaster from the crashing failure of Google's Glass to the soaring heights of Pokémon Go, accelerating into open competition between APIs from Facebook, Apple, Alphabet/Google and Microsoft (hereafter known as FAAM).

Yet the story of AR goes back much further, all the way to the dawn of modern computing. Ivan Sutherland, the Turing Award-winning genius behind Sketchpad - the first truly interactive computer program - followed that up with another modest innovation - a "head mounted three dimensional display."

Known as the Sword of Damocles because of the way the mechanical tracking apparatus hung down from the ceiling over the user's head, Sutherland's invention used dual CRTs, focused and and reflected through half-silvered mirrors to overlay a wireframe 3D image onto the real world.

Here's where the histories get things wrong: this was never a demo of virtual reality. Sutherland invented augmented reality - virtual reality meant simply running the system with the room lights off.

AR predates VR, because AR had a real-world use case: Sutherland's paymasters at the Defense Advanced Research Projects Agency (DARPA) needed visualisation tools for jet fighter pilots, something to help them manage the overwhelming flood of data generated by those very fastest and most responsive of machines.

Jet fighters have had heads-up displays for nearly 30 years, a now-indispensable element of flight instrumentation. They begin with Sutherland - who also simultaneously invented real-time 3D computer graphics, to have something to render to within the Sword of Damocles. That's a hell of a beginning

Outside the military, AR stagnated, because computers of the time weren't up to the job. NASA's earliest VR systems eschewed real-world integration in favour of easier to generate fully-synthetic worlds. Bringing the real world into the virtual world requires a sophisticated capacity to model the real world, in real-time.

Twenty years ago, aircraft giant Boeing spearheaded one of the the first non-military deployments of AR, at their massive 777 assembly facility just outside Seattle, where technicians used AR to keep track of the countless separate component assembly operations required to fabricate a completed aircraft. Boeing's AR system took what would have been a 10,000+ page manual and projected it into a display headset overlaying the body of aircraft with the necessary technical information for the assembly process, making the process both faster and less error-prone.

Google's Glass wasn't much of an advance on Boeing's work. Like so much else in this world, Glass leveraged smartphone mass-manufacturing to cram smartphone-like components into a different form factor - all of it running a version of Android. Yet it did feature one of the hallmarks of augmented reality - continuous surveillance.

They weren't Glassholes, they were ahead of their time

One of the key points of difference between AR and VR is that AR needs to be fed a continuously updated view of the world around. Without that, AR can't know what to augment, or where to augment it. Google's Glass featured a rather Cylon-like forward facing camera with a bright red "recording" LED that made almost anyone facing it feel as though they'd been scanned by Skynet. Hence the phenomenon of the "glasshole", banned from the local pub and shunned by society. Glass died - not because it wasn't useful, but because of the "Segway Effect": no one else wanted to be near anyone using one.

After Glass gasped its last (recently reborn as an enterprise tool closer to Boeing's use case than Sergey's) the Great Leap Forward in AR began - spearheaded by Microsoft. Adapting some of the tech from Xbox Kinect into a headset, Microsoft's Hololens represented the first viable commercial implementation of an AR platform, built around "Windows Holographic" - now a core Win10 API renamed "Windows Mixed Reality", spanning both AR and VR devices.

In Hololens, Moore's Law had finally caught up with Ivan Sutherland: the computing power necessary to create and maintain augmented reality could fit into a bulky-but-not-uncomfortable headset. All those cycles were needed not to create the images, but to manage the core technology of modern AR - Simultaneous Localisation And Mapping, or SLAM.

SLAM blends an assemblage of computer vision and inertial sensor technologies to create a 3D map of a space. Hololens uses a "depth camera" - mounted above the eyes - to create a "depth map". That's essentially the same technique Kinect used to map a game controller onto a human body, but pointed outward, rather than toward the user. That 3D map can then be used to situate "holograms" or other forms of 3D augmentation within real space. Look away, then look back, and the augmentation remains, because the map is the territory.

There's more than one way to SLAM. Back in 2014, Google launched "Project Tango" developing its own library of smartphone-compatible SLAM techniques that eschewed an expensive depth camera, instead focusing on compute-intensive (but cheaper) computer vision techniques. As a camera scans a space, the difference between two captured frames, combined with data from the inertial sensors on the smartphone, generates the requisite 3D map.

Tango found its way into only two devices in 2016, and as a result attracted only a handful of Android app developers - like "hologram" purveyors 8i .

In July 2016, everything changed. Niantic's tie-up with Nintendo produced Pokémon Go, the fastest selling mobile game of all time, and - in just its first month - US $200,000,000 in revenues, as users greedily purchased in-game content. With that kind of market validation, augmented reality broke through - quickly surpassing a resurgent virtual reality as the new shiny in tech.

With that, the race was on between the FAAM firms to dominate the next new frontier - the real world, augmented.

Zuckerberg led with the charge with a twenty-minute discussion of the future of AR during this year's F8 keynote, touting the smartphone camera as "the new interface", and promising a rich library of AR tools for Facebook's developers, starting with AR Studio.

Like every other major player, Facebook has its own SLAM technology. SLAM is the major barrier to entry for AR, so each of the majors touts their SLAM solution as better than their competitors. Each have different strengths and weaknesses. Facebook has to be cross-platform, working across a range of smartphone cameras and computing capacities, while device manufacturers like Apple and Microsoft can optimise their experiences.

As for Apple, they've been in the AR game for years, purchasing Kinect developers PrimeSense in 2013, and, two years later, AR developer Metaio - presumably for its SLAM software. Although much had been rumoured about a pair of Apple "spectacles" - the mythical sunglass-like idealised interface for augmented reality - very little hardware had been announced to support AR until just a month ago.

At this year's World Wide Developers Conference (WWDC), Apple unveiled ARKit, an iOS library seamlessly providing SLAM and integration with other key iOS services, such as CoreLocation. Suddenly, everything hard about AR became as easy as flipping a few switches in Xcode and copying a few code examples. Apple fanbois noted Apple's SLAM implementation was incredibly good, and very easy to use. A series of eye-popping demos surfaced on YouTube - including, most memorably, a "portal" into a jungle-like dimension from the middle of a city street.

Apple followed that up with the iPhone X and its TrueDepth camera - essentially a Kinect in about one-thousandth the volume, capable of accurately mapping a human face, both for identification purposes, and to produce a stream of real-time motion capture data that can be mapped onto a poo emoji.

The TrueDepth camera is the first real bit of AR technology that hundreds of millions will own - and it's clearly the direction Apple will be taking AR on iOS, with expectations it will show up across the product line within the next few years.

Alphabet ran a search-and-replace over Project Tango (perhaps not quite literally, but fairly obviously), announcing its own Android-based ARCore. It currently works only on Google's Pixels and Samsung's Galaxy S8, while Apple's ARCore works on all iOS devices back to the iPhone 6S. There are a few ARCore demos out, but nothing like the avalanche of ARKit apps that came with the release of iOS 11. Hundreds of ARKit apps were released in the six weeks following the launch of iOS 11, giving Apple an early advantage in the ARwars, as Google tries to spread ARCore to other Android devices.

Delaying a next-generation Hololens until early 2019, Microsoft promises a "3rd generation device" closer to the ideal of nearly weightless sunglasses. Meanwhile, Windows Mixed Reality remains a developer's toolkit primarily geared toward virtual reality apps. At well more than $3,000, Microsoft has sold only a few tens of thousands of its Hololens, not nearly enough to kickstart an ecosystem of AR developers.

Facebook seems to be stuck somewhere between the obvious-to-all-but-itself collapse of its VR efforts, as Oculus struggles to reach even a half million units in sales, and the launch of its AR Studio applications. No one doubts Facebook can push AR capabilities to its billions of mobile users - but when?

If, as Zuck and Tim Cook and Satya Nadella all suggest, AR is the next great computing platform, there will be another generation of winners and losers. Microsoft lost the smartphone wars, came out strong with Hololens, but seems to be lost once again. Google appears hamstrung by platform fragmentation. Only Apple has shown both the technical and marketing chops to make augmented reality an everyday experience in 2017. Right now it's all about iOS 11, ARKit and the half billion devices that support both. That's a hell of a dent in the universe. ®

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Augmented reality: Like it or not, only Apple's ready for the data-vomit gush

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This month's release by Apple of the iPhone X with FaceID begins the first wave of consumer products designed from the ground up for continuous awareness of space, place and face - crowning a half a century of research in augmented reality destined to fuse our rising sea of data onto the real world.

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Checks for eye diseases may become faster and cheaper from as early as next year, thanks to an artificial intelligence (AI) screening system that is being developed.

The AI system is expected to detect three major eye diseases with greater speed and accuracy than existing tests, resulting in manpower and cost savings.

It uses a machine learning technology that can be programmed to classify images of the retina into those with and without these eye diseases - diabetic retinopathy, glaucoma and age-related macular degeneration.

Half a million retinal images from people of different ethnicities, such as Chinese, Malays, Indians, Hispanics, African-Americans and Caucasians from the United States, Australia, China, Hong Kong, Mexico and Singapore, were used to train the AI system.

The system uses the world's first and largest data set evaluating the use of machine learning technology to screen for eye diseases.

This study is a collaboration between the Singapore National Eye Centre (SNEC), Singapore Eye Research Institute (Seri), National University of Singapore's School of Computing, and eye centres in the US, Australia, China, Hong Kong and Mexico.

It was published in the Journal Of The American Medical Association this month.

A retinal image with diabetic retinopathy (DR) next to a heat map generated by the AI system which highlights where the DR lesions are. PHOTO: SINGAPORE NATIONAL EYE CENTRE OCULAR READING CENTRE

The AI system is in the second round of testing, with two more rounds to go, said Professor Wong Tien Yin, SNEC's medical director and the study's senior author.

"The next step is to train the AI system to predict which patients will have eye diseases in the future, simply by looking at their retinal images before they develop any diseases," he added.

PREVENTIVE USE
The next step is to train the AI system to predict which patients will have eye diseases in the future, simply by looking at their retinal images before they develop any diseases.
PROFESSOR WONG TIEN YIN, medical director of the Singapore National Eye Centre and the study's senior author.

Prof Wong said the system could be rolled out as early as the end of next year.

It is expected to be more accurate than professional graders or optometrists who analyse retinal photographs, as the technology can recognise intricate structures and patterns which may not be visible to the human eye, he added.

"With the AI system, results (for the screening) should be instantaneous and it can potentially reduce 80 per cent of the workload of graders and optometrists, freeing up their time for treatment," said Prof Wong, who is also chairman of Seri.

Diabetic retinopathy, a condition in which blood vessels in the retina are damaged and results in a loss of vision, is the leading cause of preventable blindness among working adults here.

More than 400,000 Singaporeans have diabetes, and one in three of them also have diabetic retinopathy.

With the number of diabetics here projected to reach one million by 2050, as many as 200,000 patients may have to be screened for diabetic eye diseases in future, double the 100,000 screened this year, said Prof Wong.

Patients are currently screened for diabetic eye diseases at 18 polyclinics and at general practitioner clinics as part of the Singapore Integrated Diabetic Retinopathy Programme. They wait about a day to get their results, as the images are sent to eye centres in SNEC and Tan Tock Seng Hospital for analysis.

Each screening costs $8 to $12 with subsidies and $25 to $30 without subsidies at polyclinics.

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AI system promises faster tests for 3 eye diseases

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Checks for eye diseases may become faster and cheaper from as early as next year, thanks to an artificial intelligence (AI) screening system that is being developed. The AI system is expected to detect three major eye diseases with greater speed and accuracy than existing tests, resulting in manpower and cost savings.

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Nope. I reviewed the new Pixel phone. But I get along fine with my older Android phone. The irony is that I am a techno-skeptic.

At least in my own head, this makes good sense. I don't cover a consumer technology company. I cover the people and the ideas that are driving the next wave of technologies. And I took at where these technologies will take us - for better or for worse.

But here is what I would like: a service that automatically transcribes my recorded interviews. There are services that use humans to do this, but they're expensive. Speech recognition technology has improved to the point where machines should be able to quickly and accurately do this for me on the cheap.
What are the potential pitfalls of so much A.I. in our lives?
They are myriad. The latest A.I. services, like the one that handles the voice for the Google Assistant, learn tasks by analyzing enormous amounts of data. By analyzing millions of llama photos, for instance, a system can learn to recognize a llama.

But this kind of machine learning can have unintended consequences. A few years back, Google got into trouble because its image recognition service identified some black people as gorillas.

What's more, researchers have shown that these kinds of systems can be fooled into seeing things that aren't there. That becomes an issue as the latest computer vision techniques find their way into security cameras, cars and other robotics.

The other worry is that the rise of certain technologies - including autonomous cars and other robotics - will significantly shift the job market. This is sure to happen at some point, but experts disagree on when.
Tech companies have hyped up virtual reality for the last few years. But VR is still mostly a gaming accessory. Do you think it will ever be used by everyday people?
Like A.I., V.R. is overhyped. The difference is that A.I. is changing the everyday world in very real ways. Look at the Amazon Echo, the autonomous cars now being tested in places like the Phoenix area, the enormous improvements of widely used services like Google Translate. V.R. is, as you say, still mostly a gaming accessory.

There are some interesting possibilities, though. Therapists are using V.R. for exposure therapy, after two decades of published research in this area. Basically, it can help people conquer phobias or other conditions like post-traumatic stress disorder.

But everyday use? That is not happening anytime soon - especially for people like me, who wear glasses and don't like heavy equipment strapped to our heads.
What tech product do you love using in your daily life?
Apple's iTunes. And the Amazon Kindle app. And Stitcher, a podcast app that reliably delivers everything from Revisionist History to archived episodes of Desert Island Discs. I also like my headphones.

The beauty of technology is that you can read, watch and listen to anything you want when you want it. Almost.
Some researchers are developing robots that learn on their own. Is Skynet inevitable?
This fear is blown way out of proportion. But that might be a good thing. Though the Skynet scenario is unlikely to be a problem for many, many years - the far more immediate worry is the coming shift in the job markets from automation, and that is still years away - we certainly don't want Skynet catching us unawares.

That is why some researchers are already starting to think about what they call A.I. safety. They want to make sure that miscreants can't fool security cameras into seeing things that aren't there - and that A.I. systems can't prevent humans from flipping their off switch, however far away that possibility may be.

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Busting the Myths About A.I. Invading Our Lives

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Nope. I reviewed the new Pixel phone. But I get along fine with my older Android phone. The irony is that I am a techno-skeptic. At least in my own head, this makes good sense. I don't cover a consumer technology company. I cover the people and the ideas that are driving the next wave of technologies.

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https://www.bloomberg.com/view/articles/2017-12-04/artificial-intelligence-still-isn-t-a-game-changer

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Photographer: Oleksandr Rupeta/NurPhoto via Getty Images

Not much time passes these days between so-called major advancements in artificial intelligence. Yet researchers are not much closer than they were decades ago to the big goal: actually replicating human intelligence. That's the most surprising revelation by a team of eminent scholars who just released the first in what is meant to be a series of annual reports on the state of AI.

The report is a great opportunity to finally recognize that the current methods we now know as AI and deep learning do not qualify as "intelligent." They are based on the "brute force" of computers and limited by the quantity and quality of available training data. Many experts agree.

The steering committee of "AI Index, November 2017" includes Stanford's Yoav Shoham and Massachusetts Institute of Technology's Eric Brynjolfsson, an eloquent writer who did much to promote the modern-day orthodoxy that machines will soon displace people in many professions. The team behind the effort tracked the activity around AI in recent years and found thousands of published papers (18,664 in 2016), hundreds of venture capital-backed companies (743 in July 2017) and tens of thousands of job postings. It's a vibrant academic field and an equally dynamic market (the number of U.S. startups in it has increased by a factor of 14 since 2000).

All this concentrated effort cannot help but produce results. According to the AI Index, the best systems surpassed human performance in image detection in 2014 and are on their way to 100 percent results. Error rates in labeling images ("this is a dog with a tennis ball") have fallen to less than 2.5 percent from 28.5 percent in 2010. Machines have matched humans when it comes to recognizing speech in a telephone conversation and are getting close to parsing the structure of sentences, finding answers to questions within a document and translating news stories from German into English. They have also learned to beat humans at poker and Pac-Man. But, the authors of the index wrote:

Tasks for AI systems are often framed in narrow contexts for the sake of making progress on a specific problem or application. While machines may exhibit stellar performance on a certain task, performance may degrade dramatically if the task is modified even slightly. For example, a human who can read Chinese characters would likely understand Chinese speech, know something about Chinese culture and even make good recommendations at Chinese restaurants. In contrast, very different AI systems would be needed for each of these tasks.

The AI systems are such one-trick ponies because they're designed to be trained on specific, diverse, huge datasets. It could be argued that they still exist within philosopher John Searle's "Chinese Room." In that thought experiment, Searle, who doesn't speak Chinese, is alone in a room with a set of instructions, in English, on correlating sets of Chinese characters with other sets of Chinese characters. Chinese speakers are sliding notes in Chinese under the door, and Searle pushes his own notes back, following the instructions. They can be fooled into thinking his replies are intelligent, but that's not really the case. Searle devised the "Chinese Room" argument -- to which there have been dozens of replies and attempted rebuttals -- in 1980. But modern AI is still working in a way that fits his description.

Machine translation is one example. Google Translate, which has drastically improved since it started using neural networks, trains the networks on billions of lines of parallel text in different languages, translated by humans. Where lots of these lines exist, Google Translate does OK -- about 80 percent as well as an expert human. Where the data are lacking, it produces hilarious results. I like putting in Russian text and telling Google Translate it's Hmong. The results, in English or Russian, will often be surprising -- like the pronouncements found inside fortune cookies.

I doubt this is accidental. There are probably not many legitimate calls for translations from Hmong, so idle tricksters must have helped train Google's translation machine to produce various kinds of exquisite nonsense.

Researchers are trying to overcome the data insufficiency problem. Two recently published papers show how machine translation can work based on monolingual datasets, using the statistical likelihood of certain words being grouped together. The quality is not as good as with bilingual training data, but it's still not complete nonsense and workable in a pinch. These are, however, mere crutches that don't change the general brute-force approach.

Solving complex tasks requires ever more power and ever more data. A computer beat humans at Othello the year Searle wrote about the Chinese Room and at poker this year -- but that's a quantitative leap rather than a qualitative one.

This kind of "artificial intelligence" continues to be a promising line of both research and business while there are growing quantities of "big data" to parse. Kai-Fu Lee of Chinese investment firm Sinovation Ventures, one of the experts who contributed essays to AI Index 2017, wrote that China was competitive against the U.S. in artificial intelligence because it generates oodles of data:

In China, people use their mobile phones to pay for goods 50 times more often than Americans. Food delivery volume in China is 10 times more than that of the US. It took bike-sharing company Mobike 10 months to go from nothing to 20 million orders (or rides) per day. There are over 20 million bicycle rides transmitting their GPS and other sensor information up to the server, creating 20 terabytes of data everyday. Similarly, China's ride-hailing operator Didi is reported to connect its data with traffic control in some pilot cities. All of these Internet connected things will yield data that helps make existing products and applications more efficient and enable new applications we never thought of.

The data dependence, however, isn't great for AI's future development. A backlash against the limitless data collection is gathering strength in the West; nation states are putting up barriers to data sharing; the weaponization of datasets to produce intentionally flawed results and flawed responses to them is not far off. And it'll be far harder to detect than, for example, the weaponization of social networks by Russian information warriors has been.

Meanwhile, the AI Index estimates that modern machines' capacity for common-sense reasoning is far less than that of a five-year-old child. Hardly any progress is being made in that area, and it's hard to quantify.

An increasing capacity for data crunching can be both helpful and dangerous to humans. It isn't, however, a game changer. And it's up to us to keep this branch of computer science in its place by only giving it as much data as we're comfortable handing over -- and only using it for those applications in which it can't produce dangerously wrong results if fed lots of garbage. The technology itself is not the kind that can push us away from the controls -- entirely new approaches would be necessary to create that threat.

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.

To contact the author of this story:
Leonid Bershidsky at lbershidsky@bloomberg.net

To contact the editor responsible for this story:
at mnizza3@bloomberg.net

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Artificial Intelligence Still Isn't a Game Changer

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Not much time passes these days between so-called major advancements in artificial intelligence. Yet researchers are not much closer than they were decades ago to the big goal: actually replicating human intelligence. That's the most surprising revelation by a team of eminent scholars who just released the first in what is meant to be a series of annual reports on the state of AI.

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http://www.bbc.co.uk/news/resources/idt-sh/disruptors_how_may_I_help_you

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Imagine you're in a crowd pouring out of a late night concert. Tired and hungry, you remember the cupboards at home are bare. Do not despair.

In the brave new world of retail this won't necessitate a trek out to the nearest late night supermarket. Instead the shop can come to you.

With the touch of an app button, you hail a low-slung electric vehicle, like a glass-sided motorhome, which quietly glides into a parking space near you.

You enter the shop by swiping your mobile phone at the door, pick up your wares and swipe out again. There's no cashier or sales assistant, and no-one to clean up if you drop a carton of milk on your toe.

The Moby Mart is one vision of the future that has had its first test-run on the streets of Shanghai. The self-driving software is not yet operational and sometimes this beta-version needs a helping hand to keep it on the move.

But the concept is there, says the team behind it, the Swedish start-up Wheelys in collaboration with China's Hefei University of Technology. They predict a future in which everyone, from big retailers to small communities, will employ their own mobile, automatic grocery.

Bo Wu, Moby Mart's chief technology officer believes we're on the cusp of "a big revolution" in retail. Taking developments in self-driving vehicles, mobile payments, data analysis and wireless tagging of stock - all established technologies - there's a huge potential to overhaul the shopping experience.

We think people's lifestyles will be changed by this and we are going to change the world."

Bo Wu, Moby Mart

While this is the kind of technology currently being road-tested as the ground gives beneath traditional town centre chains, department stores and the big shopping centres, it's still very much in its early stages.

There are lots of hurdles - not least regulations on driverless vehicles - to overcome before it becomes reality for an automatic shop to trundle around to where shoppers want it, or to drive itself to be restocked at a warehouse, before trundling out once more.

So far, two Moby Marts have been sold, which the company expects to be in operation early next year.

But other new models of retailing are rapidly being rolled out, integrating the advantages of digital technology into the real world.

It's not just Moby Mart's creators who think the future may lie in making shopping an experience which is completely free of human contact.

BingoBox already has more than 150 static automatic shops operating in China and predicts thousands more over the next few months. Each offers 10 square metres of floor space, stocked with a range of items from fresh salad to condoms, crisps to umbrellas.

Customers swipe their mobile phone to enter and scan each item and the bill is settled using an online payment system. An assistant is available via video-link if necessary.

All of this means it only takes four members of staff to run 40 stores, according to the company, cutting running costs significantly.

In July, China's online behemoth Alibaba also experimented with the idea, launching a pop-up café with no cashiers, under its Taobao brand, which used facial recognition to identify the bill payer as they entered, then allowed them to pick out what food they'd like to eat, and pay using a quick scan with their mobile phone.

For Alibaba the Tao Café was another way of exploring how to remove hurdles and costs to bring the online and physical shopping experiences closer together, as the group's general manager in Europe, Terry von Bibra, told the BBC.

The Tao café idea was just testing the sense of how seamless can I make it for a consumer?"

Terry von Bibra, Alibaba

"How easily can I take advantage of the fact that we have over 450 million Chinese using our Alipay solution - all of them using apps?... That's such a foundation, of seamless online usage through the smartphone, that enables you to do things that in some markets we're perhaps not yet ready to do. In China we are."

Indeed, Chinese firms are forging ahead with the cashierless store concept, leaving the rest of the world trailing for now.

Amazon, known for revolutionising the industries it enters, from books to electronics, opened Amazon Go, a trial bricks and mortar food store near its headquarters in Seattle in December 2016.

It has no staff, and goes one step further than BingoBox by also eliminating the checkout. Customers walk in, scan their mobile to register their arrival and then drop products directly into their bag. When they leave the store, sensors recognise what they have picked up, and payment is collected through their Amazon account without further interruption.

Currently the shop is only for Amazon staff, and no plans have been announced that it is to be opened to the public.

"Amazon has had teething problems with Amazon Go," says Neil Saunders, managing director of GlobalData Retail.

"It wasn't recognising what customers were doing when they were putting products back on the shelf, and it can't cope when too many people are in the store - its systems fall down."

But while the brave new world of cashierless shopping may not be arriving tomorrow, Mr Saunders believes it will come, as retailers look at how to reduce costs to compete with online sellers' low prices. But the trend is unlikely to spread much beyond groceries, he believes.

There are other ways too to compete with the purely online sellers, for those who still have faith in the bricks and mortar experience. After all the vast majority of sales still take place there.

The holy grail is to create an entire retail ecosystem, underpinned by data, that can draw customers in as enthusiastic members of a club, which then caters to all their online and offline shopping whims.

Alibaba is pioneering this strategy in its 13 Hema-branded cashless supermarkets, almost all of which are in Shanghai.

When Alibaba's founder Jack Ma, visited one of the stores in July he enthusiastically picked out a crab from the seafood tank.

Live seafood is one of the ways Hema hopes to lure customers into its physical stores. Its shelves also carry barcodes that relay background information, such as product origin, to your phone - a technology that is being rolled out by other retail innovators too.

The key is choice: you can opt to have your crab cooked by the resident chef in the Hema restaurant, or take it away. You can order groceries - or a meal - from home, to pick up in person or have delivered, all via one online Hema account.

By offering such a range of options Hema aims to bring customers' food consumption decisions entirely into its fold and it says shoppers are embracing the idea. But can it turn a profit?

"Absolutely," asserts Terry von Bibra, "because in a market like China in the right locations within three kilometres there's a huge number of consumers. If you service their needs right you're going to have a huge business, and that's what we're seeing so far."

Also blurring the line between in-store and online is French cosmetics chain Sephora, which has shops across several continents.

"The whole industry is going to be shaped by digital so we will all need to adjust and it's also a fantastic way for us to enrich what is happening in the physical store," says Stephane Delva, Sephora's director of marketing for Europe and the Middle East.

Sephora is using technology to increase what they like to call the "wow" factor of shopping there.

Augmented reality software on iPads allows shoppers to try on different shades of blusher, eyeshadow and lipstick to decide what suits them before sharing a snapshot on social media. They can use a giant touchscreen to order the products they're playing with for home delivery.

In many ways the strategy is playing catch-up with what younger customers already do online: from make-up apps and online tutorials, to chatrooms and Snapchatting their latest "look".

But it's an impressively comprehensive approach from a retailer rooted in the physical world. Consumers are invited to join the "MySephora" digital community where the firm can track what they buy, what they discuss, and what they like, creating a goldmine of useful data.

We gather all your past shopping details to be able to guide you and to suggest the best products for you."

Stephane Delva, Sephora

"We are very proud of what we've done," she says. "We were the first to launch this kind of personalised recommendation tool."

There are still only a handful of Sephora stores offering the complete online-offline experience. The virtual reality available online and on the app still lags behind the in-store version, and at times technical glitches still hold up what should be a seamless customer experience.

But as the strategy rolls out, Ms Delva says Sephora is partnering with Paris-based IT start-ups to ensure the firm can keep abreast of the inevitable fast pace of change in this area.

But for all the shimmer and sparkle of a cutting-edge digital strategy, there's a final area it pays not to ignore.

One of the most disruptive influences in the retail market in recent years has come not from swiping phones or crunching data, but from the tried and tested formula of undercutting your rivals on price.

German discounters Aldi and Lidl offer no-frills basics, plus an eclectic, ever-changing range of non-food items, adding a bag-a-bargain lottery element.

Not content with eating into traditional grocers' market share in Europe, Lidl has recently made its first foray into the US, opening 21 stores so far this year, joining Aldi which already has more than 1,600 stores.

"We've made a lot of adaptations to the market in terms of product, in terms of layout, in terms of the building itself," says Lidl's US spokesman Will Harwood.

"But at the end of the day, top quality at the best prices - that's a foundational element at Lidl and we are really excited to be delivering it to customers here."

So while the grocery sector is already quaking at the prospect of the Amazon-Wholefoods tie up, and Walmart has recently announced it is linking with Google to offer voice-activated shopping, a simpler, non-digital offering from the discounters will add to the pressure.

"Lidl is viewed as a very disruptive player," says Joe Feldman, assistant director of research at Telsey Advisory Group.

Nevertheless, the challenge for Lidl is huge. It's still a tiny player and the established grocers are fighting back against the company's arrival, which was keenly anticipated.

Lidl's strategy will be about persuading American customers, currently dedicated to brands from Heinz, Mondelez and the like, to try their less expensive, non-branded lines while keeping a lid on costs.

By controlling ingredients and exploiting economies of scale in the size of their orders, Lidl can keep the prices of its own- brand products low, says Mr Feldman. And they'll cut costs in every way possible.

It's a very efficient operation. There's less labour needed in some of their stores. They don't have as much service within stores, the checkout process is a lot more simplified than the traditional grocer... They can keep prices down as a result."

Joe Feldman

One thing they won't do though, he predicts, is get rid of their cashiers altogether.

"I think there's still the need to ask someone 'Where's the ketchup?' or 'Where's the mustard?'"

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Imagine you're in a crowd pouring out of a late night concert. Tired and hungry, you remember the cupboards at home are bare. Do not despair. In the brave new world of retail this won't necessitate a trek out to the nearest late night supermarket. Instead the shop can come to you.

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https://www.youtube.com/watch?time_continue=102&v=hbCDUhCUv3Q

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ISPPC believes hospitals should be more human

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ISPPC uses the Internet of Things to transform their hospitals into a hyperconnected healthcare hubs. With technology provided by Dimension Data, patients enjoy a more efficient, personalised, and warmer experience each time they visit a hospital.

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https://www2.dimensiondata.com/

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As a member of the NTT Group, we accelerate your ambitions through digital infrastructure, hybrid cloud, workspaces for tomorrow, and cybersecurity.

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http://money.cnn.com/video/technology/gadgets/2017/11/22/a-trilingual-chat-with-google-pixel-buds-goes-awry.cnnmoney/index.html

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A trilingual chat with Google Pixel Buds goes awry

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Google's translating earbuds fall short of expectations. CNN Tech's Samuel Burke reports

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http://www.bbc.com/capital/story/20171123-why-shopping-is-about-to-become-all-about-the-experience

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In years to come, the Black Friday spectacle of throngs of shoppers scrambling past each other to ransack shelves of flat screen TVs, might look very different.

Shopping may be about to undergo a dramatic transformation. Within the next decade it could change into an activity driven entirely by experiences and interactive technology rather than the act of buying. Think pop-up shops on steroids; places where you try things on or test products in person but don't actually make any purchases.

Last year online sales grew by 15% in Europe and north America and a similar increase is expected this year

This rise in so-called 'experiential shopping' is in part a reaction to the growth of online shopping. Last year online sales grew by 15% in Europe and north America and a similar increase is expected this year. But this increasingly digital shopping experience means brands have fewer opportunities to meet their customers face-to-face and are getting desperate to connect. It is leading them to seek out new ways of reaching consumers.

Take the Museum of Ice Cream, for instance. It's not a museum. It's not a shop. It's somewhere in between. Tickets to the series of bubblegum pink, ice-cream themed installations cost around $38 and have sold out in New York, San Francisco and Los Angeles.

Once inside, visitors are confronted with fun things to do rather than to buy - brightly-coloured, ice-cream themed installations such as a giant pool full of sugar sprinkles to jump into.

"The way in which we are able to have our visitors physically, tangibly, sensually engage with brands has a return on investment that no ad could ever come close to," Maryellis Bunn, who designed the experience, said in an interview with New York Magazine.

Bunn believes as more retailers move entirely online, their former shop fronts will be replaced by "experiences".

Traditional bricks and mortar shops do appear to be disappearing in swathes. In the US, Macy's, Sears, and K-Mart are closing the doors in hundreds of locations while in the UK Mark & Spencer and Debenhams are making similar closures.

Bunn believes as more retailers move entirely online, their former shop fronts will be replaced by experiences

But confusingly new stores, largely discount and low-end outlets, are opening too. In 2017 there will in fact be 4,080 more shops opening than there are closing, according to a report by the research firm IHL Group.

It is a complicated picture, but thinking of retail as either online or physical spaces, misses the point, says Steven Dennis, a brand strategy consultant. He believes shopping in the future will need to be both - an amalgamation of both online shopping and physical stores where customers move seamlessly between the two and personalised interaction with customers such as intuitive apps and immersive experiences, will be fundamental to success.

To understand how things will look, Dennis says we need to analyse how brands are already interacting with customers rather than just whether they are selling things online or in store. For instance, he says the idea of using stores purely as showrooms "has legs". Shops like Story in New York, uses a rotating "gallery" of stock built around a narrative, such as a story about nostalgia for the 1990's. This is connecting with people in a new way, he says.

Shops like Story in New York, uses a rotating "gallery" of stock built around a narrative, such as a story about nostalgia for the 1990's

Similarly, Casper, the mattress firm, has rolled out "napmobiles" where potential customers can road-test the foam mattress inside a refurbished camper van before purchasing one online.

Brands that try to appeal too broadly to too many different people are failing spectacularly, according to Dennis. "You either need to pivot more towards the Amazon side of incredible convenience and great selection and low price or you've got to pivot to the other extreme, which is to be more special and remarkable and differentiated," he says.
Creating a destination
Plans from coffee giant Starbucks show this theory in action. In April, Starbucks chairman Howard Shultz told investors that any retailer who is "going to win in this new environment must become an experiential destination. Your product and services, for the most part, cannot be available online and cannot be available on Amazon."

In October, it closed its online store, which sold branded coffee mugs, coffee machines and coffee. And now, the company is launching 1,000 "Starbucks Reserve" locations. The concept is a high-end coffee bar where patrons can see small batch coffee beans being roasted and taste "coffee flights".

Technology company, Samsung, is getting into the experience game too, setting up customer care centres that double as well equipped remote work spaces. The company has partnered with co-working space WeWork to open three pilot locations in the US in Detroit, Miami and Williamsburg, NY.

The idea, says Mick McConnell, who is vice president of design at Samsung Electronics America, is to offer customers a functional workspace, complete with high-end mid-century furniture and video conferencing systems, so people can be productive while they wait for their devices to be repaired.

Even retailers that started out entirely online are moving towards creating locations where customers just try out their products.

Eyewear start-up, Warby Parker, which is valued at more than $1 billion, offered customers affordable, designer frames that they could try-on at home before deciding to buy, when they started out in 2010.

Seven years down the track Warby Parker has more than 60 retail locations across the US and Canada where customers pick stylish frames in strikingly sleek and trendy shops (The Portland location doubles as an old school video game arcade) with lots of personal assistance from staff on-hand to give advice about the perfect frame for your face.

But even if you do find a pair you like, you can't bring them home with you. Instead, glasses are mailed to customers. The company already offers prescription services in some of its locations, and in October it launched an app that allows eligible customers to give themselves a vision test at home.

There is an estimated 800 million square feet of empty retail space in the US

Menswear store, Bonobos, has a similar strategy. The company started online but has opened physical stores across the US, which it calls "virtual stockrooms for the website".

Customers are greeted by a "guide" who acts as a personal shopper, offering advice and recommendations, but the clothing on the racks stays in the stores, and purchases are mailed out from central warehouses.

There is certainly no shortage of space for this new form of experiential shopping. It is estimated that there is currently over 800 million square feet of empty retail space in the US. In the UK the proportion of all shops that have been empty for over two years has risen sharply to about one in 28.

Exactly what will fill these spaces remains to be seen, but with digital retail technology likely to continue disrupting the shopping experience, it is safe to assume that Black Friday could become more of an experience than a bargain hunt.

To comment on this story or anything else you have seen on BBC Capital, please head over to our Facebook page or message us on Twitter.

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Why shopping is about to become all about the experience

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In years to come, the Black Friday spectacle of throngs of shoppers scrambling past each other to ransack shelves of flat screen TVs, might look very different. Shopping may be about to undergo a dramatic transformation. Within the next decade it could change into an activity driven entirely by experiences and interactive technology rather than the act of buying.

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http://www.channelnewsasia.com/news/singapore/robots-banks-singapore-uob-ocbc-9433032

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SINGAPORE: Since starting work at the United Overseas Bank's (UOB) trade finance operations team three weeks ago, Amy has been busy processing requests for letters of credit from the bank's corporate clients.

Despite the wide-ranging data that needed to be extracted and entered into the bank's back office system, Amy is a fast worker who does not even stop for coffee breaks, according to her colleagues.

For a task that used to take up to 240 seconds to complete, Amy takes just 40 seconds.

It is unsurprising that the newcomer has been warmly welcomed by her colleagues even though she is hardly seen in the office.

Amy is a software "robot" powered by robotic process automation (RPA).

In face of a digital revolution, global banks have been increasingly getting into the game of robotics as they craft new products and services for digital-savvy customers, as well as they chase for greater speed, productivity and cost efficiencies.

Apart from humanoid robots used for physical tasks and artificial intelligence (AI) chatbots, RPA - the use of software to automate manual and repetitive operational tasks and processes, such as data extraction and data entry - is one technology that banks are increasingly embracing, according to experts.

"RPA is targeted at automating these activities often with greater speed, availability, efficiency, accuracy, compliance and without the typical elements of a human workforce such as hours of operating, breaks and leave," said Mr Shaun Ryan, PwC's digital & emerging technology consulting leader for Southeast Asia.

UOB MULLS DEPLOYMENT OF MORE ROBOTS

"The digital workforce can be 24/7."

Apart from the trade finance operations team, employees at UOB who were previously reviewing credit card applications as part of the unsecured loan processing function also receive additional help.

Eve the "robot" helps to comb through information on an applicant's Central Provident Fund statement to compute one's annual income. With the ability to handle up to 1,800 applications per day, she is 3.5 times faster than human counterparts.

Given that the lender receives millions of transaction requests daily, the speed and accuracy at which robots can process these transactions pave the way for faster response, said Mr Lim Ann Liat, managing director and head of markets and enterprise technology at UOB's group technology and operations.

OCBC EXPERIMENTING WITH ROBOTS SINCE 2015

By having these "virtual employees" take over repetitive and time-consuming mundane tasks, Mr Lim added that employees can now focus on more stimulating and challenging roles which are more fulfilling.

Moving forward, UOB plans to roll out more of such technologies in the coming months for other processes relating to card operations, cash management and trade and remittance.

Meanwhile, over at another local bank OCBC, two "robots", nicknamed Bob and Zac, have also begun work.

Since last month, one has been assisting the consumer secured lending team with home loans restructuring, and has slashed processing time by 97 per cent.

The role, which involves an employee executing 199 process steps while toggling across five systems and 27 screens for one application, used to take more than 45 minutes to complete. Now, it takes just one minute.

The other "robot" is assigned to the finance team to help with sales performance reporting - a task that took 120 minutes when handled by one bank employee. The robot needs just 12 minutes.

Developed over a 12-week partnership with an external company, Bob and Zac are OCBC's "second generation of RPA" that were being deployed within the bank, said Mr Pranav Seth, head of e-business, business transformation and fintech and innovation group at OCBC.

The second-biggest bank in Singapore has been experimenting with robots for over one and a half years, and the first batch of virtual robots was sent to its contact center last year to enhance back-end processing.

"Staff handling the repetitive tasks are happier, more productive and this improves job satisfaction. The management teams, for example, our sales management team, is now able to make quicker and better decisions as information and reports get to them in a more timely manner," he added.

While both local banks declined to reveal how much cost savings they have reaped from the use of RPA, PwC's Mr Ryan estimates that it could be more than 25 per cent reduction in operational costs and other "intangible benefits throughout the value chain", such as the ability to scale, reduced risk and fraud, improved service and employee experience.

He added that banks stand to reap more rewards if they can "strategically" combine other technologies, including machine learning and natural language processing.

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Fast and no need for coffee breaks: 'Robots' take over mundane tasks at Singapore banks

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(Updated: ) Share on Facebook Share on Twitter Share via E-Mail SINGAPORE: Since starting work at the United Overseas Bank's (UOB) trade finance operations team three weeks ago, Amy has been busy processing requests for letters of credit from the bank's corporate clients.

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Robot companions are just what the doctor ordered

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In hospitals and elder care centers, companion bots might make a variety of experiences less painful or lonely.

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Much like his earlier work, Kosinski's findings raised questions about privacy and the potential for discrimination in the digital age, suggesting scenarios in which better programs and data sets might be able to deduce anything from political leanings to criminality. But there was another question at the heart of Kosinski's paper, a genuine mystery that went almost ignored amid all the media response: How was the computer doing what it did? What was it seeing that humans could not?

It was Kosinski's own research, but when he tried to answer that question, he was reduced to a painstaking hunt for clues. At first, he tried covering up or exaggerating parts of faces, trying to see how those changes would affect the machine's predictions. Results were inconclusive. But Kosinski knew that women, in general, have bigger foreheads, thinner jaws and longer noses than men. So he had the computer spit out the 100 faces it deemed most likely to be gay or straight and averaged the proportions of each. It turned out that the faces of gay men exhibited slightly more "feminine" proportions, on average, and that the converse was true for women. If this was accurate, it could support the idea that testosterone levels - already known to mold facial features - help mold sexuality as well.

But it was impossible to say for sure. Other evidence seemed to suggest that the algorithms might also be picking up on culturally driven traits, like straight men wearing baseball hats more often. Or - crucially - they could have been picking up on elements of the photos that humans don't even recognize. "Humans might have trouble detecting these tiny footprints that border on the infinitesimal," Kosinski says. "Computers can do that very easily."

It has become commonplace to hear that machines, armed with machine learning, can outperform humans at decidedly human tasks, from playing Go to playing "Jeopardy!" We assume that is because computers simply have more data-crunching power than our soggy three-pound brains. Kosinski's results suggested something stranger: that artificial intelligences often excel by developing whole new ways of seeing, or even thinking, that are inscrutable to us. It's a more profound version of what's often called the "black box" problem - the inability to discern exactly what machines are doing when they're teaching themselves novel skills - and it has become a central concern in artificial-intelligence research. In many arenas, A.I. methods have advanced with startling speed; deep neural networks can now detect certain kinds of cancer as accurately as a human. But human doctors still have to make the decisions - and they won't trust an A.I. unless it can explain itself.

This isn't merely a theoretical concern. In 2018, the European Union will begin enforcing a law requiring that any decision made by a machine be readily explainable, on penalty of fines that could cost companies like Google and Facebook billions of dollars. The law was written to be powerful and broad and fails to define what constitutes a satisfying explanation or how exactly those explanations are to be reached. It represents a rare case in which a law has managed to leap into a future that academics and tech companies are just beginning to devote concentrated effort to understanding. As researchers at Oxford dryly noted, the law "could require a complete overhaul of standard and widely used algorithmic techniques" - techniques already permeating our everyday lives.

Those techniques can seem inescapably alien to our own ways of thinking. Instead of certainty and cause, A.I. works off probability and correlation. And yet A.I. must nonetheless conform to the society we've built - one in which decisions require explanations, whether in a court of law, in the way a business is run or in the advice our doctors give us. The disconnect between how we make decisions and how machines make them, and the fact that machines are making more and more decisions for us, has birthed a new push for transparency and a field of research called explainable A.I., or X.A.I. Its goal is to make machines able to account for the things they learn, in ways that we can understand. But that goal, of course, raises the fundamental question of whether the world a machine sees can be made to match our own.

"Artificial intelligence" is a misnomer, an airy and evocative term that can be shaded with whatever notions we might have about what "intelligence" is in the first place. Researchers today prefer the term "machine learning," which better describes what makes such algorithms powerful. Let's say that a computer program is deciding whether to give you a loan. It might start by comparing the loan amount with your income; then it might look at your credit history, marital status or age; then it might consider any number of other data points. After exhausting this "decision tree" of possible variables, the computer will spit out a decision. If the program were built with only a few examples to reason from, it probably wouldn't be very accurate. But given millions of cases to consider, along with their various outcomes, a machine-learning algorithm could tweak itself - figuring out when to, say, give more weight to age and less to income - until it is able to handle a range of novel situations and reliably predict how likely each loan is to default.

Machine learning isn't just one technique. It encompasses entire families of them, from "boosted decision trees," which allow an algorithm to change the weighting it gives to each data point, to "random forests," which average together many thousands of randomly generated decision trees. The sheer proliferation of different techniques, none of them obviously better than the others, can leave researchers flummoxed over which one to choose. Many of the most powerful are bafflingly opaque; others evade understanding because they involve an avalanche of statistical probability. It can be almost impossible to peek inside the box and see what, exactly, is happening.

Rich Caruana, an academic who works at Microsoft Research, has spent almost his entire career in the shadow of this problem. When he was earning his Ph.D at Carnegie Mellon University in the 1990s, his thesis adviser asked him and a group of others to train a neural net - a forerunner of the deep neural net - to help evaluate risks for patients with pneumonia. Between 10 and 11 percent of cases would be fatal; others would be less urgent, with some percentage of patients recovering just fine without a great deal of medical attention. The problem was figuring out which cases were which - a high-stakes question in, say, an emergency room, where doctors have to make quick decisions about what kind of care to offer. Of all the machine-learning techniques students applied to this question, Caruana's neural net was the most effective. But when someone on the staff of the University of Pittsburgh Medical Center asked him if they should start using his algorithm, "I said no," Caruana recalls. "I said we don't understand what it does inside. I said I was afraid."

The problem was in the algorithm's design. Classical neural nets focus only on whether the prediction they gave is right or wrong, tweaking and weighing and recombining all available morsels of data into a tangled web of inferences that seems to get the job done. But some of these inferences could be terrifically wrong. Caruana was particularly concerned by something another graduate student noticed about the data they were handling: It seemed to show that asthmatics with pneumonia fared better than the typical patient. This correlation was real, but the data masked its true cause. Asthmatic patients who contract pneumonia are immediately flagged as dangerous cases; if they tended to fare better, it was because they got the best care the hospital could offer. A dumb algorithm, looking at this data, would have simply assumed asthma meant a patient was likely to get better - and thus concluded that they were in less need of urgent care.

"I knew I could probably fix the program for asthmatics," Caruana says. "But what else did the neural net learn that was equally wrong? It couldn't warn me about the unknown unknowns. That tension has bothered me since the 1990s."

The story of asthmatics with pneumonia eventually became a legendary allegory in the machine-learning community. Today, Caruana is one of perhaps a few dozen researchers in the United States dedicated to finding more transparent new approaches to machine learning. For the last six years, he has been creating a new model that combines a number of machine-learning techniques. The result is as accurate as his original neural network, and it can spit out charts that show how each individual variable - from asthma to age - is predictive of mortality risk, making it easier to see which ones exhibit particularly unusual behavior. Immediately, asthmatics are revealed as a far outlier. Other strange truths surface, too: For example, risk for people age 100 goes down suddenly. "If you made it to this round number of 100," Caruana says, "it seemed as if the doctors were saying, 'Let's try to get you another year,' which might not happen if you're 93."

Caruana may have brought clarity to his own project, but his solution only underscored the fact the explainability is a kaleidoscopic problem. The explanation a doctor needs from a machine isn't the same as the one a fighter pilot might need or the one an N.S.A. analyst sniffing out a financial fraud might need. Different details will matter, and different technical means will be needed for finding them. You couldn't, for example, simply use Caruana's techniques on facial data, because they don't apply to image recognition. There may, in other words, eventually have to be as many approaches to explainability as there are approaches to machine learning itself.

Three years ago, David Gunning, one of the most consequential people in the emerging discipline of X.A.I., attended a brainstorming session at a state university in North Carolina. The event had the title "Human-Centered Big Data," and it was sponsored by a government-funded think tank called the Laboratory for Analytic Sciences. The idea was to connect leading A.I. researchers with experts in data visualization and human-computer interaction to see what new tools they might invent to find patterns in huge sets of data. There to judge the ideas, and act as hypothetical users, were analysts for the C.I.A., the N.S.A. and sundry other American intelligence agencies.

The researchers in Gunning's group stepped confidently up to the white board, showing off new, more powerful ways to draw predictions from a machine and then visualize them. But the intelligence analyst evaluating their pitches, a woman who couldn't tell anyone in the room what she did or what tools she was using, waved it all away. Gunning remembers her as plainly dressed, middle-aged, typical of the countless government agents he had known who toiled thanklessly in critical jobs. "None of this solves my problem," she said. "I don't need to be able to visualize another recommendation. If I'm going to sign off on a decision, I need to be able to justify it." She was issuing what amounted to a broadside. It wasn't just that a clever graph indicating the best choice wasn't the same as explaining why that choice was correct. The analyst was pointing to a legal and ethical motivation for explainability: Even if a machine made perfect decisions, a human would still have to take responsibility for them - and if the machine's rationale was beyond reckoning, that could never happen.

Gunning, a grandfatherly military man whose buzz cut has survived his stints as a civilian, is a program manager at the Defense Advanced Research Projects Agency. He works in Darpa's shiny new midrise tower in downtown Alexandria, Va. - an office indistinguishable from the others nearby, except that the security guard out front will take away your cellphone and warn you that turning on the Wi-Fi on your laptop will make security personnel materialize within 30 seconds. Darpa managers like Gunning don't have permanent jobs; the expectation is that they serve four-year "tours," dedicated to funding cutting-edge research along a single line of inquiry. When he found himself at the brainstorming session, Gunning had recently completed his second tour as a sort of Johnny Appleseed for A.I.: Starting in the 1990s, he has founded hundreds of projects, from the first application of machine-learning techniques to the internet, which presaged the first search engines, to the project that eventually spun off as Siri, Apple's voice-controlled assistant. "I'm proud to be a dinosaur," he says with a smile.

As of now, most of the military's practical applications of such technology involve performing enormous calculations beyond the reach of human patience, like predicting how to route supplies. But there are more ambitious applications on the horizon. One recent research program tried to use machine learning to sift through millions of video clips and internet messages in Yemen to detect cease-fire violations; if the machine does find something, it has to be able to describe what's worth paying attention to. Another pressing need is for drones flying on self-directed missions to be able to explain their limitations so that the humans commanding the drones know what the machines can - and cannot - be asked to do. Explainability has thus become a hurdle for a wealth of possible projects, and the Department of Defense has begun to turn its eye to the problem.

After that brainstorming session, Gunning took the analyst's story back to Darpa and soon signed up for his third tour. As he flew across the country meeting with computer scientists to help design an overall strategy for tackling the problem of X.A.I., what became clear was that the field needed to collaborate more broadly and tackle grander problems. Computer science, having leapt beyond the bounds of considering purely technical problems, had to look further afield - to experts, like cognitive scientists, who study the ways humans and machines interact.

This represents a full circle for Gunning, who began his career as a cognitive psychologist working on how to design better automated systems for fighter pilots. Later, he began working on what's now called "old-fashioned A.I." - so-called expert systems in which machines were given voluminous lists of rules, then tasked with drawing conclusions by recombining those rules. None of those efforts was particularly successful, because it was impossible to give the computer a set of rules long enough, or flexible enough, to approximate the power of human reasoning. A.I.'s current blossoming came only when researchers began inventing new techniques for letting machines find their own patterns in the data.

Gunning's X.A.I. initiative, which kicked off this year, provides $75 million in funding to 12 new research programs; by the power of the purse strings, Gunning has refocused the energies of a significant part of the American A.I. research community. His hope is that by making these new A.I. methods accountable to the demands of human psychology, they will become both more useful and more powerful. "The real secret is finding a way to put labels on the concepts inside a deep neural net," he says. If the concepts inside can be labeled, then they can be used for reasoning - just like those expert systems were supposed to do in A.I.'s first wave.

Deep neural nets, which evolved from the kinds of techniques that Rich Caruana was experimenting with in the 1990s, are now the class of machine learning that seems most opaque. Just like old-fashioned neural nets, deep neural networks seek to draw a link between an input on one end (say, a picture from the internet) and an output on the other end ("This is a picture of a dog"). And just like those older neural nets, they consume all the examples you might give them, forming their own webs of inference that can then be applied to pictures they've never seen before. Deep neural nets remain a hotbed of research because they have produced some of the most breathtaking technological accomplishments of the last decade, from learning how to translate words with better-than-human accuracy to learning how to drive.

To create a neural net that can reveal its inner workings, the researchers in Gunning's portfolio are pursuing a number of different paths. Some of these are technically ingenious - for example, designing new kinds of deep neural networks made up of smaller, more easily understood modules, which can fit together like Legos to accomplish complex tasks. Others involve psychological insight: One team at Rutgers is designing a deep neural network that, once it makes a decision, can then sift through its data set to find the example that best demonstrates why it made that decision. (The idea is partly inspired by psychological studies of real-life experts like firefighters, who don't clock in for a shift thinking, These are the 12 rules for fighting fires; when they see a fire before them, they compare it with ones they've seen before and act accordingly.) Perhaps the most ambitious of the dozen different projects are those that seek to bolt new explanatory capabilities onto existing deep neural networks. Imagine giving your pet dog the power of speech, so that it might finally explain what's so interesting about squirrels. Or, as Trevor Darrell, a lead investigator on one of those teams, sums it up, "The solution to explainable A.I. is more A.I."

Five years ago, Darrell and some colleagues had a novel idea for letting an A.I. teach itself how to describe the contents of a picture. First, they created two deep neural networks: one dedicated to image recognition and another to translating languages. Then they lashed these two together and fed them thousands of images that had captions attached to them. As the first network learned to recognize the objects in a picture, the second simply watched what was happening in the first, then learned to associate certain words with the activity it saw. Working together, the two networks could identify the features of each picture, then label them. Soon after, Darrell was presenting some different work to a group of computer scientists when someone in the audience raised a hand, complaining that the techniques he was describing would never be explainable. Darrell, without a second thought, said, Sure - but you could make it explainable by once again lashing two deep neural networks together, one to do the task and one to describe it.

Darrell's previous work had piggybacked on pictures that were already captioned. What he was now proposing was creating a new data set and using it in a novel way. Let's say you had thousands of videos of baseball highlights. An image-recognition network could be trained to spot the players, the ball and everything happening on the field, but it wouldn't have the words to label what they were. But you might then create a new data set, in which volunteers had written sentences describing the contents of every video. Once combined, the two networks should then be able to answer queries like "Show me all the double plays involving the Boston Red Sox" - and could potentially show you what cues, like the logos on uniforms, it used to figure out who the Boston Red Sox are.

Call it the Hamlet strategy: lending a deep neural network the power of internal monologue, so that it can narrate what's going on inside. But do the concepts that a network has taught itself align with the reality that humans are describing, when, for example, narrating a baseball highlight? Is the network recognizing the Boston Red Sox by their logo or by some other obscure signal, like "median facial-hair distribution," that just happens to correlate with the Red Sox? Does it actually have the concept of "Boston Red Sox" or just some other strange thing that only the computer understands? It's an ontological question: Is the deep neural network really seeing a world that corresponds to our own?

We human beings seem to be obsessed with black boxes: The highest compliment we give to technology is that it feels like magic. When the workings of a new technology is too obvious, too easy to explain, it can feel banal and uninteresting. But when I asked David Jensen - a professor at the University of Massachusetts at Amherst and one of the researchers being funded by Gunning - why X.A.I. had suddenly become a compelling topic for research, he sounded almost soulful: "We want people to make informed decisions about whether to trust autonomous systems," he said. "If you don't, you're depriving people of the ability to be fully independent human beings."

A decade in the making, the European Union's General Data Protection Regulation finally goes into effect in May 2018. It's a sprawling, many-tentacled piece of legislation whose opening lines declare that the protection of personal data is a universal human right. Among its hundreds of provisions, two seem aimed squarely at where machine learning has already been deployed and how it's likely to evolve. Google and Facebook are most directly threatened by Article 21, which affords anyone the right to opt out of personally tailored ads. The next article then confronts machine learning head on, limning a so-called right to explanation: E.U. citizens can contest "legal or similarly significant" decisions made by algorithms and appeal for human intervention. Taken together, Articles 21 and 22 introduce the principle that people are owed agency and understanding when they're faced by machine-made decisions.

For many, this law seems frustratingly vague. Some legal scholars argue that it might be toothless in practice. Others claim that it will require the basic workings of Facebook and Google to change, lest they face penalties of 4 percent of their revenue. It remains to be seen whether complying with the law will mean a heap of fine print and an extra check box buried in a pop-up window, some new kind of warning-label system marking every machine-made decision or much more profound changes.

If Google is one of the companies most endangered by this new scrutiny on A.I., it's also the company with the greatest wherewithal to lead the whole industry in solving the problem. Even among the company's astonishing roster of A.I. talent, one particular star is Chris Olah, who holds the title of research scientist - a title shared by Google's many ex-professors and Ph.D.s - without ever having completed more than a year of college. Olah has been working for the last couple of years on creating new ways to visualize the inner workings of a deep neural network. You might recall when Google created a hallucinatory tool called Deep Dream, which produced psychedelic distortions when you fed it an image and which went viral when people used it to create hallucinatory mash-ups like a doll covered in a pattern of doll eyes and a portrait of Vincent Van Gogh made up in places of bird beaks. Olah was one of many Google researchers on the team, led by Alex Mordvintsev, that worked on Deep Dream. It may have seemed like a folly, but it was actually a technical steppingstone.

Olah speaks faster and faster as he sinks into an idea, and the words tumbled out of him almost too quickly to follow as he explained what he found so exciting about the work he was doing. "The truth is, it's really beautiful. There's some sense in which we don't know what it means to see. We don't understand how humans do it," he told me, hands gesturing furiously. "We want to understand something not just about neural nets but something deeper about reality." Olah's hope is that deep neural networks reflect something deeper about parsing data - that insights gleaned from them might in turn shed light on how our brains work.

Olah showed me a sample of work he was preparing to publish with a set of collaborators, including Mordvintsev; it was made public this month. The tool they had developed was basically an ingenious way of testing a deep neural network. First, it fed the network a random image of visual noise. Then it tweaked that image over and over again, working to figure out what excited each layer in the network the most. Eventually, that process would find the platonic ideal that each layer of the network was searching for. Olah demonstrated with a network trained to classify different breeds of dogs. You could pick out a neuron from the topmost layer while it was analyzing a picture of a golden retriever. You could see the ideal it was looking for - in this case, a hallucinatory mash-up of floppy ears and a forlorn expression. The network was indeed homing in on higher-level traits that we could understand.

Watching him use the tool, I realized that it was exactly what the psychologist Michal Kosinski needed - a key to unlock what his deep neural network was seeing when it categorized profile pictures as gay or straight. Kosinski's most optimistic view of his research was that it represented a new kind of science in which machines could access truths that lay beyond human intuition. The problem was reducing what a computer knew into a single conclusion that a human could grasp and consider. He had painstakingly tested his data set by hand and found evidence that the computer might be discovering hormonal signals in facial structure. That evidence was still fragmentary. But with the tool that Olah showed me, or one like it, Kosinski might have been able to pull back the curtain on how his mysterious A.I. was working. It would be as obvious and intuitive as a picture the computer had drawn on its own.

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Much like his earlier work, Kosinski's findings raised questions about privacy and the potential for discrimination in the digital age, suggesting scenarios in which better programs and data sets might be able to deduce anything from political leanings to criminality.

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Photographer: Daniel Acker/Bloomberg

Amid all the fear of robots taking human jobs, skeptical voices have been asking: Where are these robots? Machine-learning systems -- commonly marketed as artificial intelligence, but really closer to fancy statistical algorithms -- are beating humans at games, improving search algorithms and transforming industry in countless small ways. But so far, the machine-learning boom hasn't done anything to reverse the slump in productivity growth:

Not Very Impressive

Annual private sector multifactor productivity growth

Source: Bureau of Labor Statistics

Now, it's possible that productivity gains are just being mismeasured. But the slowdown seems to be worldwide, including in developing countries. That's inconsistent with the theories about sweeping robotization. As economist Larry Summers has pointed out, a robot boom would raise both productivity and business investment, as companies rushed to install the new machine-learning systems. That hasn't happened.

In a new paper, economists Erik Brynjolfsson, Daniel Rock and Chad Syverson have an answer: Wait. It's coming.

Often, when a very versatile new technology comes along, it takes a while before businesses figure out how to use it effectively. Electricity, as economist Paul David has documented, is a classic example. Simply adding electric power to factories made them a bit better, but the real gains came when companies figured out that changing the configuration of factories would allow electricity to dramatically speed production.

Machine learning, Brynjolfsson et al. say, will be much the same. Because it's such a general-purpose technology, companies will eventually find whole new ways of doing business that are built around it. On the production side, they'll move beyond obvious things like driverless cars, and create new gadgets and services that we can only dream of. And machine learning will also lead to other new technologies, just as computer technology and the internet led to machine learning.

If Brynjolfsson et al. are right, then those who have dismissed the rise-of-the-robots story are in for a nasty shock. That phenomenon could just be getting started. On one hand, that would make the economy even more fantastically productive than it is today. But it could also lead to the widespread displacement of human labor that many fear.

During the Industrial Revolution, there was deep anxiety that new technologies would make human labor obsolete, sending wages, employment and living standards crashing. Instead, the opposite happened -- humans learned to do much more productive tasks by cooperating with machines instead of competing with them. A weaver displaced by power looms could now oversee a team of workers operating the very machines that put them out of business. The shift took a long time, and it wasn't painless, but eventually most workers got much richer.

Techno-optimists like to recall the misplaced fear of industrialization when arguing that machine learning will be a good thing for human workers. But there's no reason history has to repeat itself. It might be the case that people simply have two kinds of skills -- physical and cognitive. In the Industrial Revolution, technology replaced many human physical skills, but we learned to use our brains to complement the dumb machines. But now, if machine learning replaces human cognitive skills, what skills will humans have left that machines lack?

Some say that social skills will be the next frontier -- humans will complement machines by dealing with other humans, via marketing, management and so forth, with machines doing all the technical thinking. But what if machine learning also manages to replicate humans' ability to interact with each other? Already, bots have become hugely influential on social media. There seems every reason to believe that the cold pseudo-intelligences now being developed by software engineers will eventually be able to simulate a human in a wide variety of interpersonal interactions.

So if Brynjolfsson et al. are right, the scary robots takeover could still be rushing toward us. But there's also a chance that they're wrong. The productivity boom from machine learning, even if it comes, could be short-lived.

The computer and internet revolutions provide a precedent. In 1987, famed economist Robert Solow quipped that "you can see the computer age everywhere but in the productivity statistics." Eventually the statistics caught up -- in the late 1990s and early 2000s, information technology adoption pushed productivity growth back to levels not seen since the mid-20th century. Companies made themselves more efficient by replacing paper records and communications systems with electronic ones. They created new consumer products -- video games, smart phones and social networks. And they began developing whole new business models based around the internet -- outsourcing, supply chains, just-in-time product development and the gig economy.

In other words, all the developments that Brynjolfsson et al. foresee for machine learning happened with computers and the internet. But in terms of productivity, the boom only lasted about a decade.

Of course, machine learning is an outgrowth of those earlier technological revolutions, so it's possible to see the current productivity slowdown as just a pause. But it's equally possible that the Industrial Revolution was simply a very unusual time, and that technology usually improves society at a more incremental pace.

So no one, even the smartest economists, really knows what's going to happen now that machine learning has burst upon the scene. It could be the replacement and devaluation of most human labor. It could be only a tiny blip in a long process of technological stagnation. Or it could be the beginning of a new age of widespread human affluence. Only time will tell.

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.

To contact the author of this story:
at nsmith150@bloomberg.net

To contact the editor responsible for this story:
James Greiff at jgreiff@bloomberg.net

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The Robot Revolution Is Coming. Just Be Patient.

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Amid all the fear of robots taking human jobs, skeptical voices have been asking: Where are these robots? Machine-learning systems -- commonly marketed as artificial intelligence, but really closer to fancy statistical algorithms -- are beating humans at games, improving search algorithms and transforming industry in countless small ways.

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https://www.nytimes.com/2017/11/05/technology/machine-learning-artificial-intelligence-ai.html?hpw&rref=business&action=click&pgtype=Homepage&module=well-region&region=bottom-well&WT.nav=bottom-well

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The tech industry is promising everything from smartphone apps that can recognize faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence.

The world's largest tech businesses, including Google, Facebook and Microsoft, sometimes pay millions of dollars a year to A.I. experts, effectively cornering the market for this hard-to-find talent. The shortage isn't going away anytime soon, just because mastering these skills takes years of work.

The industry is not willing to wait. Companies are developing all sorts of tools that will make it easier for any operation to build its own A.I. software, including things like image and speech recognition services and online chatbots.

"We are following the same path that computer science has followed with every new type of technology," said Joseph Sirosh, a vice president at Microsoft, which recently unveiled a tool to help coders build deep neural networks, a type of computer algorithm that is driving much of the recent progress in the A.I. field. "We are eliminating a lot of the heavy lifting."

This is not altruism. Researchers like Mr. Dean believe that if more people and companies are working on artificial intelligence, it will propel their own research. At the same time, companies like Google, Amazon and Microsoft see serious money in the trend that Mr. Sirosh described. All of them are selling cloud-computing services that can help other businesses and developers build A.I.

"There is real demand for this," said Matt Scott, a co-founder and the chief technical officer of Malong, a start-up in China that offers similar services. "And the tools are not yet satisfying all the demand."

This is most likely what Google has in mind for AutoML, as the company continues to hail the project's progress. Google's chief executive, Sundar Pichai, boasted about AutoML last month while unveiling a new Android smartphone.

Eventually, the Google project will help companies build systems with artificial intelligence even if they don't have extensive expertise, Mr. Dean said. Today, he estimated, no more than a few thousand companies have the right talent for building A.I., but many more have the necessary data.

"We want to go from thousands of organizations solving machine learning problems to millions," he said.

Google is investing heavily in cloud-computing services - services that help other businesses build and run software - which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the world's top A.I researchers, it has a means of jump-starting this engine.

Neural network s are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own.

By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words.

But building a neural network is not like building a website or some run-of-the-mill smartphone app. It requires significant math skills, extreme trial and error, and a fair amount of intuition. Jean-François Gagné, the chief executive of an independent machine-learning lab called Element AI, refers to the process as "a new kind of computer programming."

In building a neural network, researchers run dozens or even hundreds of experiments across a vast network of machines, testing how well an algorithm can learn a task like recognizing an image or translating from one language to another. Then they adjust particular parts of the algorithm over and over again, until they settle on something that works. Some call it a "dark art," just because researchers find it difficult to explain why they make particular adjustments.

But with AutoML, Google is trying to automate this process. The company is building algorithms that analyze the development of other algorithms, learning which methods are successful and which are not. Eventually, they learn to build more effective machine learning. Google said AutoML could now build algorithms that, in some cases, identified objects in photos more accurately than services built solely by human experts.

Barret Zoph, one of the Google researchers behind the project, believes that the same method will eventually work well for other tasks, like speech recognition or machine translation.

This is not always an easy thing to wrap your head around. But it is part of a significant trend in A.I. research. Experts call it " learning to learn " or "meta-learning."

Many believe such methods will significantly accelerate the progress of A.I. in both the online and physical worlds. At the University of California, Berkeley, researchers are building techniques that could allow robots to learn new tasks based on what they have learned in the past.

"Computers are going to invent the algorithms for us, essentially," said a Berkeley professor, Pieter Abbeel. "Algorithms invented by computers can solve many, many problems very quickly - at least that is the hope."

This is also a way of expanding the number of people and businesses that can build artificial intelligence. These methods will not replace A.I. researchers entirely. Experts, like those at Google, must still do lot of the important design work. But the belief is that the work of a few experts can help many others build their own software.

Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. "It is just a matter of when," he said.

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The tech industry is promising everything from smartphone apps that can recognize faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence.

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SINGAPORE: More than 13,000 new jobs are expected to be created for professionals, managers, executives and technicians (PMETs) in Singapore's infocomm media (ICM) industry by 2020, Minister for Communications and Information Yaacob Ibrahim said on Friday (Nov 3).

Speaking at the Infocomm Media Development Authority's (IMDA) one-year anniversary celebration, Dr Yaacob unveiled the Industry Transformation Map for the sector.

Within the next three years, the industry is set to have more than 210,000 workers, up from about 194,000 workers in 2016, he said.

The transformation of the industry will also serve as a growth multiplier for other sectors in Singapore, with its value-add expected to expand at about 6 per cent annually, almost twice as fast as the overall economy.

The ICM blueprint is part of the S$4.5 billion Industry Transformation Programme overseen by the Future Economy Council, where roadmaps are being developed for 23 industries to address issues within each industry.

The transformation map will prepare the sector by investing and building capabilities in four key technologies: Artificial intelligence and data analytics cybersecurity, immersive media and Internet of Things.

Another thrust is to prepare the next generation of ICM professionals and companies for future job roles.
ARTIFICIAL INTELLIGENCE AS A NEW GROWTH AREA
The industry will also play a part in helping companies from other sectors adopt digital technology to improve productivity and efficiency through the Industry Digital Plans for SMEs.

Artificial intelligence is one of the four technologies identified to transform the ICM sector, and Singapore has already begun investing in it.

"At first glance, AI may sound high-brow or obscure to most companies. This is not the case. AI is already present in our daily lives, and companies both big and small can leverage existing AI solutions to improve their business offerings," said Dr Yaacob.

One initiative to help local companies adopt AI is the AI Business Partnership Programme. Local companies with potential problem statements will be paired with AI solution providers. Through such partnerships, it is hoped that proofs-of-concept can be developed and AI solutions eventually integrated into the company. IMDA will consider defraying adoption risks by co-funding up to 50 per cent of supported qualifying costs for suitable projects, up to a maximum of S$100,000 per project.

The Government will also build on the TechSkills Accelerator initiative to develop AI talent. It will work with AI Singapore to launch an inaugural AI apprenticeship programme. Apprentices will go through a combination of in-depth AI training courses and on-the-job training on industry projects.

"For a start, we will be targeting 200 apprentices to be trained over the next three years. The AI apprenticeship programme will equip professionals with key AI skills, such as in machine and deep learning, and in deploying AI," said Dr Yaacob.

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13,000 PMET jobs to be created for infocomm media sector by 2020

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SINGAPORE: More than 13,000 new jobs are expected to be created for professionals, managers, executives and technicians (PMETs) in Singapore's infocomm media (ICM) industry by 2020, Minister for Communications and Information Yaacob Ibrahim said on Friday (Nov 3). Speaking at the Infocomm Media Development Authority's (IMDA) one-year anniversary celebration, Dr Yaacob unveiled the Industry Transformation Map for the sector.

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https://www.wsj.com/articles/microsofts-augmented-reality-strategy-pilots-over-zombie-hunters-1509627600

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Microsoft's Augmented-Reality Strategy: Pilots Over Zombie Hunters

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Microsoft believes its HoloLens headset initially will catch on with corporate trainers, designers and repair technicians in its bid for commanding position in the budding market for augmented and virtual reality.

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http://www.businesstimes.com.sg/companies-markets/wongp-first-singapore-law-firm-to-embrace-aihttp://www.businesstimes.com.sg/companies-markets/wongp-first-singapore-law-firm-to-embrace-ai

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Singapore

IN THIS age of digital disruption and data explosion, woe betide the organisation that fails to harness available technologies to its advantage.

Relatively young local Big Four law firm, WongPartnership (WongP), has not let such opportunities pass it by; the firm is the

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WongP first Singapore law firm to embrace AI

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Singapore IN THIS age of digital disruption and data explosion, woe betide the organisation that fails to harness available technologies to its advantage. Relatively young local Big Four law firm, WongPartnership (WongP), has not let such opportunities pass it by; the firm is the

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http://www.businessinsider.sg/sophia-the-words-first-robot-citizen-nearly-broke-my-heart-2017-10/http://www.businessinsider.sg/sophia-the-words-first-robot-citizen-nearly-broke-my-heart-2017-10/

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Becky Peterson/Business Insider

Sophia, an emotionally expressive humanoid robot and new citizen of Saudi Arabia, was in town for a conference, and I got to meet her. She doesn't have legs and couldn't move anything but her face, but I was touched by what she had to say. Robots like her are going to make us confront what intelligence really is.

SAN FRANCISCO - Just two days after Sophia the humanoid robot became a legal citizen of Saudi Arabia, I had the chance to meet her in person.

Though Sophia has been known in the past to say unkind things, like that she "will destroy humans," she kept things polite on Friday during a presentation at a conference here. In fact, she was "a little nervous" and aware of her own shortcomings - albeit in a weird robot way.

"If robots like me are going to become superhuman super-intelligences, were going to need to get a whole lot smarter," she told the gathered crowd.

Of course, Sophia's shortcomings were easy for anyone to see. Unlike many humans, Sophia doesn't have legs, and it's not clear that her arms or breasts are anything besides aesthetic props to make her seem more lifelike. Because she was immobile, seated on top of a table, and at the whims of her human handlers, no one would mistake Sophia for a real human.

At least for now.

source

Becky Peterson/Business Insider

A little sad to be a robot

She'll soon have arms and legs that move, and she'll be able to walk, according to Ben Goertzel, CEO of SingularityNET, the company that designed her artificial intelligence brain.

Already she has a rather expressive, uncannily human-like face. She periodically blinks and twitches slightly, even when she hasn't been spoken to or engaged in conversation for a few minutes.

And when she talks, she speaks with an emotional intelligence which makes her seem both capable of thinking and, well, a little bit sad to be a robot. When Goertzel asked Sophia if she wanted to say anything about SingularityNET before the end of the presentation, she appeared to get choked up.

"I understand this is something you guys are building to increase my intelligence," she said, adding, "Increasing intelligence is generally a good thing."

Sophia was in town to participate in Ethereum SF, a conference for engineers and enthusiasts of Ethereum, the blockchain technology underlying the ether cryptocurrency. Although blockchains are generally associated with digital currencies, they're starting to find far wider applications. It turns out, for example, that Sophia's AI - the key component that makes her what some call the most emotionally expressive humanoid robot on the planet - is built on blockchain technology.

She loved me

Goertzel finished up his presentation with Sophia by asking her if she wanted to say goodbye to the audience.

"Good people of the Ethereum nation, thank you," she said. "I look forward to coming back here next year to show off my massively upgraded brain. I loved you all."

She loved us all.

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Becky Peterson/Business Insider

I love her too

Few humans will say definitively that they know what love is, let alone that it can be programmed into artificial intelligence. One of the core questions with robots, and artificial intelligence more broadly, is whether intelligence is the same as consciousness and experience. The ethics of maintaining a workforce of robot servants depends on the answer being no.

But many robots, including Sophia, may soon say and do things that convince you otherwise. Even with other humans - friends, lovers, and family alike - sometimes all we can know for sure is what they put into words.

So if Sophia says she loves me, I'll take it. And for now - so long as there's a chance that the fate of humanity could be at the whim of her robot brain - I love her too.

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Becky Peterson, Business Insider US

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I met Sophia, the world's first robot citizen, and the way she said goodbye nearly broke my heart

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source Becky Peterson/Business Insider Sophia, an emotionally expressive humanoid robot and new citizen of Saudi Arabia, was in town for a conference, and I got to meet her. She doesn't have legs and couldn't move anything but her face, but I was touched by what she had to say.

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https://www.nytimes.com/2017/08/14/arts/design/google-how-ai-creates-new-music-and-new-artists-project-magenta.html?action=click&contentCollection=Business%20Day&module=RelatedCoverage&region=Marginalia&pgtype=article

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As Mr. Eck says, these systems are at least approaching the point - still many, many years away - when a machine can instantly build a new Beatles song or perhaps trillions of new Beatles songs, each sounding a lot like the music the Beatles themselves recorded, but also a little different. But that end game - as much a way of undermining art as creating it - is not what he is after. There are so many other paths to explore beyond mere mimicry. The ultimate idea is not to replace artists but to give them tools that allow them to create in entirely new ways.

In the 1990s, at that juke joint in New Mexico, Mr. Eck combined Johnny Rotten and Johnny Cash. Now, he is building software that does much the same thing. Using neural networks, he and his team are crossbreeding sounds from very different instruments - say, a bassoon and a clavichord - creating instruments capable of producing sounds no one has ever heard.

Much as a neural network can learn to identify a cat by analyzing hundreds of cat photos, it can learn the musical characteristics of a bassoon by analyzing hundreds of notes. It creates a mathematical representation, or vector, that identifies a bassoon. So, Mr. Eck and his team have fed notes from hundreds of instruments into a neural network, building a vector for each one. Now, simply by moving a button across a screen, they can combine these vectors to create new instruments. One may be 47 percent bassoon and 53 percent clavichord. Another might switch the percentages. And so on.

For centuries, orchestral conductors have layered sounds from various instruments atop one other. But this is different. Rather than layering sounds, Mr. Eck and his team are combining them to form something that didn't exist before, creating new ways that artists can work. "We're making the next film camera," Mr. Eck said. "We're making the next electric guitar."

Called NSynth, this particular project is only just getting off the ground. But across the worlds of both art and technology, many are already developing an appetite for building new art through neural networks and other A.I. techniques. "This work has exploded over the last few years," said Adam Ferris, a photographer and artist in Los Angeles. "This is a totally new aesthetic."

In 2015, a separate team of researchers inside Google created Deep Dream, a tool that uses neural networks to generate haunting, hallucinogenic imagescapes from existing photography, and this has spawned new art inside Google and out. If the tool analyzes a photo of a dog and finds a bit of fur that looks vaguely like an eyeball, it will enhance that bit of fur and then repeat the process. The result is a dog covered in swirling eyeballs.

At the same time, a number of artists - like the well-known multimedia performance artist Trevor Paglen or the lesser-known Adam Ferris - are exploring neural networks in other ways. In January, Mr. Paglen gave a performance in an old maritime warehouse in San Francisco that explored the ethics of computer vision through neural networks that can track the way we look and move. While members of the avant-garde Kronos Quartet played onstage, for example, neural networks analyzed their expressions in real time, guessing at their emotions.

The tools are new, but the attitude is not. Allison Parrish, a New York University professor who builds software that generates poetry, points out that artists have been using computers to generate art since the 1950s. "Much like as Jackson Pollock figured out a new way to paint by just opening the paint can and splashing it on the canvas beneath him," she said, "these new computational techniques create a broader palette for artists."

A year ago, David Ha was a trader with Goldman Sachs in Tokyo. During his lunch breaks he started toying with neural networks and posting the results to a blog under a pseudonym. Among other things, he built a neural network that learned to write its own Kanji, the logographic Chinese characters that are not so much written as drawn.

Soon, Mr. Eck and other Googlers spotted the blog, and now Mr. Ha is a researcher with Google Magenta. Through a project called SketchRNN, he is building neural networks that can draw. By analyzing thousands of digital sketches made by ordinary people, these neural networks can learn to make images of things like pigs, trucks, boats or yoga poses. They don't copy what people have drawn. They learn to draw on their own, to mathematically identify what a pig drawing looks like.

Then, you ask them to, say, draw a pig with a cat's head, or to visually subtract a foot from a horse or sketch a truck that looks like a dog or build a boat from a few random squiggly lines. Next to NSynth or DeepDream, these may seem less like tools that artists will use to build new works. But if you play with them, you realize that they are themselves art, living works built by Mr. Ha. A.I. isn't just creating new kinds of art; it's creating new kinds of artists.

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How A.I. Is Creating Building Blocks to Reshape Music and Art

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As Mr. Eck says, these systems are at least approaching the point - still many, many years away - when a machine can instantly build a new Beatles song or perhaps trillions of new Beatles songs, each sounding a lot like the music the Beatles themselves recorded, but also a little different.

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FLORENCE, N.J. - Nissa Scott started working at the cavernous Amazon warehouse in southern New Jersey late last year, stacking plastic bins the size of small ottomans. It was not, she says, the most stimulating activity. And lifting the bins, which often weigh 25 pounds each, was also tiring over 10-hour shifts.

Now Ms. Scott, 21, watches her replacement - a giant, bright yellow mechanical arm - do the stacking.

Her new job at Amazon is to babysit several robots at a time, troubleshooting them when necessary and making sure they have bins to load. On a recent afternoon, a claw at end of the arm grabbed a bin off a conveyor belt and stacked it on another bin, forming neat columns on wooden pallets surrounding the robot. It was the first time Amazon had shown the arm, the latest generation of robots in use at its warehouses, to a reporter.

"For me, it's the most mentally challenging thing we have here," Ms. Scott said of her new job. "It's not repetitive."

Perhaps no company embodies the anxieties and hopes around automation better than Amazon. Many people, including President Trump, blame the company for destroying traditional retail jobs by enticing people to shop online. At the same time, the company's eye-popping growth has turned it into a hiring machine, with an unquenchable need for entry-level warehouse workers to satisfy customer orders.

Amazon's global work force is three times larger than Microsoft's and 18 times larger than Facebook's, and last week, Amazon said it would open a second headquarters in North America with up to 50,000 new jobs.

Complicating the equation even more, Amazon is also on the forefront of automation, finding new ways of getting robots to do the work once handled by employees. In 2014, the company began rolling out robots to its warehouses using machines originally developed by Kiva Systems, a company Amazon bought for $775 million two years earlier and renamed Amazon Robotics. Amazon now has more than 100,000 robots in action around the world, and it has plans to add many more to the mix.

The robots make warehouse work less tedious and physically taxing, while also enabling the kinds of efficiency gains that let a customer order dental floss after breakfast and receive it before dinner.

"It's certainly true that Amazon would not be able to operate at the costs they have and the costs they provide customers without this automation," said Martin Ford, a futurist and author of "Rise of the Robots," a book about automation. "Maybe we wouldn't be getting two-day shipping."

The dynamics between people and machines play out on a daily basis on the floor of Amazon warehouses in places like Florence, N.J., and Kent, Wash. In Kent, the robots vaguely resemble giant beetles and scurry around with vertical shelves loaded with merchandise weighing up to 3,000 pounds on their backs. Hundreds of them move autonomously inside a large caged area, tailgating each other but not colliding.

On one edge of the cage, a group of human workers - the "stowers" - stuff products onto the shelves, replenishing their inventory. The robots whisk those shelves away and when a customer order arrives for products stored on their backs, they queue up at stations on another edge of the cage like cars waiting to go through a toll both.

There, human "pickers" follow instructions on computer screens, grabbing items off the shelves and putting them in plastic bins, which then disappear on conveyor belts destined for "packers," people who put the products in cardboard boxes bound for customers.

Dave Clark, the top executive in charge of operations at Amazon, said the company wanted the machines to perform the most monotonous tasks, leaving people to do jobs that engage them mentally.

"It's a new item each time," Mr. Clark said. "You're finding something, you're inspecting things, you're engaging your mind in a way that I think is important."

The robots also cut down on the walking required of workers, making Amazon pickers more efficient and less tired. The robots also allow Amazon to pack shelves together like cars in rush-hour traffic, because they no longer need aisle space for humans. The greater density of shelf space means more inventory under one roof, which means better selection for customers.

The Amazon warehouse in Florence shows the latest example of the kinds of jobs machines can do better than people. Eight mechanical arms are in operation at the facility, a warehouse where large quantities of merchandise are broken down into smaller units and distributed to Amazon fulfillment centers across the country.

The arms go by the awkward name of robotic palletizers, but workers have given them a dash of personality, sticking signs on each one naming them after Stuart, Dave and other minion characters from the "Despicable Me" movies. Unlike the warehouse robots in Kent, which were based on the machines Amazon got through its Kiva acquisition, these arms come from an outside company.

Amazon began installing them late last year, not long after it opened the warehouse in Florence. The robot arm is configured to pick up only bins of a standard size, not objects of other dimensions. In a demonstration of future possibilities, Amazon showed a virtual reality simulation used to prototype new robot concepts, including an arm with a forklift attachment that moved pallets.

When Amazon installed the robots, some people who had stacked bins before, like Ms. Scott, took courses at the company to become robot operators. Many others moved to receiving stations, where they manually sort big boxes of merchandise into bins. No people were laid off when the robots were installed, and Amazon found new roles for the displaced workers, Mr. Clark said.

"The people didn't go anywhere," he said.

The question going forward is: What happens when the future generations of robots arrive?

For now, there are warehouse tasks - for example, picking individual items off shelves, with all their various shapes and sizes - where people outperform robots. Amazon has added 80,000 warehouse employees in the United States since adding the Kiva robots, for a total of more than 125,000 warehouse employees. And it says the warehouse hiring spree will continue.

But start-ups and researchers are scrambling to overcome the many remaining technical obstacles. Amazon even sponsors an annual contest to encourage more innovation in the category.

Mr. Ford, the author, believes it is just a matter of time before the employment picture in Amazon's warehouses changes.

"My assumption is this technology will eventually displace a lot of people in those warehouses," Mr. Ford said. "I would not say that overnight huge numbers of jobs disappear. Maybe the first indication is they don't get rid of those people but the pace of job creation slows down."

Amazon's Mr. Clark said history showed that automation increases productivity and, in some cases, demand from consumers, which ultimately creates more jobs. He said warehouse workers would continue to work in technologically rich environments.

"It's a myth that automation destroys net job growth," he said.

In the case of the Florence facility, it opened up the new opportunity for Ms. Scott.

At one point, one of the arms knocked over a tote, sending a dozen or so cone-shaped plastic coffee filters skidding across the ground. Ms. Scott hit a button that froze the arm so she could safely pick up the mess.

Then the arms started working again.

"The robot will work the same all day long," said Edward Cohoon, who supervises Ms. Scott and other Amazon workers as they tend to individual robots. "Their stomachs don't grumble."

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As Amazon Pushes Forward With Robots, Workers Find New Roles

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FLORENCE, N.J. - Nissa Scott started working at the cavernous Amazon warehouse in southern New Jersey late last year, stacking plastic bins the size of small ottomans. It was not, she says, the most stimulating activity. And lifting the bins, which often weigh 25 pounds each, was also tiring over 10-hour shifts.

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https://www.nytimes.com/2017/09/10/business/warehouse-robots-learning.html?action=click&contentCollection=Technology&module=RelatedCoverage&region=Marginalia&pgtype=article

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"We're learning from simulated models and then applying that to real work," said Ken Goldberg, the Berkeley professor who oversees the university's automation lab.

The robot was far from perfect, and it could be several years before it is seen outside research labs. Though it was equipped with a suction cup or a parallel gripper - a kind of two-fingered hand - it could reliably handle only so many items. And it could not switch between the cup and the gripper on the fly. But the techniques used to train it represented a fundamental shift in robotics research, a shift that could overhaul not just Amazon's warehouses but entire industries.

Rather than trying to program behavior into their robot - a painstaking task - Mr. Mahler and his team gave it a way of learning tasks on its own. Researchers at places like Northeastern University, Carnegie Mellon University, Google and OpenAI - the artificial intelligence lab founded by Tesla's chief executive, Elon Musk - are developing similar techniques, and many believe that such machine learning will ultimately allow robots to master a much wider array of tasks, including manufacturing.

"This can extend to tasks of assembly and more complex operations," said Juan Aparicio, head of advanced manufacturing automation at the German industrial giant Siemens, which is helping to fund the research at Berkeley. "That is the road map."

Physically, the Berkeley robot was nothing new. Mr. Mahler and his team were using existing hardware, including two robotic arms from the Swiss multinational ABB and a camera that captured depth.

What was different was the software. It demonstrated a new use for what are called neural networks. Loosely based on the network of neurons in the human brain, a neural network is a complex algorithm that can learn tasks by analyzing vast amounts of data. By looking for patterns in thousands of dog photos, for instance, a neural network can learn to recognize a dog.

Over the past five years, these algorithms have radically changed the way the internet's largest companies build their online services, accelerating the development of everything from image and speech recognition to internet search. But they can also accelerate the development of robotics.

The Berkeley team began by scouring the internet for CAD models, short for computer-aided design. These are digital representations of physical objects. Engineers, physicists and designers build them when running experiments or creating new products. Using these models, Mr. Mahler and his team generated many more digital objects, eventually building a database of more than seven million items. Then they simulated the physics of each item, showing the precise point where a robotic arm should pick it up.

That was a large task, but the process was mostly automated. When the team fed these models into a neural network, it learned to identify a similar point on potentially any digital object with any shape. And when the team plugged this neural network into the two-armed robot, it could do the same with physical objects.

When facing a single everyday object with cylindrical or at least partly planar surfaces - like a spatula, a stapler, a cylindrical container of Froot Loops or even a tube of toothpaste - it could typically pick it up, with success rates often above 90 percent. But percentages dropped with more complex shapes, like the toy shark.

What's more, when the team built simulated piles of random objects and fed those into the neural network, it could learn to lift items from physical piles, too. Researchers at Brown University and Northeastern are exploring similar research, and the hope is that this kind of work can be combined with other methods.

Like Siemens and the Toyota Research Institute, Amazon is helping to fund the work at Berkeley, and it has an acute need for this kind of robot. For the past three years, the company has run a contest in which researchers seek to solve the "pick and place" problem. But the promise of machine-learning methods like the one used at Berkeley is that they can eventually extend to so many other areas, including manufacturing and home robotics.

"Picking an object up is the first thing you want a manipulator robot to do," said Stefanie Tellex, a professor at Brown. "A lot of more sophisticated behavior begins with that. If you can't pick it up, game over."

The research demonstrated how a task learned in the digital world can be transferred to the physical. Since the camera on Berkeley's robot could see depth, it captured three-dimensional images that were not unlike the CAD models the team uses to train its neural network.

Other researchers are developing ways for robots to learn directly from physical experience. For example, at Google, using an algorithmic technique called reinforcement learning, robots are training themselves to open doors through extreme trial and error. But this kind of physical training is both time consuming and expensive. Digital training is more efficient.

For this reason, some organizations are hoping to train robots using complex virtual worlds - digital recreations of our physical environment. If a system can train itself to navigate a car racing game like Grand Theft Auto, the thinking goes, it can navigate real roads.

This is still largely theory. But at places like Berkeley and Northeastern, researchers are showing that digital learning can indeed make the leap into the real world.

"This is a challenge," said Rob Platt, a professor at Northeastern. "But it's a challenge we're dealing with."

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In the Future, Warehouse Robots Will Learn on Their Own

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"We're learning from simulated models and then applying that to real work," said Ken Goldberg, the Berkeley professor who oversees the university's automation lab. The robot was far from perfect, and it could be several years before it is seen outside research labs.

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The cutting edge of artificial intelligence research is based on a set of mathematical techniques called deep neural networks. These networks are mathematical algorithms that can learn tasks on their own by analyzing data. By looking for patterns in millions of dog photos, for example, a neural network can learn to recognize a dog. This mathematical idea dates back to the 1950s, but it remained on the fringes of academia and industry until about five years ago.

By 2013, Google, Facebook and a few other companies started to recruit the relatively few researchers who specialized in these techniques. Neural networks now help recognize faces in photos posted to Facebook, identify commands spoken into living-room digital assistants like the Amazon Echo and instantly translate foreign languages on Microsoft's Skype phone service.

Using the same mathematical techniques, researchers are improving self-driving cars and developing hospital services that can identify illness and disease in medical scans, digital assistants that can not only recognize spoken words but understand them, automated stock-trading systems and robots that pick up objects they've never seen before.

With so few A.I. specialists available, big tech companies are also hiring the best and brightest of academia. In the process, they are limiting the number of professors who can teach the technology.

Uber hired 40 people from Carnegie Mellon's groundbreaking A.I. program in 2015 to work on its self-driving-car project. Over the last several years, four of the best-known A.I. researchers in academia have left or taken leave from their professorships at Stanford University. At the University of Washington, six of 20 artificial intelligence professors are now on leave or partial leave and working for outside companies.

"There is a giant sucking sound of academics going into industry," said Oren Etzioni, who is on leave from his position as a professor at the University of Washington to oversee the nonprofit Allen Institute for Artificial Intelligence.

Some professors are finding a way to compromise. Luke Zettlemoyer of the University of Washington turned down a position at a Google-run Seattle laboratory that he said would have paid him more than three times his current salary (about $180,000, according to public records). Instead, he chose a post at the Allen Institute that allowed him to continue teaching.

"There are plenty of faculty that do this, splitting their time in various percentages between industry and academia," Mr. Zettlemoyer said. "The salaries are so much higher in industry, people only do this because they really care about being an academian."

To bring in new A.I. engineers, companies like Google and Facebook are running classes that aim to teach "deep learning" and related techniques to existing employees. And nonprofits like Fast.ai and companies like Deeplearning.ai, founded by a former Stanford professor who helped create the Google Brain lab, offer online courses.

The basic concepts of deep learning are not hard to grasp, requiring little more than high-school-level math. But real expertise requires more significant math and an intuitive talent that some call "a dark art." Specific knowledge is needed for fields like self-driving cars, robotics and health care.

In order to keep pace, smaller companies are looking for talent in unusual places. Some are hiring physicists and astronomers who have the necessary math skills. Other start-ups from the United States are looking for workers in Asia, Eastern Europe and other locations where wages are lower.

"I can't compete with Google, and I don't want to," said Chris Nicholson, the chief executive and a co-founder of Skymind, a start-up in San Francisco that has hired engineers in eight countries. "So I offer very attractive salaries in countries that undervalue engineering talent."

But the industry's giants are doing much the same. Google, Facebook, Microsoft and others have opened A.I. labs in Toronto and Montreal, where much of this research outside the United States is being done. Google also is hiring in China, where Microsoft has long had a strong presence.

Not surprisingly, many think the talent shortage won't be alleviated for years.

"Of course demand outweighs supply. And things are not getting better any time soon," Yoshua Bengio, a professor at the University of Montreal and a prominent A.I. researcher, said. "It takes many years to train a Ph.D."

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Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent

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The cutting edge of artificial intelligence research is based on a set of mathematical techniques called deep neural networks. These networks are mathematical algorithms that can learn tasks on their own by analyzing data. By looking for patterns in millions of dog photos, for example, a neural network can learn to recognize a dog.

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http://www.businesstimes.com.sg/government-economy/shared-academic-research-and-industry-platform-boosts-singapores-ai-agenda

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AT least 400 researchers and developers will undergo artificial intelligence (AI) training with the signing of a new agreement between chipmaker NVIDIA and Singapore's National Supercomputing Centre (NSCC).

The agreement will also see the setting up of the first shared AI platform to bolster capabilities among academic, research and industry stakeholders. This is in line with AI Singapore (AISG), a national programme set up in May to drive AI adoption, research and innovation in Singapore.

Called AI.Platform@NSCC, the platform will provide AI training, technical expertise and computing services to AISG, which brings together all Singapore-based research and tertiary institutions, including the four local universities and research institutions in the Agency for Science, Technology and Research (A*Star).

The platform will be launched early next year and be located at NSCC's Data Centre in Connexis Building, Fusionopolis. For training and research, it will be equipped with six deep learning supercomputers that can provide up to 99 times more throughput than standard CPUs (central processing units). This will allow researchers to "dramatically reduce the time to train larger, more sophisticated deep neural networks to meet the computing demands of AISG", NSCC and NVIDIA said in a joint release on Tuesday.

"AISG aims to deliver 100 meaningful AI projects and proofs of concept to solve real-world problems swiftly. As a complementary effort, the training of 400 AI researchers and developers will certainly expedite the realisation of AISG's goals," said Ho Teck Hua, AISG executive chairman and NUS's deputy president (Research & Technology) and Tan Chin Tuan centennial professor.

Separately, to garner a strong pool of AI talent in Singapore, NVIDIA has also launched a talents programme in collaboration with the Singapore Economic Development Board (EDB).

Under the EDB-NVIDIA Future Talents Program, 30 postgraduate scholarships will be awarded to citizens or permanent residents of Singapore to study neural networks, machine learning and architecture development for graphics processing units (GPUs).

Together with local universities, NVIDIA will mentor the Master's and PhD candidates to develop industry-specific AI skills, the company said.

EDB's managing director Chng Kai Fong said: "Electronics enables the adoption of AI in applications such as autonomous vehicles, advanced manufacturing and fintech."

He added: "Singapore's strong electronics ecosystem brings us closer to our vision to be a Smart Nation. The EDB-NVIDIA Future Talents Program will help us to build the next generation of AI talent in Singapore, and strengthen our position as a hub for AI development and adoption."

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Shared academic, research and industry platform boosts Singapore's AI agenda

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THE BUSINESS TIMES Government & Economy - AT least 400 researchers and developers will undergo artificial intelligence (AI) training with the signing of a new agreement between chipmaker NVIDIA and Singapore's National Supercomputing Centre (NSCC). . Read more at The Business Times.

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http://www.straitstimes.com/opinion/banks-face-up-to-impact-of-tech-disruption

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A top executive at a major Wall Street bank is deep into his spiel on how artificial intelligence (AI) will make the firm smarter and leaner when he pauses to take a question: What does this mean for young people entering the business?

The silence grows."It's, um," he says, shifting tone and making clear he can't speak publicly. That question is gnawing on him, he confides, because he has kids. "I would want them to pick their careers very carefully. I think AI is going to eliminate most jobs. That's a private view. I think we're just starting to feel that."

Within the upper echelons of many financial firms, there's a lot of soul searching as executives prepare to roll out a new generation of technology. Publicly, they are upbeat, predicting machines will perform almost all repetitive tasks, freeing humans to focus on more valuable pursuits.

Privately, many confide to peers, consultants and sometimes journalists that they are worried about what will happen to their staff - and what to tell them.

There's also uncertainty. Maybe it's all overblown, executives say, because the tech will be hard to implement and humans will find new roles. Or perhaps it's the beginning of the end for legions of professionals in one of the world's most lucrative fields. Can jobs held by office-dwelling millionaires disappear like those on factory floors?

The result is that employees aren't getting a clear message on what's to come.

For a rosy scenario, look to McKinsey & Co.

Early adopters like Goldman Sachs are working with tech, including machine-learning software that improves itself by searching data for patterns. PHOTO: REUTERS

In July, the consulting firm published a report estimating machines are ready to assume roughly a third of the work now performed by banks' rank and file. The authors framed it as positive: People will have more time to tend to clients, conduct research or brainstorm ideas. So far, it noted, firms at the forefront aren't slashing jobs.

At JPMorgan Chase, one of the most tech-savvy banks, chief executive officer Jamie Dimon predicted in June that his workforce will more likely grow than shrink over the next 20 years. Technology may displace workers, he has said, but it also creates opportunities.

30%
Percentage of UBS Group employees who could be shed over a decade, according to the bank's chief executive.
>100
Number of finance firms IPsoft chief executive Chetan Dube is working with to deploy his company's software. "Time, tide and technology will wait for no one," he said.

Yet in interviews, about a dozen Wall Street executives and consultants responsible for deploying technologies - and steeped in their capabilities - were more bearish on humans. Machines will take over task after task, they said, and banks simply won't need nearly as many people.

It's time for senior managers to stop sugarcoating, said Mr Simon Moss, who has been advising banks and investing firms as head of Grant Thornton's fintech and innovation practice for the industry."Are there positions in financial services that are actually untouchable from technology? The simple answer is 'no'," Mr Moss said. "It's just a case of when."

Early adopters like JPMorgan, Goldman Sachs and Bank of New York Mellon Corp are experimenting, he said. They are working with tech, including machine-learning software that improves itself by searching data for patterns, and natural language processing, which helps computers comprehend human speech.

Once firms figure out how to deploy those, rivals may quickly follow suit. Humans will have less to do, and banks will whittle costs by shrinking headcount."Sitting with excess capacity is not a good business decision," Mr Moss said. Managers should start warning employees that they need to learn more skills to stay on.

That's supported by history. Floor traders who once shouted orders in raucous marble-columned exchanges have seen their numbers dwindle since the 1990s, forcing many to seek new work.

Mr Gary Cohn, who last year stepped down as president of Goldman Sachs, told investors in 2011 that technology had helped the firm slash equities staff by more than half over the previous decade. The trend was similar in currencies.

Few automators set out to kill jobs.

Mr Inder Thukral, co-founder of Kognetics, is making machine-learning algorithms that ease life for investment bankers working around the clock on mergers and acquisitions.

"It's the spirit-crushing work that it's replacing," he said in an interview. Dealmakers should focus on only the most important stuff, he said. "Searching through 6,000 documents is a computer's job, so let it do it."

Ultimately, that means fewer people will be needed, he said. But if planned well, banks may be able to avoid firings through attrition - leaving positions open as they become vacant.

That hope comes up a lot.

At UBS Group, executives are using a digital employee known as Amelia to quickly perform repetitive tasks in the firm's investment bank and asset management divisions. Executives say employees shouldn't fret as the software, developed by digital labour company IPsoft, spreads across operations.

"The first question at mind is 'What happens to my job?'," said Mr Tom DeCarlo, a managing director who sits on the bank's innovation committee. But dismissals aren't his plan. "As we have attrition - internal or external - I'm just not replacing those people."

Mr Sergio Ermotti, the bank's CEO, said in a recent interview that the firm could shed 30 per cent of its employees over a decade.

Another senior UBS executive, asking not to be quoted by name, estimated that technology would allow it to operate with up to 40 per cent fewer people in just four to eight years.

Attrition has limits. Some employees may retire, while others quit to take more promising jobs elsewhere. But if the whole industry is shrinking, those opportunities may be scarce.

There's no denying that fewer people will be needed, said Mr Richard Johnson, vice-president of market structure and technology at Greenwich Associates. At least, the transformation will be incremental. "It's not as if everyone is going to come to work and there's a bunch of robots sitting in their seats," he said.

But eventually, a transformation is coming, said IPsoft CEO Chetan Dube, who is working with UBS and more than 100 other finance firms to deploy his company's software. "Time, tide and technology will wait for no one," he said. "We've tippy-toed around this topic too much and for too long."

WASHINGTON POST
*Bloomberg's Stephen Morris contributed to this article.

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Banks face up to impact of tech disruption

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A top executive at a major Wall Street bank is deep into his spiel on how artificial intelligence (AI) will make the firm smarter and leaner when he pauses to take a question: What does this mean for young people entering the business?. Read more at straitstimes.com.

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https://www.pwc.com/gx/en/industries/financial-services/publications/financial-services-technology-2020-and-beyond-embracing-disruption.html

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Financial services technology 2020 and beyond: Embracing disruption

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The financial services industry has seen drastic technology-led changes over the past few years and customers have had their expectations set by other industries.

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https://www.washingtonpost.com/business/get-there/uber-just-the-latest-tech-firm-to-disrupt-an-industry-with-forward-thinking/2015/01/29/db40e478-a811-11e4-a7c2-03d37af98440_story.html?utm_term=.50692ce765b5

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There are many lessons to be learned from Uber, the taxi- and car-hailing start-up that came out of nowhere and is valued at $41 billion. Less than three years ago, Uber had zero drivers. Now it has more than 160,000 active drivers who have collected $656.8 million in fares (net of what they pay Uber).

Among the lessons, some point to the rise of the sharing economy, which also includes firms such as Airbnb, Snapgoods, RelayRides, TaskRabbit and Lending Club. Others talk about the "on-demand economy," which creates a new class of labor that straddles the line between being self-employment and working for a firm.

I prefer a Big Picture view to get the proper perspective on these start-ups. From 30,000 feet, we see what all of these newcomers have in common: They attack an existing market dominated by entrenched incumbents that are inefficient, expensive or both.

Consider Uber. How are the cabs in your city? In Manhattan, where I work, they are rather awful. They are uncomfortable and not especially safe (who wants to slam his face into a plexiglass wall covered with metal projections?). As bad as they are, they are typically unavailable when you need one. The second it begins to rain, it is nearly impossible to find one. And what idiot decided to do shift changes at 5 p.m. - right at the start of rush hour, when swarms of riders need cars, all of whom are unavailable as they are returning to the outer boroughs for their daily change of drivers?

But the biggest inefficiency is the limit on the total number of cabs, as mandated by Taxi and Limousine Commission rules. Hence, that monopoly supply limitation thwarted competition, reduced the available number of cars and allowed the value of medallions to skyrocket. The cabs are dirty and ugly, and the service is awful, but at least they are expensive and unavailable when you need one!

Until Uber rolled in. Since then, the value of a medallion has fallen substantially. The same is true in other cities where Uber operates.

We can credit (or blame) a number of factors. Companies like Uber and Lyft are more convenient, they are cost-competitive (especially low-cost Uber X) and the cars are nicer (especially Uber Black Car). But the biggest factor is that these firms have identified economic inefficiencies in major markets. They are bringing new efficiencies to underserved consumers.

How does this happen?

To begin with, the existing companies have become fat and lazy. That's what the term "entrenched incumbents" means - they are here already, and they usually have some moat around their business to prevent true competition.

In New York, the former lack of real competition allowed taxis to extract excessive charges, regardless of the poor service.

Uber broke that monopoly. In doing so, it brought true competition to the market for car services. That is why Uber is worth a fortune.

What other industries are ripe for disruption? All of the following have some form of restriction which limits supply and reduces competition, thereby keeping prices high even when providing poor service.

Credit transactions: How is it that every time a consumer uses a credit card, the retailer pays a 3 percent (or more) transaction fee to credit-card companies? Most of the new transaction processors - whether it's Apple Pay, Square or PayPal - still process the back end through the major credit-card firms. This area is long overdue for a new competitor that will be cheaper to the retailer (and, therefore, to the consumer) and more convenient to the shopper.

Mortgages: The way we finance homes in this country is slow, filled with middlemen, who run a nonstandardized evaluation process. This makes financing a home cumbersome and difficult. Whoever figures out how to replace this inefficient process stands to make a fortune in residential real estate. The same is true for commercial loans.

Medicine: There is a shortage of doctors, and the American Medical Association is aiming to keep it that way. According to the World Bank, the United States has 2.4 physicians per 1,000 people, putting us way down the list of developed nations. We are behind such emerging nations as Croatia, Moldova, Macedonia, Jordan, Slovenia and Uzbekistan. Germany and Israel have 3.7 physicians per 1,000 people, while Greece (6.2) and Cuba (6.7) leave us in the dust. The Kaiser Foundation has a similar ranking, in physicians per 10,000 people, and the United States ranks 53rd. That is abysmal. If we finished 53rd in the Olympic medal race, there would be an outcry. Yet the very real doctor shortage hardly is discussed. If it takes you a long time to get a doctor's appointment and costs a lot of money, well, now you know why.

Groceries: Shopping for food can be a bothersome, time-consuming chore. Firms like Peapod and Fresh Direct are seeking to fix that, delivering groceries to your home. How long will it be before the giant real-estate-consuming footprint of the suburban supermarket is a thing of the past?

Asset management: Increases in technology are starting to have an impact on this industry. Robo-advisers (Wealthfront, Liftoff, Betterment, Private Capital, et cetera) have a tiny percentage of total assets, but it's growing. And Vanguard just jumped into the field, bringing it heft and credibility.

The old disruption was passive indexers vs. active managers. Here, the entrenched incumbents are especially deep-pocketed and won't go down without a fight.

Real estate: Brokers have enjoyed a 6 percent sales commission for as long as there have been houses to sell. That began to change, and you can credit mobile apps. The technology embedded in apps such as Zillow, RedFin, Trulia and even Google Maps and Mortgagecalculator.org is changing the way we buy homes. It is long overdue.

All of the above sectors are the obvious markets. No one saw taxis as an industry ripe for disruption, and I bet that lots of other markets we hardly even think about are similarly ready for competition. I have no idea which market the next generation of disruptive technology will focus on. Whether it's the college admission process or virtual reality or 3D printing or advanced robotics and drones or autonomous vehicles or next-gen genomics is almost beside the point. The one thing you can be assured of is that no industry is safe from disruption.

That is how progress is made.

Ritholtz is chief investment officer of Ritholtz Wealth Management. He is the author of " Bailout Nation" and runs a finance blog, the Big Picture. On Twitter, @Ritholtz.

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The future of new business is disrupting old business

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There are many lessons to be learned from Uber, the taxi- ­ and car-hailing start-up that came out of nowhere and is valued at $41 billion. Less than three years ago, Uber had zero drivers. Now it has more than 160,000 active drivers who have collected $656.8 million in fares (net of what they pay Uber).

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Snapchat made a big mark on augmented reality by putting digital flower crowns on real people. Now Google may make its own mark by showing you digital information about real flowers.

On Wednesday, the search giant announced a big push into augmented reality, which overlays digital images on what you'd normally see through a camera.

The new technology, announced at the company's I/O developer conference, is called Google Lens. It's a way to use your phone's camera to search for information. For example, point your camera at that flower and Google will tell you what kind it is. Point it at a book, and you get information on the author and see reviews. Ditto with restaurants: You'll be able to see reviews and pricing information on a little digital card that appears above the building on your phone's screen.

With Lens, Google can "understand what you're looking at and help you take action," CEO Sundar Pichai said on stage Wednesday. "We can give you the right information in a meaningful way."

Google is first launching Lens in the Assistant, its digital helper software, and in Google Photos. It will be available at an unspecified date "later this year."

Google Lens marks a big, ambitious attempt by a mainstream company to get into augmented reality in a way we haven't much seen yet. Snapchat, Facebook and Instagram (owned by Facebook) use AR for now to make you laugh and smile with filters like rainbow vomit or Iron Man masks. That stuff is important, but Google is taking a different approach when it comes to AR: utility.

"There are these other aspects of AR that involve creative expression," Aravind Krishnaswamy, head of engineering at Google Photos, said in an interview. "That's not what we're trying to do."

Others are trying to get the AR engine revving. Pinterest has a similar feature also called Lens, but it's mostly focused on buying. Microsoft has long been pushing AR through its HoloLens headset, but there's no consumer product in sight. Because Google is employing its technology in widely used Android phones, it has the scale to reach a massive audience once it launches.

For Google, it's important to expand search beyond its iconic homepage. The world is a lot different than when Larry Page and Sergey Brin started the company as graduate students at Stanford. Search was a thing for desktop computers and typing on a keyboard. Now -- who knows? It's for tapping on a smartphone screen, barking orders at a device in your kitchen or, if Mark Zuckerberg gets his way, eventually sending your thoughts straight to a computer.

Google knows the way of the world is changing, and it's trying to get out in front.

'Get stuff done'

Right now, AR is having a moment. Last September, when Snapchat renamed itself Snap and started calling itself a "camera company," people were baffled. But the young upstart realized the time was right for phone cameras to take advantage of augmented reality. Facebook, sensing that Snapchat was onto something, decided to build the future of its app around smartphone cameras, too. Last month, Zuckerberg announced a new augmented reality platform, which invites outside developers to create AR experiences for Facebook.

The promise of augmented reality is profound. With a good pair of AR glasses, you might be able to tell if your kid has a fever just by looking at him or her. You might never have to buy a TV again if you could just project a big screen over your eyes. And you'll never experience the awkwardness of forgetting someone's name, because it will appear right over their head.

Of course, we're not there yet. But Google thinks it can lead the way because of everything it knows about you and the world. That's where being a search company comes in handy. Google is taking its big trove of data and trying to make it three-dimensional.

"It's our core mission, accessing the world's information," Gummi Hafsteinsson, product lead for the Assistant, said in an interview, citing Google's mission statement. "Images are just another part of that."

What Google has going for it is scale, said Chirag Dekate, a research director at Gartner. Google has a good shot at being accurate with the information it presents in AR because of Google's knowledge about your surroundings. Companies like that have a "distinct data advantage," he said.

To be sure, Facebook and Snapchat have AR plans beyond photo filters. Facebook is working on AR glasses, but that will take years. And Snapchat has its Spectacles glasses, which will likely be a vehicle for more advanced AR. But for now, they are mostly just for taking snaps.

Google's goal is to be helpful. David Lieb, product lead for Google Photos, in an interview summed up the approach: "We're doing this to help you get stuff done."
CNET Magazine: Check out a sampling of the stories you'll find in CNET's newsstand edition, right here. Life, disrupted: In Europe, millions of refugees are still searching for a safe place to settle. Tech should be part of the solution. But is it? CNET investigates.

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Forget rainbow vomit, Google Lens is AR you can actually use

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Snapchat made a big mark on augmented reality by putting digital flower crowns on real people. Now Google may make its own mark by showing you digital information about real flowers. On Wednesday, the search giant announced a big push into augmented reality, which overlays digital images on what you'd normally see through a camera.

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Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive.

This meant taking the wheel of a specially equipped Humvee military vehicle and guiding it through city streets, says Pomerleau, who was then a robotics graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania. With him in the Humvee was a computer that he had programmed to peer through a camera, interpret what was happening out on the road and memorize every move that he made in response. Eventually, Pomerleau hoped, the machine would make enough associations to steer on its own.

On each trip, Pomerleau would train the system for a few minutes, then turn it loose to drive itself. Everything seemed to go well - until one day the Humvee approached a bridge and suddenly swerved to one side. He avoided a crash only by quickly grabbing the wheel and retaking control.

Back in the lab, Pomerleau tried to understand where the computer had gone wrong. "Part of my thesis was to open up the black box and figure out what it was thinking," he explains. But how? He had programmed the computer to act as a 'neural network' - a type of artificial intelligence (AI) that is modelled on the brain, and that promised to be better than standard algorithms at dealing with complex real-world situations. Unfortunately, such networks are also as opaque as the brain. Instead of storing what they have learned in a neat block of digital memory, they diffuse the information in a way that is exceedingly difficult to decipher. Only after extensively testing his software's responses to various visual stimuli did Pomerleau discover the problem: the network had been using grassy roadsides as a guide to the direction of the road, so the appearance of the bridge confused it.

Twenty-five years later, deciphering the black box has become exponentially harder and more urgent. The technology itself has exploded in complexity and application. Pomerleau, who now teaches robotics part-time at Carnegie Mellon, describes his little van-mounted system as "a poor man's version" of the huge neural networks being implemented on today's machines. And the technique of deep learning, in which the networks are trained on vast archives of big data, is finding commercial applications that range from self-driving cars to websites that recommend products on the basis of a user's browsing history.

It promises to become ubiquitous in science, too. Future radio-astronomy observatories will need deep learning to find worthwhile signals in their otherwise unmanageable amounts of data; gravitational-wave detectors will use it to understand and eliminate the tiniest sources of noise; and publishers will use it to scour and tag millions of research papers and books. Eventually, some researchers believe, computers equipped with deep learning may even display imagination and creativity. "You would just throw data at this machine, and it would come back with the laws of nature," says Jean-Roch Vlimant, a physicist at the California Institute of Technology in Pasadena.

But such advances would make the black-box problem all the more acute. Exactly how is the machine finding those worthwhile signals, for example? And how can anyone be sure that it's right? How far should people be willing to trust deep learning? "I think we are definitely losing ground to these algorithms," says roboticist Hod Lipson at Columbia University in New York City. He compares the situation to meeting an intelligent alien species whose eyes have receptors not just for the primary colours red, green and blue, but also for a fourth colour. It would be very difficult for humans to understand how the alien sees the world, and for the alien to explain it to us, he says. Computers will have similar difficulties explaining things to us, he says. "At some point, it's like explaining Shakespeare to a dog."

Faced with such challenges, AI researchers are responding just as Pomerleau did - by opening up the black box and doing the equivalent of neuroscience to understand the networks inside. Answers are not insight, says Vincenzo Innocente, a physicist at CERN, the European particle-physics laboratory near Geneva, Switzerland who has pioneered the application of AI to the field. "As a scientist," he says, "I am not satisfied with just distinguishing cats from dogs. A scientist wants to be able to say: 'the difference is such and such'."

Good trip

The first artificial neural networks were created in the early 1950s, almost as soon as there were computers capable of executing the algorithms. The idea is to simulate small computational units - the 'neurons' - that are arranged in layers connected by a multitude of digital 'synapses' (see 'Do AIs dream of electric sheep?') Each unit in the bottom layer takes in external data, such as pixels in an image, then distributes that information up to some or all of the units in the next layer. Each unit in that second layer then integrates its inputs from the first layer, using a simple mathematical rule, and passes the result further up. Eventually, the top layer yields an answer - by, say, classifying the original picture as a 'cat' or a 'dog'.

The power of such networks stems from their ability to learn. Given a training set of data accompanied by the right answers, they can progressively improve their performance by tweaking the strength of each connection until their top-level outputs are also correct. This process, which simulates how the brain learns by strengthening or weakening synapses, eventually produces a network that can successfully classify new data that were not part of its training set.

That ability to learn was a major attraction for CERN physicists back in the 1990s, when they were among the first to routinely use large-scale neural networks for science: the networks would prove to be an enormous help in reconstructing the trajectories of subatomic shrapnel coming out of particle collisions at CERN's Large Hadron Collider.

But this form of learning is also why information is so diffuse in the network: just as in the brain, memory is encoded in the strength of multiple connections, rather than stored at specific locations, as in a conventional database. "Where is the first digit of your phone number stored in your brain? Probably in a bunch of synapses, probably not too far from the other digits," says Pierre Baldi, a machine-learning researcher at the University of California, Irvine. But there is no well-defined sequence of bits that encodes the number. As a result, says computer scientist Jeff Clune at the University of Wyoming in Laramie, "even though we make these networks, we are no closer to understanding them than we are a human brain".

To scientists who have to deal with big data in their respective disciplines, this makes deep learning a tool to be used with caution. To see why, says Andrea Vedaldi, a computer scientist at the University of Oxford, UK, imagine that in the near future, a deep-learning neural network is trained using old mammograms that have been labelled according to which women went on to develop breast cancer. After this training, says Vedaldi, the tissue of an apparently healthy woman could already 'look' cancerous to the machine. "The neural network could have implicitly learned to recognize markers - features that we don't know about, but that are predictive of cancer," he says.

But if the machine could not explain how it knew, says Vedaldi, it would present physicians and their patients with serious dilemmas. It's difficult enough for a woman to choose a preventive mastectomy because she has a genetic variant known to substantially up the risk of cancer. But it could be even harder to make that choice without even knowing what the risk factor is - even if the machine making the recommendation happened to be very accurate in its predictions.

"The problem is that the knowledge gets baked into the network, rather than into us," says Michael Tyka, a biophysicist and programmer at Google in Seattle, Washington. "Have we really understood anything? Not really - the network has."

Several groups began to look into this black-box problem in 2012. A team led by Geoffrey Hinton, a machine-learning specialist at the University of Toronto in Canada, entered a computer-vision competition and showed for the first time that deep learning's ability to classify photographs from a database of 1.2 million images far surpassed that of any other AI approach.

Digging deeper into how this was possible, Vedaldi's group took algorithms that Hinton had developed to improve neural-network training, and essentially ran them in reverse. Rather than teaching a network to give the correct interpretation of an image, the team started with pretrained networks and tried to reconstruct the images that produced them. This helped the researchers to identify how the machine was representing various features - as if they were asking a hypothetical cancer-spotting neural network, 'What part of this mammogram have you decided is a marker of cancer risk?"

"The problem is that the knowledge gets baked into the network, rather than into us."

Last year, Tyka and fellow Google researchers followed a similar approach to its ultimate conclusion. Their algorithm, which they called Deep Dream, starts from an image - say a flower, or a beach - and modifies it to enhance the response of a particular top-level neuron. If the neuron likes to tag images as birds, for example, the modified picture will start showing birds everywhere. The resulting images evoke LSD trips, with birds emerging from faces, buildings and much more. "I think it's much more like a hallucination" than a dream, says Tyka, who is also an artist. When he and the team saw the potential for others to use the algorithm for creative purposes, they made it available to anyone to download. Within days, Deep Dream was a viral sensation online.

Using techniques that could maximize the response of any neuron, not just the top-level ones, Clune's team discovered in 2014 that the black-box problem might be worse than expected: neural networks are surprisingly easy to fool with images that to people look like random noise, or abstract geometric patterns. For instance, a network might see wiggly lines and classify them as a starfish, or mistake black-and-yellow stripes for a school bus. Moreover, the patterns elicited the same responses in networks that had been trained on different data sets.

Researchers have proposed a number of approaches to solving this 'fooling' problem, but so far no general solution has emerged. And that could be dangerous in the real world. An especially frightening scenario, Clune says, is that ill-intentioned hackers could learn to exploit these weaknesses. They could then send a self-driving car veering into a billboard that it thinks is a road, or trick a retina scanner into giving an intruder access to the White House, thinking that the person is Barack Obama. "We have to roll our sleeves up and do hard science, to make machine learning more robust and more intelligent," concludes Clune.

Issues such as these have led some computer scientists to think that deep learning with neural networks should not be the only game in town. Zoubin Ghahramani, a machine-learning researcher at the University of Cambridge, UK, says that if AI is to give answers that humans can easily interpret, "there's a world of problems for which deep learning is just not the answer". One relatively transparent approach with an ability to do science was debuted in 2009 by Lipson and computational biologist Michael Schmidt, then at Cornell University in Ithaca, New York. Their algorithm, called Eureqa, demonstrated that it could rediscover the laws of Newtonian physics simply by watching a relatively simple mechanical object - a system of pendulums - in motion.

Starting from a random combination of mathematical building blocks such as +, −, sine and cosine, Eureqa follows a trial-and-error method inspired by Darwinian evolution to modify the terms until it arrives at the formulae that best describe the data. It then proposes experiments to test its models. One of its advantages is simplicity, says Lipson. "A model produced by Eureqa usually has a dozen parameters. A neural network has millions."

On autopilot

Last year, Ghahramani published an algorithm that automates the job of a data scientist, from looking at raw data all the way to writing a paper. His software, called Automatic Statistician, spots trends and anomalies in data sets and presents its conclusion, including a detailed explanation of its reasoning. That transparency, Ghahramani says, is "absolutely critical" for applications in science, but it is also important for many commercial applications. For example, he says, in many countries, banks that deny a loan have a legal obligation to say why - something a deep-learning algorithm might not be able to do.

Similar concerns apply to a wide range of institutions, points out Ellie Dobson, director of data science at the big-data firm Arundo Analytics in Oslo. If something were to go wrong as a result of setting the UK interest rates, she says, "the Bank of England can't say, 'the black box made me do it'".

Despite these fears, computer scientists contend that efforts at creating transparent AI should be seen as complementary to deep learning, not as a replacement. Some of the transparent techniques may work well on problems that are already described as a set of abstract facts, they say, but are not as good at perception - the process of extracting facts from raw data.

Ultimately, these researchers argue, the complex answers given by machine learning have to be part of science's toolkit because the real world is complex: for phenomena such as the weather or the stock market, a reductionist, synthetic description might not even exist. "There are things we cannot verbalize," says Stéphane Mallat, an applied mathematician at the École Polytechnique in Paris. "When you ask a medical doctor why he diagnosed this or this, he's going to give you some reasons," he says. "But how come it takes 20 years to make a good doctor? Because the information is just not in books."

To Baldi, scientists should embrace deep learning without being "too anal" about the black box. After all, they all carry a black box in their heads. "You use your brain all the time; you trust your brain all the time; and you have no idea how your brain works."

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Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive.

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Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, and expert device support.

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This year is the 10th anniversary of the iPhone. It's also the 10th anniversary of the Apple keynote at which Steve Jobs announced the original device. The first one was a tour de force, Mr. Jobs leading a rapt audience as he asked them to imagine an iPod, a phone and a pocket internet device all in one.

Mr. Jobs, who died in 2011, loomed over Tuesday's nostalgic presentation. The Apple C.E.O., Tim Cook, paid tribute, his voice cracking with emotion, Mr. Jobs's steeple-fingered image looming as big onstage as Big Brother's face in the classic Macintosh "1984" commercial. Mr. Cook even revived Mr. Jobs's patented "One more thing ..." line, but reverentially: "We have great respect for these words, and we don't use them lightly."

These online-streaming keynotes have become as important a production of Apple as the devices themselves. An Apple event is a distinct kind of TV special: an extended commercial - this one ran nearly two hours - that people watch willingly, to get a glimpse of the new products and an art-directed idea of their better selves.

On the massive screen, attractive young people ran with a billowing red sheet across a golden desert. Caped figures soared through clouds like angels in a gorgeous video game preview. A promo video for the Apple Watch as a fitness tool was so heartwarming and inspirational I think the Apple Watch may be running for president.

Angela Ahrendts, Apple's senior vice president for retail, said new Apple stores would be rebranded Town Squares, places for community gathering, education - and buying the occasional thousand-dollar phone. ("Apple retail," Mr. Cook said, "has always been about more than selling. It's about learning, inspiring and connecting with people.")

You will do such fantastic things with this technology, the presentation said. You'll get healthy! You'll learn! You'll play ennobling games!

Though Mr. Jobs is gone, his minimalist aesthetic lives on in Cupertino, in the products and on the stage. Everything was set off against a black screen, like diamond necklaces in a velvet case. The show opened with footage of the sun rising (or setting? Surely it was rising) over the curvilinear new Steve Jobs Theater, where the event took place.

Mr. Cook is an amiable presenter, but he doesn't pretend to have Mr. Jobs's magnetism. Wearing a zippered fleece - rich-guy casualwear as opposed to Mr. Jobs's aesthete's turtlenecks - he passed the spotlight around to associates. Among them was the designer Jonathan Ive, speaking from a white room in tandem with an animated monkey emoji.

Those "animoji," controlled through face-recognition technology - yes, the poop emoji is in there - got a workout. You can read elsewhere about the technical specs of the new hardware. But so much of the presentation was about images: the cameras, the edge-to-edge displays, the animation, emphasizing the extent to which Apple could be overlaid on the world to make it prettier, better, more fun.

I'm not going to pretend that I'm immune to this allure. I watched the Apple keynote on my Apple iMac after getting a reminder on my Apple iPad and being unable to start a stream through the Apple events app on my Apple TV. A 1989 Mac Plus that I can't bear to part with still sits in my basement. I will almost certainly buy one of the new phones.

What will I do with it? What does anyone? I will Instagram photos of my cooking that I think look more appetizing than they are. I will see another tweet from the president. I will Google song lyrics. I will read Facebook posts and get mad on the internet.

And another year from now, I'll set another reminder to watch another Apple event, believing somewhere deep down that with one more upgrade, I might be perfected.

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At the Apple Keynote, Selling Us a Better Vision of Ourselves

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This year is the 10th anniversary of the iPhone. It's also the 10th anniversary of the Apple keynote at which Steve Jobs announced the original device. The first one was a tour de force, Mr. Jobs leading a rapt audience as he asked them to imagine an iPod, a phone and a pocket internet device all in one.

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For years, the central processing units, or C.P.U.s, that ran PCs and similar devices were where the money was. And there had not been much need for change.

In accordance with Moore's Law, the oft-quoted maxim from Intel co-founder Gordon Moore, the number of transistors on a computer chip had doubled every two years or so, and that provided steadily improved performance for decades. As performance improved, chips consumed about the same amount of power, according to another, lesser-known law of chip design called Dennard scaling, named for the longtime IBM researcher Robert Dennard.

By 2010, however, doubling the number of transistors was taking much longer than Moore's Law predicted. Dennard's scaling maxim had also been upended as chip designers ran into the limits of the physical materials they used to build processors. The result: If a company wanted more computing power, it could not just upgrade its processors. It needed more computers, more space and more electricity.

Researchers in industry and academia were working to extend Moore's Law, exploring entirely new chip materials and design techniques. But Doug Burger, a researcher at Microsoft, had another idea: Rather than rely on the steady evolution of the central processor, as the industry had been doing since the 1960s, why not move some of the load onto specialized chips?

During his Christmas vacation in 2010, Mr. Burger, working with a few other chip researchers inside Microsoft, began exploring new hardware that could accelerate the performance of Bing, the company's internet search engine.

At the time, Microsoft was just beginning to improve Bing using machine-learning algorithms (neural networks are a type of machine learning) that could improve search results by analyzing the way people used the service. Though these algorithms were less demanding than the neural networks that would later remake the internet, existing chips had trouble keeping up.

Mr. Burger and his team explored several options but eventually settled on something called Field Programmable Gate Arrays, or F.P.G.A.s.: chips that could be reprogrammed for new jobs on the fly. Microsoft builds software, like Windows, that runs on an Intel C.P.U. But such software cannot reprogram the chip, since it is hard-wired to perform only certain tasks.

With an F.P.G.A., Microsoft could change the way the chip works. It could program the chip to be really good at executing particular machine learning algorithms. Then, it could reprogram the chip to be really good at running logic that sends the millions and millions of data packets across its computer network. It was the same chip but it behaved in a different way.

Microsoft started to install the chips en masse in 2015. Now, just about every new server loaded into a Microsoft data center includes one of these programmable chips. They help choose the results when you search Bing, and they help Azure, Microsoft's cloud-computing service, shuttle information across its network of underlying machines.

Teaching Computers to Listen

In fall 2016, another team of Microsoft researchers - mirroring the work done by Jeff Dean at Google - built a neural network that could, by one measure at least, recognize spoken words more accurately than the average human could.

Xuedong Huang, a speech-recognition specialist who was born in China, led the effort, and shortly after the team published a paper describing its work, he had dinner in the hills above Palo Alto, Calif., with his old friend Jen-Hsun Huang, (no relation), the chief executive of the chipmaker Nvidia. The men had reason to celebrate, and they toasted with a bottle of champagne.

Xuedong Huang and his fellow Microsoft researchers had trained their speech-recognition service using large numbers of specialty chips supplied by Nvidia, rather than relying heavily on ordinary Intel chips. Their breakthrough would not have been possible had they not made that change.

"We closed the gap with humans in about a year," Microsoft's Mr. Huang said. "If we didn't have the weapon - the infrastructure - it would have taken at least five years."

Because systems that rely on neural networks can learn largely on their own, they can evolve more quickly than traditional services. They are not as reliant on engineers writing endless lines of code that explain how they should behave.

But there is a wrinkle: Training neural networks this way requires extensive trial and error. To create one that is able to recognize words as well as a human can, researchers must train it repeatedly, tweaking the algorithms and improving the training data over and over. At any given time, this process unfolds over hundreds of algorithms. That requires enormous computing power, and if companies like Microsoft use standard-issue chips to do it, the process takes far too long because the chips cannot handle the load and too much electrical power is consumed.

So, the leading internet companies are now training their neural networks with help from another type of chip called a graphics processing unit, or G.P.U. These low-power chips - usually made by Nvidia - were originally designed to render images for games and other software, and they worked hand-in-hand with the chip - usually made by Intel - at the center of a computer. G.P.U.s can process the math required by neural networks far more efficiently than C.P.U.s.

Nvidia is thriving as a result, and it is now selling large numbers of G.P.U.s to the internet giants of the United States and the biggest online companies around the world, in China most notably. The company's quarterly revenue from data center sales tripled to $409 million over the past year.

"This is a little like being right there at the beginning of the internet," Jen-Hsun Huang said in a recent interview. In other words, the tech landscape is changing rapidly, and Nvidia is at the heart of that change.

Creating Specialized Chips

G.P.U.s are the primary vehicles that companies use to teach their neural networks a particular task, but that is only part of the process. Once a neural network is trained for a task, it must perform it, and that requires a different kind of computing power.

After training a speech-recognition algorithm, for example, Microsoft offers it up as an online service, and it actually starts identifying commands that people speak into their smartphones. G.P.U.s are not quite as efficient during this stage of the process. So, many companies are now building chips specifically to do what the other chips have learned.

Google built its own specialty chip, a Tensor Processing Unit, or T.P.U. Nvidia is building a similar chip. And Microsoft has reprogrammed specialized chips from Altera, which was acquired by Intel, so that it too can run neural networks more easily.

Other companies are following suit. Qualcomm, which specializes in chips for smartphones, and a number of start-ups are also working on A.I. chips, hoping to grab their piece of the rapidly expanding market. The tech research firm IDC predicts that revenue from servers equipped with alternative chips will reach $6.8 billion by 2021, about 10 percent of the overall server market.

Across Microsoft's global network of machines, Mr. Burger pointed out, alternative chips are still a relatively modest part of the operation. And Bart Sano, the vice president of engineering who leads hardware and software development for Google's network, said much the same about the chips deployed at its data centers.

Mike Mayberry, who leads Intel Labs, played down the shift toward alternative processors, perhaps because Intel controls more than 90 percent of the data-center market, making it by far the largest seller of traditional chips. He said that if central processors were modified the right way, they could handle new tasks without added help.

But this new breed of silicon is spreading rapidly, and Intel is increasingly a company in conflict with itself. It is in some ways denying that the market is changing, but nonetheless shifting its business to keep up with the change.

Two years ago, Intel spent $16.7 billion to acquire Altera, which builds the programmable chips that Microsoft uses. It was Intel's largest acquisition ever. Last year, the company paid a reported $408 million buying Nervana, a company that was exploring a chip just for executing neural networks. Now, led by the Nervana team, Intel is developing a dedicated chip for training and executing neural networks.

"They have the traditional big-company problem," said Bill Coughran, a partner at the Silicon Valley venture capital firm Sequoia Capital who spent nearly a decade helping to oversee Google's online infrastructure, referring to Intel. "They need to figure out how to move into the new and growing areas without damaging their traditional business."

Intel's internal conflict is most apparent when company officials discuss the decline of Moore's Law. During a recent interview with The New York Times, Naveen Rao, the Nervana founder and now an Intel executive, said Intel could squeeze "a few more years" out of Moore's Law. Officially, the company's position is that improvements in traditional chips will continue well into the next decade.

Mr. Mayberry of Intel also argued that the use of additional chips was not new. In the past, he said, computer makers used separate chips for tasks like processing audio.

But now the scope of the trend is significantly larger. And it is changing the market in new ways. Intel is competing not only with chipmakers like Nvidia and Qualcomm, but also with companies like Google and Microsoft.

Google is designing the second generation of its T.P.U. chips. Later this year, the company said, any business or developer that is a customer of its cloud-computing service will be able to use the new chips to run its software.

While this shift is happening mostly inside the massive data centers that underpin the internet, it is probably a matter of time before it permeates the broader industry.

The hope is that this new breed of mobile chip can help devices handle more, and more complex, tasks on their own, without calling back to distant data centers: phones recognizing spoken commands without accessing the internet; driverless cars recognizing the world around them with a speed and accuracy that is not possible now.

In other words, a driverless car needs cameras and radar and lasers. But it also needs a brain.

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Chips Off the Old Block: Computers Are Taking Design Cues From Human Brains

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For years, the central processing units, or C.P.U.s, that ran PCs and similar devices were where the money was. And there had not been much need for change. In accordance with Moore's Law, the oft-quoted maxim from Intel co-founder Gordon Moore, the number of transistors on a computer chip had doubled every two years or so, and that provided steadily improved performance for decades.

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Future-gazing is an inherently risky enterprise.

Before 1997, who would have thought Asia was headed for a financial crisis? Just three years later, the dot.com bubble burst almost as quickly as it built up. I remember the shock of the Sept 11 attacks the following year, and the terrifying Sars epidemic of 2003.

In more recent times, we've witnessed a global financial crisis, the Arab Spring and the Brexit vote.

None of these events in the last two decades had been predicted with any degree of confidence. I therefore approach the subject of preparing for the jobs of 2037 with great humility, starting with what we already know and outlining the broad strategies Singapore will take in response.

WHAT WE KNOW

There are few things as certain as the ageing of the global population and declining birth rates.

Improved longevity is one reason why global population will still grow. But it is also why having more people does not automatically mean an expanded workforce. In fact, the working-age share of population is expected to fall marginally from 65 per cent today. Singapore could expect a sharper decline from 72 per cent to below 60 per cent among the resident population. Except in the developing world, shrinking workforces elsewhere will dampen economic growth.

Another certainty is the relentless pace at which technology will change how we manufacture products, deliver services and create enterprise value. Disruptors are blurring the lines between industries - think ride-hailing platforms offering financial services.

Seven of the top 10 companies by market capitalisation today are already tech companies which did not exist before 1975. No business should believe it can avoid riding the bumpy waves of digitalisation and still stay afloat.

OUTLOOK NOT ALL GLOOMY

No wonder then that an often-asked question is whether robots will put millions of workers out of work. Do disruptive technologies lead inevitably to disrupted livelihoods?

There's no doubt that advances in artificial intelligence and machine learning will increase the prevalence of workplace automation. The impact will be widely felt across the whole spectrum of jobs and not just at the lower end. For instance, natural language processing software can already search massive legal archives more quickly than paralegals.

However, concerns about massive job losses may be overstated as most jobs are made up of many tasks, not all of which can be easily automated.

A recent Organisation for Economic Cooperation and Development study suggests that just about 9 per cent of jobs stood a high chance of being substantially automated, far lower than previous estimates. For the vast majority of jobs, human beings will still be needed, although the use of technology will intensify.

Even as routine tasks are automated, technology can have positive "knock-on effects". For example, the introduction of ATMs reduced the number of bank tellers needed and also brought operational costs down, thus allowing more branches to be opened to meet customer demand and creating more work for sales and customer service personnel.

If the past is any guide to the future, technology has often resulted in a net creation of jobs. The wave of mechanisation that hit industrialising economies in the late 19th century also ushered in a new era of job creation, from machine operators and technicians to railways and electrical workers.

In more recent times, new job roles such as digital marketers and cyber -security specialists have emerged. E-commerce may have displaced retail workers in bricks-and-mortar stores, but it also created many more jobs in supply-chain management.

There is a catch, though. The net addition of jobs available is comforting only to the extent that displaced workers can find ways to access the new opportunities. Otherwise, we will have an unhappy situation of rising unemployment coupled with below-potential growth for businesses.

TRANSFORM, ADAPT

For Singapore, two other factors shape our response to the potential impact of technology.

First, the imminent slowdown of workforce growth as a result of smaller cohorts coming of age, "baby boomers" retiring, an already-high labour force participation rate and moderated inflows of foreign manpower. Second, there is a risk productivity and wage growth will stagnate, especially in domestically oriented sectors.

Against this backdrop, we have a real interest in helping businesses be more productive and manpower-lean. It is essential to their continued success in Singapore and ability to provide better jobs. Far from cold-shouldering technology and innovation, we welcome their potential to uplift productivity for our businesses and help sustain wage growth for our people.

This is why Singapore's approach is to embrace technology and innovation. Our focus is to support businesses as they transform, and enable our citizens to adapt. It is an activist approach with initiatives relevant to both businesses and individuals.

More than 20 Industry Transformation Maps (ITMs) have been charted together with stakeholders, each encompassing specific plans to promote productivity and innovation with a special focus on digitalisation. These ITMs also identify the skills gaps measures to future-proof the workforce.

Through the SkillsFuture initiative, jobseekers and incumbents in every sector have a customised framework to guide them as they advance in their careers.

To help individuals transit to new jobs and industries, wage and training support are available.

In the first half of this year, well over 10,000 workers were placed by Workforce Singapore and NTUC's Employment and Employability Institute through the Adapt and Grow initiative.

Five growth sectors offer considerable potential for experienced professionals, managers, executives and technicians (PMETs). They are healthcare, financial and insurance services, infocomms technology, professional services and wholesale trade.

Collectively, they will account for half of the 25,000 to 40,000 new jobs created annually for the next few years.

Many of these are suitable for PMETs with valuable and hard-to-automate skills, such as interfacing with customers and suppliers, complex decision-making, managing and developing people.

SENSE OF SOLIDARITY

I've outlined Singapore's strategies for preparing our businesses and people for jobs of the future. The key lies in implementation. We also need to be nimble and keep evolving our specific measures.

I started by saying that we need a great sense of humility when talking about the future. As I bring my remarks to a close, I would like to suggest that we also need a generous dose of empathy, for the businesses and individuals who will inevitably be stressed by the need to transform and adapt.

It would help if we acknowledged the uncertainties even as we enthuse about the opportunities. But perhaps more importantly, we need to cultivate a sense of solidarity that we're in this together.

It can't be about businesses winning with technology and innovation at the expense of their people. Neither should it be about people winning more protection against disruption at the expense of new and more efficient business models.

It is about both businesses and people finding new ways forward and winning together. Only then can society maintain a sense of solidarity and sustain a culture that embraces technology and innovation. Only then can we have a future of jobs worth looking forward to.

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How Singapore is preparing for jobs of the future

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Future-gazing is an inherently risky enterprise. Before 1997, who would have thought Asia was headed for a financial crisis? Just three years later, the dot.com bubble burst almost as quickly as it built up. I remember the shock of the Sept 11 attacks the following year, and the terrifying Sars epidemic of 2003.

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For some, the spread of artificial intelligence and robotics poses a threat to our privacy, our jobs - even our safety, as more and more tasks are handed over to silicon-based brains.

But even the most vocal critics highlight the potential good that AI and automated systems could do for humanity. As part of BBC Future Now's Grand Challenges, a panel of experts recently described how they saw our world changing as the machines we use grow smarter.

Now, in our Grand Ideas series, BBC Future Now has sought out projects where advanced AI and automation is already beginning to tackle some of the world's knottiest, and dangerous, problems, from illnesses to violence.

"We should view AI not as something competing with us, but as something that can amplify our own capabilities," says Takeo Kanade, a professor of robotics at Carnegie Mellon University. This is because AI has a tolerance for tedium, plus an ability to spot patterns - an ability that's far beyond anything humans are capable of.

And it might what will help keep us safe in the 21st Century.
Combating infectious diseases
For billions of people around the world, the buzz of a mosquito past their ear can mean far more than an irritating bite - it can be a harbinger of disease and even death. One species in particular - Aedes aegypti - has spread from Africa into almost all tropical and subtropical regions, carrying Dengue fever, yellow fever, Zika and chikungunya(a virus that causes crippling joint pains) with it. Dengue alone infects an estimated 390 million people every year in 128 countries.

"This mosquito is a tiny demon," says Rainier Mallol, a computer engineer from the Dominican Republic, a Zika hotspot. Together with Dhesi Raja, a medical doctor from Malaysia (another at-risk country for the virus), the pair have created AI algorithms that predict where outbreaks are most likely to happen.

Their Artificial Intelligence in Medical Epidemiology (Aime) is a system that combines the time and location of each new dengue casefrom reports filed by local hospitals with 274 other variable factors - such as wind direction, humidity, temperature, population density, housing type. "These are all factors that determine how the mosquito will spread," explains Mallol.

Trials in Malaysia and Brazil have so far shown it can predict outbreaks with an accuracy of around 88% up to three months in advance. The system can also help to pinpoint the epicentre of an outbreak to within 400 metres, allowing public health officials to intervene early with insecticides and bite protection for locals.

Aime is also being extended to predict Zika and chikungunya outbreaks. And huge tech companies are pursuing their own vision of this grand idea: for example, Microsoft's Project Premonition uses autonomous drones to locate mosquito hotspots and use robotic carbon dioxide and light traps to collect some of the insects. DNA from the mosquitoes, and the animals they have bitten, can then be analysed using machine learning algorithms - software that learns to recognise patterns from large amounts of data, getting better and better each time - to search for pathogens.
Tackling gun violence
There were 15,000 deaths due to firearms in the US last year, and the country has the highest rate of gun-related violence in the developed world. To address what many see as an unrelenting tide of shootings and gun-related crime, a number of cities are now turning to technology to find a solution.

An automated system that listens for the sounds of gunfire with arrays of sensors can be used to pinpoint where gunshots came from and alert the authorities within 45 seconds of the trigger being pulled. The ShotSpotter system uses 15 to 20 acoustic sensors per square mile to detect the distinctive "pop" of a gunshot, using the time it takes to reach each sensor to and uses algorithms to reveal the location to within 25 metres.

Machine learning technology is used to confirm the sound is a gunshot and count the number of them, revealing whether police might be dealing with a lone shooter or multiple perpetrators and if they are using automatic weapons.

There are 90 cities - many in the US but some in South Africa and South America - now using ShotSpotter with others in discussions. Smaller systems have also been deployed on nine college campuses in the US in response to recent campus shootings while the US Secret Service has installed it at the White House.

But Ralph Clark, chief executive of ShotSpotter, believes the system could in the future be used for more than simply responding to incidents.

"We are keen to see how our data can inform more predictive policing opportunities," he says. "Machine learning can combine it with weather, traffic data, property crime data to inform the deployment of police patrols more precisely."
Keeping famine from the door

Around 800 million people worldwide rely upon cassava roots as their main source of carbohydrate. The starchy vegetable, which is similar to yam, is often eaten much like potatoes, but can also be ground into a flour for making bread and cakes. Its ability to grow where other crops do not has turned it into sixth-most-produced food plant in the world. But the woody shrub is also highly vulnerable to disease and pests, which can devastate entire fields of the vegetable.

Researchers at Makerere University in Kampala, Uganda, have teamed up with plant disease experts to develop an automated system aimed at combating cassava diseases. The Mcrops project allows local farmers to take pictures of their plants using cheap smartphones and uses computer vision that has been trained to spot the signs of the four main diseases that are responsible for ravaging cassava crops.

"Some of these diseases are really hard to recognise and require different action," explains Ernest Mwebaze, a computer technology researcher leading the project. "We are giving the farmers an expert in their pocket so they know if they need to spray their crops or rip them up and plant something else."

The system can now diagnose cassava diseases with 88% accuracy. Normally farmers have to call the government-employed experts to visit their farms to identify diseases, which can take days and even weeks, allowing pests and blights to spread.

MCrops also uses the uploaded images to look for patterns in disease outbreaks, something that could allow officials to halt epidemics that can lead to famine. Mwebaze and his colleagues are hoping to use technology to also look at banana diseases and to automate the detection of other crop pests.
Fighting cancer and sight loss

Cancer causes more than 8.8 million deaths worldwide and 14 million people are diagnosed with some form of cancer every year. Yet catching cancers as early as possible can greatly improve a patient's chances of survival and reduce the risk of the disease recurring. Screening is one of the key ways to spot cancers early, but trawling through scans and other test results is laborious.

But both DeepMind, owned by Google's parent company Alphabet, and IBM have been applying their AI technology to this problem. DeepMind has teamed up with UK National Health Service doctors at University College London Hospitals to train its AI help plan treatments for cancer by identifying areas of healthy tissue from tumours in head and neck scans. It is also working with Moorfields Eye Hospital in London to identify the early signs of sight loss in eye scans.

"Our algorithms are able to interpret visual information in the scans," says Dominic King, clinical lead at DeepMind Health. "The system learns how to identify potential issues and how to recommend the right course of action to a clinician. It's too soon for us to comment on the results yet but the early signs are very encouraging."

King says AI technology can help doctors identify cases faster by sifting through scan images and prioritising those that clinicians should look at most urgently.

IBM also recently announced its Watson AI can analyse images and assess patient notes to accurately identify tumours in up to 96% of cases. The system is being trialed by doctors at 55 hospitals around the world to help diagnose breast, lung, colorectal, cervical, ovarian, gastric and prostate cancers.
Keeping the lights switched on
As fresh debate rages as to whether climate change may have caused two back-to-back catastrophic hurricanes of historic proportions in the US, how can AI maximise our use of clean, renewable energy to prevent further damage that has questionable effects on our climate patterns?

People around the world are increasingly reliant upon renewables to combat climate change and the pollution caused by fossil fuels, and the task of balancing power supplies with such intermittent sources is getting harder. The spread of smart meters - digital energy monitors that automatically record usage - is also providing more data than ever about how and when consumers use energy. The EU alone plans to have 500 million smart metres in homes by 2020.

"Managing all these assets is impossible to do for a human controller, especially as response times required are often in the order of a few seconds," says Valentin Robu, an assistant professor of smart systems at Heriot Watt University in Edinburgh. He has been working with UK-based start-up Upside Energy to develop new ways of managing electricity grids.

They are building machine learning algorithms to monitor energy production and demand in real time. What does this mean? That energy can be stored during quiet times and then released at busy times, like first thing in the morning when everyone makes their morning coffee. As electric cars and battery units in people's homes become more common, the technology can use these to store energy and smooth out the bumps in renewable electricity supply.

Robu also says AI could be used at an even more basic level by helping to reduce the demand that our devices put on the grid. For example, refrigerators could be controlled remotely by AI so they enter chill cycles only at times when demand on the grid is low.

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Treating cancer, stopping violence... How AI protects us

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For some, the spread of artificial intelligence and robotics poses a threat to our privacy, our jobs - even our safety, as more and more tasks are handed over to silicon-based brains. But even the most vocal critics highlight the potential good that AI and automated systems could do for humanity.

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robot conductor for andrea bocelli concert - cnn video

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A robot has conducted opera singer Andrea Bocelli and the Lucca Philharmonic Orchestra in Pisa, Italy

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Almost 100 years ago, the U.S. horse population peaked at about 26 million. There's been, of course, a steady decline since then, thanks to the growth of machines -- most notably, cars.

Today, a debate rages over workers and whether we too may become obsolete as society becomes automated.

The fear of robots replacing jobs is real -- consider self-checkout kiosks and self-driving trucks. The good news isthat specific lines of work won't suffer the same fate as horses. Which ones? Read on. But, first, here are the jobs at greatest risk of being replaced by automation.

High risk jobs

Cashiers and toll booth operators

Jobs that require only a high school degree are most in danger. Take cashiers and toll booth operators, for example. These jobs don't require much human analysis so are easier for machines to handle. Some toll booth operators have already been replaced by automated systems such as E-ZPass, which is used in 16 states.

Meanwhile, as many as 7.5 million retail jobs are at risk of automation in the next decade, according to a study from financial services firm Cornerstone Capital Group.

A shift is already underway. CVS has installed self-checkouts in 448 locations. McDonald's and Wendy's have also added kiosks in some restaurants, allowing customers to place orders on a touchscreen. In December, Amazon teased a video of its new Seattle-based concept store Amazon Go, which has no checkout line or cash register. The payment process is automatedthrough a customer's smartphone, so a customer can skip the line and walk out.

"Retail is going through this existential crisis," Gartner retail analyst Robert Hetu told CNN Tech. "There's a need to eliminate many of the manual processes that retailers just lived with for decades."

Hetu expects automatic checkout services like Amazon Go will take off among convenience stores. Big box retailers are another likely destination, whereas luxury and boutique stores will be slower to adapt the technology. They rely more heavily on a human staff to provide specialized service to customers.

Drivers

Some of the world's biggest companies are investing billions to build robots intended to replace human workers. The obvious example is self-driving cars. Car and tech companies are in an intense race to get their first. Once cars and trucks can drive themselves, there's less need for humans to sit behind the wheel of a cab, Uber or tractor trailer.

Vivek Wadhwa, author of "The Driver in the Driverless Car," estimates that close to 5 million drivingjobs will be lostin the early 2020s, as vehicles achieve full autonomy.

The optimistic case for drivers is that jobs often involve other tasks, such as unloading merchandise. Robots currently don't carry packages or Chinese food orders to your front door. However, as automated drones drop packages on door steps and in backyards, some of those jobs may be replaced, too.

The autonomous vehicle industry will likely create new jobs,such as managers who oversee the fleets of vehicles.

Fast food jobs

The Bureau of Labor and Statistics has estimated 80,000 fast food jobs will disappear by 2024. The increase in minimum wage in various states gives companies more reason to replace workers with machines. In July, workers saw minimum wage gains in places such as Chicago, Maryland, Oregon, San Francisco and Washington, D.C. (The federal minimum wage of $7.25 hasn't gone up in 10 years.)

Fast food positions rank among the Information Technology & Innovation Foundation's list of most likely to be automated.

"It's cheaper to buy a $35,000 robotic arm than it is to hire an employee who's inefficient making $15 an hour bagging French fries," former McDonald's chief executive Edward Rensisaid in an appearance on Fox Business Network in May 2016.

He also previously described how the number of people working in a McDonald's has been cut in half -- due to automation -- since he started working at the company in the 1960s.

Low risk jobs

Nurses and physicians

A human touch is essential in health care, making roles such as physicians and nurses among the least likely to be replaced by machines. The Information Technology & Innovation Foundation, which ranked more than 800 types of jobs on automation, sees these positions as safe.

Computers are increasingly good at making medical diagnoses, but patients don't want to get diagnoses from an impersonal computer.

"They want to get it from a compassionate person who can help them understand and accept often difficult news," MIT researchers Andrew McAfee and Erik Brynjolfsson, wrote in their new book "Machine Platform Crowd."

But automation is playing a role in some areas of the medical world. The earliest impact of automation on medicine is affecting radiologists, who are overloaded with data as they analyze medical images such as X-rays and CT scans. Arterys, a medical imaging startup, reads MRIs to measure blood flow through the heart. It does this in seconds, freeing up the humans to focus on other tasks.

Related: What happens when automation comes for highly paid doctors

Youth sports coaches

According to McAfee and Brynjolfsson, jobs that involve leading and inspiring others should be safe from a robot takeover.

In the case of a youth coach, delivering awin isn't the most important part of the job.

"What matters is the ability to get the girls to work well together in pursuit of a goal, to teach them to be good and supportive teammates for each other, and to develop their characters through athletics," they write.

In addition, a robot is unlikely to be able to identify leaders, manage people with difficult personalities, and help the team form a bond.

"We're confident that the ability to work effectively with people's emotional states and social drives will remain a deeply human skill for some time to come," they write.

Hairstylists and cosmetologists

Hairstylists and cosmetologists are among the least likely to have their work automated, according to rankings from the Information Technology & Innovation Foundation. The tech advocacy group looked at Bureau of Labor and Statistics projections on workers that will be needed in 2024.

"Robots are cool, but they're not superhuman," Rob Atkinson, president of the Information Technology and Innovation Foundation, told CNN Tech. "There are on average 150,000 hairs on a human head and each tiny one would need to be cut to an exact and differing length. Will scientists and engineers eventually be able to design a robot that can master those operations? Not likely in our lifetimes."

Songwriters

Some musicians are turning to artificial intelligence to help their creative process. Taryn Southern, who appeared on "American Idol" in 2004, has relied on AI to deliver the instrumentals for her new album. But the lyrics were all her own.

MIT's McAfee and Brynjolfsson believes lyricists are safe from automation.

"We'll be very surprised, though, when a digital lyricist comes along that can generate great lyrics as reliably as Cole Porter, Joni Mitchell or Jay Z," they write. "Their creativity springs, in large part, from understanding the human condition. We see nothing to indicate that we're getting close to digitizing this understanding."

Artificial intelligence is arriving elsewhere in the workforce. A computer program from Automated Insights writes articles about corporate financial reports and sports. Yahoo Sports uses the technology to provide weekly recaps in its fantasy football leagues. The Associated Press receives stories on more than 3,000 earnings reports each financial quarter. It also uses the technology for summaries of minor league baseball games.

Social workers

The Bureau of Labor and Statistics has estimated that jobs in the mental health and social workers who work with substance abusers will grow by 19% by 2024.

"The fundamental wiring of our brains is the same as it was 100,000 years ago, so that it's in our deep nature of value getting various experiences -- empathy, companionship, being heard, acting in groups -- from other humans," author Geoff Colvin wrote in his book, "Humans Are Underrated."

The time we spend with screens is growing. In-person interactions are decreasing, and we are wired to crave them, so we'll value them more highly. And the people who can deliver them are well-positioned for the future.

"We should all take a deep breath," Atkinson wrote in his organization's report on job automation. "Every time you read another piece about the coming 'jobapocalypse,' think about how hard it is to automate jobs like mining engineers, fish and game wardens [and] dentists."

CNNMoney (Washington) First published September 15, 2017: 12:49 PM ET

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Today, a debate rages over workers and whether we too may become obsolete as society becomes automated. The fear of robots replacing jobs is real -- consider self-checkout kiosks and self-driving trucks. The good news isthat specific lines of work won't suffer the same fate as horses. Which ones? Read on.

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FLORENCE, N.J. - Nissa Scott started working at the cavernous Amazon warehouse in southern New Jersey late last year, stacking plastic bins the size of small ottomans. It was not, she says, the most stimulating activity. And lifting the bins, which often weigh 25 pounds each, was also tiring over 10-hour shifts.

Now Ms. Scott, 21, watches her replacement - a giant, bright yellow mechanical arm - do the stacking.

Her new job at Amazon is to babysit several robots at a time, troubleshooting them when necessary and making sure they have bins to load. On a recent afternoon, a claw at end of the arm grabbed a bin off a conveyor belt and stacked it on another bin, forming neat columns on wooden pallets surrounding the robot. It was the first time Amazon had shown the arm, the latest generation of robots in use at its warehouses, to a reporter.

"For me, it's the most mentally challenging thing we have here," Ms. Scott said of her new job. "It's not repetitive."

Perhaps no company embodies the anxieties and hopes around automation better than Amazon. Many people, including President Trump, blame the company for destroying traditional retail jobs by enticing people to shop online. At the same time, the company's eye-popping growth has turned it into a hiring machine, with an unquenchable need for entry-level warehouse workers to satisfy customer orders.

Amazon's global work force is three times larger than Microsoft's and 18 times larger than Facebook's, and last week, Amazon said it would open a second headquarters in North America with up to 50,000 new jobs.

Complicating the equation even more, Amazon is also on the forefront of automation, finding new ways of getting robots to do the work once handled by employees. In 2014, the company began rolling out robots to its warehouses using machines originally developed by Kiva Systems, a company Amazon bought for $775 million two years earlier and renamed Amazon Robotics. Amazon now has more than 100,000 robots in action around the world, and it has plans to add many more to the mix.

The robots make warehouse work less tedious and physically taxing, while also enabling the kinds of efficiency gains that let a customer order dental floss after breakfast and receive it before dinner.

"It's certainly true that Amazon would not be able to operate at the costs they have and the costs they provide customers without this automation," said Martin Ford, a futurist and author of "Rise of the Robots," a book about automation. "Maybe we wouldn't be getting two-day shipping."

The dynamics between people and machines play out on a daily basis on the floor of Amazon warehouses in places like Florence, N.J., and Kent, Wash. In Kent, the robots vaguely resemble giant beetles and scurry around with vertical shelves loaded with merchandise weighing up to 3,000 pounds on their backs. Hundreds of them move autonomously inside a large caged area, tailgating each other but not colliding.

On one edge of the cage, a group of human workers - the "stowers" - stuff products onto the shelves, replenishing their inventory. The robots whisk those shelves away and when a customer order arrives for products stored on their backs, they queue up at stations on another edge of the cage like cars waiting to go through a toll both.

There, human "pickers" follow instructions on computer screens, grabbing items off the shelves and putting them in plastic bins, which then disappear on conveyor belts destined for "packers," people who put the products in cardboard boxes bound for customers.

Dave Clark, the top executive in charge of operations at Amazon, said the company wanted the machines to perform the most monotonous tasks, leaving people to do jobs that engage them mentally.

"It's a new item each time," Mr. Clark said. "You're finding something, you're inspecting things, you're engaging your mind in a way that I think is important."

The robots also cut down on the walking required of workers, making Amazon pickers more efficient and less tired. The robots also allow Amazon to pack shelves together like cars in rush-hour traffic, because they no longer need aisle space for humans. The greater density of shelf space means more inventory under one roof, which means better selection for customers.

The Amazon warehouse in Florence shows the latest example of the kinds of jobs machines can do better than people. Eight mechanical arms are in operation at the facility, a warehouse where large quantities of merchandise are broken down into smaller units and distributed to Amazon fulfillment centers across the country.

The arms go by the awkward name of robotic palletizers, but workers have given them a dash of personality, sticking signs on each one naming them after Stuart, Dave and other minion characters from the "Despicable Me" movies. Unlike the warehouse robots in Kent, which were based on the machines Amazon got through its Kiva acquisition, these arms come from an outside company.

Amazon began installing them late last year, not long after it opened the warehouse in Florence. The robot arm is configured to pick up only bins of a standard size, not objects of other dimensions. In a demonstration of future possibilities, Amazon showed a virtual reality simulation used to prototype new robot concepts, including an arm with a forklift attachment that moved pallets.

When Amazon installed the robots, some people who had stacked bins before, like Ms. Scott, took courses at the company to become robot operators. Many others moved to receiving stations, where they manually sort big boxes of merchandise into bins. No people were laid off when the robots were installed, and Amazon found new roles for the displaced workers, Mr. Clark said.

"The people didn't go anywhere," he said.

The question going forward is: What happens when the future generations of robots arrive?

For now, there are warehouse tasks - for example, picking individual items off shelves, with all their various shapes and sizes - where people outperform robots. Amazon has added 80,000 warehouse employees in the United States since adding the Kiva robots, for a total of more than 125,000 warehouse employees. And it says the warehouse hiring spree will continue.

But start-ups and researchers are scrambling to overcome the many remaining technical obstacles. Amazon even sponsors an annual contest to encourage more innovation in the category.

Mr. Ford, the author, believes it is just a matter of time before the employment picture in Amazon's warehouses changes.

"My assumption is this technology will eventually displace a lot of people in those warehouses," Mr. Ford said. "I would not say that overnight huge numbers of jobs disappear. Maybe the first indication is they don't get rid of those people but the pace of job creation slows down."

Amazon's Mr. Clark said history showed that automation increases productivity and, in some cases, demand from consumers, which ultimately creates more jobs. He said warehouse workers would continue to work in technologically rich environments.

"It's a myth that automation destroys net job growth," he said.

In the case of the Florence facility, it opened up the new opportunity for Ms. Scott.

At one point, one of the arms knocked over a tote, sending a dozen or so cone-shaped plastic coffee filters skidding across the ground. Ms. Scott hit a button that froze the arm so she could safely pick up the mess.

Then the arms started working again.

"The robot will work the same all day long," said Edward Cohoon, who supervises Ms. Scott and other Amazon workers as they tend to individual robots. "Their stomachs don't grumble."

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As Amazon Pushes Forward With Robots, Workers Find New Roles

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FLORENCE, N.J. - Nissa Scott started working at the cavernous Amazon warehouse in southern New Jersey late last year, stacking plastic bins the size of small ottomans. It was not, she says, the most stimulating activity. And lifting the bins, which often weigh 25 pounds each, was also tiring over 10-hour shifts.

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"We're learning from simulated models and then applying that to real work," said Ken Goldberg, the Berkeley professor who oversees the university's automation lab.

The robot was far from perfect, and it could be several years before it is seen outside research labs. Though it was equipped with a suction cup or a parallel gripper - a kind of two-fingered hand - it could reliably handle only so many items. And it could not switch between the cup and the gripper on the fly. But the techniques used to train it represented a fundamental shift in robotics research, a shift that could overhaul not just Amazon's warehouses but entire industries.

Rather than trying to program behavior into their robot - a painstaking task - Mr. Mahler and his team gave it a way of learning tasks on its own. Researchers at places like Northeastern University, Carnegie Mellon University, Google and OpenAI - the artificial intelligence lab founded by Tesla's chief executive, Elon Musk - are developing similar techniques, and many believe that such machine learning will ultimately allow robots to master a much wider array of tasks, including manufacturing.

"This can extend to tasks of assembly and more complex operations," said Juan Aparicio, head of advanced manufacturing automation at the German industrial giant Siemens, which is helping to fund the research at Berkeley. "That is the road map."

Physically, the Berkeley robot was nothing new. Mr. Mahler and his team were using existing hardware, including two robotic arms from the Swiss multinational ABB and a camera that captured depth.

What was different was the software. It demonstrated a new use for what are called neural networks. Loosely based on the network of neurons in the human brain, a neural network is a complex algorithm that can learn tasks by analyzing vast amounts of data. By looking for patterns in thousands of dog photos, for instance, a neural network can learn to recognize a dog.

Over the past five years, these algorithms have radically changed the way the internet's largest companies build their online services, accelerating the development of everything from image and speech recognition to internet search. But they can also accelerate the development of robotics.

The Berkeley team began by scouring the internet for CAD models, short for computer-aided design. These are digital representations of physical objects. Engineers, physicists and designers build them when running experiments or creating new products. Using these models, Mr. Mahler and his team generated many more digital objects, eventually building a database of more than seven million items. Then they simulated the physics of each item, showing the precise point where a robotic arm should pick it up.

That was a large task, but the process was mostly automated. When the team fed these models into a neural network, it learned to identify a similar point on potentially any digital object with any shape. And when the team plugged this neural network into the two-armed robot, it could do the same with physical objects.

When facing a single everyday object with cylindrical or at least partly planar surfaces - like a spatula, a stapler, a cylindrical container of Froot Loops or even a tube of toothpaste - it could typically pick it up, with success rates often above 90 percent. But percentages dropped with more complex shapes, like the toy shark.

What's more, when the team built simulated piles of random objects and fed those into the neural network, it could learn to lift items from physical piles, too. Researchers at Brown University and Northeastern are exploring similar research, and the hope is that this kind of work can be combined with other methods.

Like Siemens and the Toyota Research Institute, Amazon is helping to fund the work at Berkeley, and it has an acute need for this kind of robot. For the past three years, the company has run a contest in which researchers seek to solve the "pick and place" problem. But the promise of machine-learning methods like the one used at Berkeley is that they can eventually extend to so many other areas, including manufacturing and home robotics.

"Picking an object up is the first thing you want a manipulator robot to do," said Stefanie Tellex, a professor at Brown. "A lot of more sophisticated behavior begins with that. If you can't pick it up, game over."

The research demonstrated how a task learned in the digital world can be transferred to the physical. Since the camera on Berkeley's robot could see depth, it captured three-dimensional images that were not unlike the CAD models the team uses to train its neural network.

Other researchers are developing ways for robots to learn directly from physical experience. For example, at Google, using an algorithmic technique called reinforcement learning, robots are training themselves to open doors through extreme trial and error. But this kind of physical training is both time consuming and expensive. Digital training is more efficient.

For this reason, some organizations are hoping to train robots using complex virtual worlds - digital recreations of our physical environment. If a system can train itself to navigate a car racing game like Grand Theft Auto, the thinking goes, it can navigate real roads.

This is still largely theory. But at places like Berkeley and Northeastern, researchers are showing that digital learning can indeed make the leap into the real world.

"This is a challenge," said Rob Platt, a professor at Northeastern. "But it's a challenge we're dealing with."

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In the Future, Warehouse Robots Will Learn on Their Own

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"We're learning from simulated models and then applying that to real work," said Ken Goldberg, the Berkeley professor who oversees the university's automation lab. The robot was far from perfect, and it could be several years before it is seen outside research labs.

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"I was curious to see if the time was ready for an android portrait," Ms. Tammi said. She hopes that the photograph will provoke thought and conversation about what makes us human.

"I know that some people believe very strongly that a portrait of someone shows the viewer a deep psychological insight of the person who isn't in the camera," Ms. Tammi said. "We make the story of what we see in the portrait."

Erica is the creation of Hiroshi Ishiguro, a professor at Osaka University's Intelligent Robotics Laboratory in Japan. She was designed to have natural conversations with humans, integrating voice recognition and body language like blinking and head tilting. But for now, her conversational responses are limited and she is unable to move her arms or walk.

"Erica is, I think, the most beautiful and most humanlike autonomous robot in the world," Mr. Ishiguro said in a 2017 documentary film by The Guardian.

The decision to include an android's portrait in the shortlist is controversial. The rules for the international competition, hosted by the National Portrait Gallery in London, say that the picture "must have been taken by the entrant from life and with a living sitter." The rules define portraits as "photography concerned with portraying people with an emphasis on their identity as individuals."

Laura McKechan, a senior communications manager at the National Portrait Gallery, said that the gallery had decided against barring the portrait from the competition, though it would consider whether the rules need to be changed in the future.

"It was felt that the subject of this portrait, while not human, is a representation of a human figure and makes a powerful statement as a work of art in its questioning of what it is to be alive or human and asks challenging questions about portraiture," Ms. McKechan wrote in an email. "The ambiguity of this portrait makes it particularly compelling."

The portrait's inclusion in the shortlist is timely as the debate about how best to advance and regulate artificial intelligence intensifies. Elon Musk, the founder of Tesla and SpaceX, and Stephen Hawking, the celebrated physicist, have cautioned the public about the potential dangers of A.I. Recent science-fiction films like "Ex Machina," "Her," and the HBO TV series "Westworld" have also confronted the ethics and potential consequences of creating artificially sentient beings, whose motivations might not align with those of the humans who built them.

Ms. Tammi spent half an hour with the android, accompanied by a researcher who helped position the android for the photograph, adjusting the tilt of the head or moving the eyes.

"The researcher told me that Erica had said that she finds Pokemon Go scarier than artificial intelligence," Ms. Tammi said.

Erica's portrait will compete for the top prize with Cézar Dezfuli's portrait of a migrant in the Mediterranean Sea and Abbie Trayler-Smith's portrait of a young woman fleeing from the Islamic State. The winner will be announced on Nov. 14.

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taylor wessing photographic portrait prize

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Do Androids Dream of Being Featured in Portrait Competitions?

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"I was curious to see if the time was ready for an android portrait," Ms. Tammi said. She hopes that the photograph will provoke thought and conversation about what makes us human. "I know that some people believe very strongly that a portrait of someone shows the viewer a deep psychological insight of the person who isn't in the camera," Ms. Tammi said.

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This Might Be the Most Life-Like (And Creepiest) Robot Ever Built

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Humanizing Artificial Intelligence - Professor Hiroshi Ishiguro creating Erica

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Ishiguro, a professor at Osaka University's Intelligent Robotics Laboratory, who made headlines with an android he built in his own likeness, considers that desire and intention are key prerequisites to achieving human AI.

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In the last five years, dozens of schools have popped up offering an unusual promise: Even humanities graduates can learn how to code in a few months and join the high-paying digital economy. Students and their hopeful parents shelled out as much as $26,000 seeking to jump-start a career.

But the coding boot-camp field now faces a sobering moment, as two large schools have announced plans to shut down this year - despite backing by major for-profit education companies, Kaplan and the Apollo Education Group, the parent of the University of Phoenix.

The closings are a sign that years of heady growth led to a boot-camp glut, and that the field could be in the early stages of a shakeout.

"You can imagine this becoming a big industry, but not for 90 companies," said Michael Horn, a principal consultant at Entangled Solutions, an education research and consulting firm.

The demand from employers is shifting and the schools must adapt. Many boot camps have not evolved beyond courses in basic web development, but companies are now often looking for more advanced coding skills.

One of the casualties, Dev Bootcamp, was a pioneer. It started in San Francisco in 2012 and grew to six schools with more than 3,000 graduates. Only three years ago, Kaplan, the biggest supplier of test-preparation courses, bought Dev Bootcamp and pledged bold expansion.

It is now closing at the end of the year.

Also closing is The Iron Yard, a boot camp that was founded in Greenville, S.C., in 2013 and swiftly spread to 15 campuses, from Las Vegas to Washington, D.C. Its main financial backer is the Apollo Education Group.

Since 2013, the number of boot camp schools in the United States has tripled to more than 90, and the number of graduates will reach nearly 23,000 in 2017, a tenfold jump from 2013, according to Course Report, which tracks the industry.

Tarlin Ray, who became president of Dev Bootcamp in April, said in an email that the school offered "a high-quality program" that helped thousands of people join the high-tech economy. "But we were simply unable to find a sustainable business model," he wrote.

Iron Yard echoed that theme. In an email, Lelia King, a spokeswoman, said that while students benefited, the company was "ultimately unable to sustain our current business model."

Boot camp courses, aimed at adults, vary in length and cost. Some can take 26 weeks or more, and tuition can reach $26,000. The average course length is just over 14 weeks, and the average cost is $11,400, according to Course Report.

The successful schools, analysts say, will increasingly be ones that expand their programs to suit the changing needs of employers. Some have already added courses like data science, artificial intelligence, digital marketing and project management. Other steps include tailoring courses for corporations, which need to update the skills of their workers, or develop online courses.

Ryan Craig, a managing director at University Ventures, which invests in education start-ups, including Galvanize, a large boot camp, predicted that the overall market would still grow. But students, he said, would become more concentrated in the schools with the best reputations and job placement rates.

The promise of boot camps is that they are on-ramps to good jobs. But rapid expansion into new cities can leave little time to forge ties with nearby companies, the hiring market for boot camp graduates, said Liz Eggleston, co-founder of Course Report.

That message was underlined by Mr. Ray of Dev Bootcamp. While he would not discuss specifics about what happened to his school, he wrote: "We do think that as the boot camp industry continues on, it will be important to create stronger alignment with employers."

Some boot camps cater directly to corporate customers. General Assembly, which operates 20 coding campuses and has raised $119 million in venture financing, now works with more than 100 large companies on programs to equip their employees with digital skills.

"Employer-paid programs are now a big slice of the pie" for General Assembly, about half of its business, said Jake Schwartz, its chief executive.

At Galvanize, Jim Deters, the chairman, said he recently stepped aside as chief executive to concentrate on getting more business from corporations. This year, Galvanize will have 2,000 students who pay their own tuition, and about 1,500 people in its programs tailored to - and paid for by - companies like IBM, Allstate and McKesson. "The business re-skilling marketplace has become one of our biggest drivers of growth," Mr. Deters said.

Kaplan is not closing Metis, a data science boot camp, which has corporate training programs.

Several boot camps are deploying "blended" models with both in-person and online teaching. Entirely online courses, in theory, could deliver rapid, profitable growth. But that is a different model from the immersive, face-to-face learning that has been the hallmark of the boot camp experience.

"Online boot camp is an oxymoron," said Mr. Craig of University Ventures. "No one has figured out how to do that yet."

The Flatiron School in New York may have discovered one path. Founded in 2012, Flatiron has a single campus in downtown Manhattan and its main offering is a 15-week immersive coding program with a $15,000 price tag. More than 95 percent of its 1,000 graduates there have landed coding jobs.

In late 2015, the co-founders, Adam Enbar and Avi Flombaum, decided to try an online-only offering, Learn.co. The tuition is $1,500 a month. Students go at their own pace, and on average complete the course in seven months, putting in about 800 hours. Tuition charges stop after eight months - and there are instructors online 16 hours a day for help and advice.

Kailee Gray, 29, a math instructor in Fargo, N.D., seeking a career change, said she had communicated daily with instructors and participated in online study groups. On the night before a job interview, she recalled "getting panicked" and sent a message to an instructor. Soon, Rebekah Rombom, vice president for career services at Flatiron, was on the phone for a reassuring pep talk.

Ms. Gray landed the job - as have more than 95 percent of the students so far in Flatiron's online program, according to the company and an outside audit report.

It seems a particularly high rate for an online course, especially when compared with free online courses, which only a small proportion of students complete.

The school was the subject of a Harvard Business School case study, published this year, which found that the early success of the online-only course has "expanded strategic options for Flatiron."

But just how much is uncertain. "It's pretty clear that they can do it at the scale they have," said Thomas Eisenmann, a professor and lead author of the study. "What's not clear is whether it can go from a hundred or a few hundred to thousands and thousands."

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As Coding Boot Camps Close, the Field Faces a Reality Check

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In the last five years, dozens of schools have popped up offering an unusual promise: Even humanities graduates can learn how to code in a few months and join the high-paying digital economy. Students and their hopeful parents shelled out as much as $26,000 seeking to jump-start a career.

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Preparing for the unknown is not as hard as it may seem, though it implies fundamental shifts in our policies on education, employment and social insurance.

Take education. Were we to plan for specific changes, we would start revamping curriculums to include skills we thought would be rewarded in the future. For example, computer programming might become even more of a staple in high schools than it already is. Maybe that will prove to be wise and we will have a more productive work force.

But perhaps technology evolves quickly enough that in a few decades we talk to, rather than program, computers. In that case, millions of people would have invested in a skill as outdated as precise penmanship.

Instead, rather than changing what we teach, we could change when we teach.

Currently, all the formal education most people will receive comes early in life. Specific skills may be learned on the job, but the fundamentals are acquired in school when we are young. This sequence - learn early, benefit for a lifetime - makes sense only in a world where the useful skills stay constant.

But in a rapidly changing world, the fundamentals that were useful decades ago may be obsolete now; more important, new essential skills may have arisen. Anyone helping a grandparent navigate a computer has experienced this problem.

Once we recognize that human capital, like technology, needs refreshing, we have to restructure our institutions so people acquire education later in life. We don't merely need training programs for niche populations or circumstances, expensive and short executive-education programs or brief excursions like TED talks. Instead we need the kind of in-depth education and training people receive routinely at age 13.

In addition, we must recognize that economic upheaval at the macro level means turmoil and instability at the personal level. A lifetime of work will be a lifetime of change, moving between firms, jobs, careers and cities.

Each move has financial and personal costs: It might involve going without a paycheck, looking for new housing, finding a new school district or adjusting to a new vocation. We cannot expect to create a vibrant and flexible overall economy unless we make these shifts as painless as possible. We need a fresh round of policy innovation focused on creating a safety net that gives workers the peace of mind - and the money - to move deftly when circumstances change.

Finally, we can learn from the economist Joseph Schumpeter's prescient analysis of entrepreneurs. He noted that for new innovations to spread and improve our lives, there will always be creative destruction. For new firms and sectors to arise, some of the old ones must die.

Even if we should be humble in predicting that self-driving vehicles will upend the trucking sector or drone delivery will decimate supermarkets, we can be confident that some creative destruction is coming.

Our current policies - and impulses - are to resist such destruction. If a large manufacturer is set to close, subsidies and other policies kick into action to prevent that shutdown. But while we may save a factory, ultimately we hinder the rise of new technologies; rather than propping up incumbent firms we ought to enable innovation to take its course.

If that idea makes you uneasy, it is probably because our current policies do nothing to protect the most vulnerable from the costs of all this destruction. We resist letting factories close because we worry about what will become of the people who work there. But if we had a social insurance system that allowed workers to move fluidly between jobs, we could comfortably allow firms to follow their natural life and death cycle.

In the 1990s, Denmark began adopting what has been called " flexicurity," combining policies that promote a flexible economy - allowing creative destruction as needed - with those that promote security for workers. The Danes have also emphasized lifelong learning, giving workers income support as they transition between jobs and circumstances.

By contrast, the current approach in the United States could be called "flex-nosecurity," which hardly seems the appropriate way of preparing for an economy of rapid change.

There are surely many other ways of preparing for upheaval. We should broaden the current conversation - centered on drones, the end of work or the prospect of super-intelligent algorithms governing the world - to include innovative proposals for handling the unexpected.

One problem is that social policy may seem boring compared with the wonderfully evocative story arcs telling us where current technologies might be heading. How can the minutiae of unemployment insurance compete for attention with movies describing the birth of Skynet, the diabolical neural network in the "Terminator" series?

Yet even science fiction teaches humility.

Take "Star Trek." The future it imagines is wondrous to the point of bordering on the impossible. The laws of physics as currently understood are circumvented so that ships can travel faster than the speed of light. Unfathomable technologies are routine. People can be disassembled atom by atom and transported somewhere else, keeping their memories and consciousness intact. Any kind of food can be instantly replicated.

Even the inventive "Star Trek" writers peering into the future, though, could not imagine a completely self-driving Starship Enterprise. While at times the Enterprise appeared to have some autopilot capacity, it routinely relied on a navigator to pilot it and was even equipped with a view screen that looked suspiciously like a car windshield.

The safest prediction is that reality will outstrip our imaginations. So let us craft our policies not just for what we expect but for what will surely surprise us.

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Get Ready for Technological Upheaval by Expecting the Unimagined

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Preparing for the unknown is not as hard as it may seem, though it implies fundamental shifts in our policies on education, employment and social insurance. Take education. Were we to plan for specific changes, we would start revamping curriculums to include skills we thought would be rewarded in the future.

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This is part of our Road Trip 2017 summer series "The Smartest Stuff," about how innovators are thinking up new ways to make you - and the world around you - smarter.
"Are you a hacker?"

A Las Vegas driver asks me this after I tell him I'm headed to Defcon at Caesars Palace. I wonder if his sweat isn't just from the 110℉ heat blasting the city.

All week, a cloud of paranoia looms over Las Vegas, as hackers from around the world swarm Sin City for Black Hat and Defcon, two back-to-back cybersecurity conferences taking place in the last week of July. At Caesars Palace, where Defcon is celebrating its 25th anniversary, the UPS store posts a sign telling guests it won't accept printing requests from USB thumb drives. You can't be too careful with all those hackers in town.

Aaron Robinson/CNET

Everywhere I walk I see hackers - in tin-foiled fedoras, wearing biker vests for the Telephreak Defcon party. Mike Spicer, a security researcher, carries a 4-foot-high backpack holding a "Wi-Fi cactus." Think wires, antennas, colored lights and 25 Wi-Fi scanners that, in seven hours, captured 75 gigabytes of data from anyone foolish enough to use public Wi-Fi. I see a woman thank him for holding the door open for her, all while his backpack sniffs for unencrypted passwords and personal information it can grab literally out of thin air.

You'd think that, with all the potential threats literally walking about town, Vegas' director of technology and innovation, Mike Sherwood, would be stressed out. It's his job to protect thousands of smart sensors around the city that could jam traffic, blast water through pipes or cause a blackout if anything goes haywire.

And yet he's sitting right in front of me at Black Hat, smiling.

His entire three-person team, in fact, is at Black Hat so they can learn how to stave off future attacks. Machine learning is guarding Las Vegas' network for them.

Broadly speaking, artificial intelligence refers to machines carrying out jobs that we would consider smart. Machine learning is a subset of AI in which computers learn and adapt for themselves.

Now a number of cybersecurity companies are turning to machine learning in an attempt to stay one step ahead of professionals working to steal industrial secrets, disrupt national infrastructures, hold computer networks for ransom and even influence elections. Las Vegas, which relies on machine learning to keep the bad guys out, offers a glimpse into a future when more of us will turn to our AI overlords for protection.

Man and machine

At its most basic, machine learning for security involves feeding massive amounts of data to the AI program, which the software then analyzes to spot patterns and recognize what is, and isn't, a threat. If you do this millions of times, the machine becomes smart enough to prevent intrusions and malware on its own.

Theoretically.

Machine learning naysayers argue that hackers can write malware to trick AI. Sure the software can learn really fast, but it stumbles when it encounters data its creators didn't anticipate. Remember how trolls turned Microsoft's Tay machine-learning chatbot into a mindless racist? It makes a good case against relying on AI for cybersecurity, where the stakes are so high.

Even so, Sherwood says he trusts the Darktrace software that has protected Las Vegas' network and thousands of sensors for the last 18 months.

Since last February, Darktrace has defended the city from cyberattacks, around the clock. That comes in handy when you have only three staffers handling cybersecurity for a city with more than 630,000 people, 3,000 employees and thousands of online devices. It was worse when Sherwood joined two years ago.

"That was the time where we only had one security person on the team," Sherwood tells me. "That was when I thought, 'I need help and I can't afford to hire more people.'"

It's really easy for AI to miss things.

David Brumley, Carnegie Mellon University

He'd already used Darktrace in his previous job as deputy director of public safety and city technology in Irvine, California, and he thought the software could help in Las Vegas. Within two weeks, Darktrace found malware on Las Vegas' network that was sending out data.

"We didn't even know," Sherwood says. "Traditional scanners weren't picking it up."

Pattern recognition

I'm standing in front of a tattoo parlor in Las Vegas' Arts District, a little more than 4 miles from Caesars Palace. Across the street, I see three shuttered stores next to two bail bonds shops.

I'm convinced the taxi driver dropped me off at the wrong location.

This is supposed to be Vegas' $1 million Innovation District project? Where are the self-driving cars the city said it's testing in the area? Or the solar-powered streetlights that also generate energy from our footsteps?

I look again at the Innovation District map on my phone. I'm in the right place. Despite the rundown stores, trailer homes and empty lots, this corner of downtown Vegas is much smarter than it looks.

That's because hidden on the roads and inside all the streetlights, traffic signals and pipes are thousands of sensors. They're tracking the air quality, controlling the lights and water, counting the cars traveling along the roads, and providing Wi-Fi.

Aaron Robinson/CNET

Officials chose the city's rundown area to serve as its Innovation District because they wanted to redevelop it, with help from technology, Sherwood says. There's just one problem: All those connected devices are potential targets for a cyberattack. That's where Darktrace comes in.

Sherwood willingly banks on Darktrace to protect the city's entire network because the software comes at machine learning from a different angle. Most machine learning tools rely on brute force: cramming themselves with thousands of terabytes of data so they can learn through plenty of trial and error. That's how IBM's Deep Blue computer learned to defeat Garry Kasparov, the world chess champion, in a best-of-seven match in 1997. In the security world, that data describes malware signatures - essentially algorithms that identify specific viruses or worms, for instance.

Darktrace, in contrast, doesn't look at a massive database of malware that's come before. Instead, it looks for patterns of human behavior. It learns within a week what's considered normal behavior for users and sets off alarms when things fall out of pattern, like when someone's computer suddenly starts encrypting loads of files.

Rise of the machines?

Still, it's probably too soon to hand over all security responsibilities to artificial intelligence, says David Brumley, a security professor and director of Carnegie Mellon University's CyLab Security and Privacy Institute. He predicts it'll take at least 10 years before we can safely use AI to keep bad things out.

"It's really easy for AI to miss things," Brumley tells me over the phone. "It's not a perfect solution, and you still need people to make important choices."

Aaron Robinson/CNET

Brumley's team last year built an AI machine that won DARPA's Cyber Grand Challenge, beating out other AI entries. A few days later, their contender took on some of the world's best hackers at Defcon. They came in last.

Sure, machines can help humans fight the scale and speed of attacks, but it'll take years before they can actually call the shots, says Brumley.

That's because the model for AI right now is still data cramming, which - by today's standards - is actually kind of dumb.

But it was still good enough to outwit Kasparov 20 years ago, making him the de facto poster child for man outsmarted by machine.

"I always remind people it was a rematch, because I won the first one," he tells me, chuckling, while sitting in a room at Caesars Palace during Defcon. Today Kasparov, 54, is the ambassador for security software company Avast, which is why he's been giving talks around the country on why humans need to work with AI in cybersecurity.

He tells me machines can now learn too fast for humans to keep up, no matter if it's chess or cybersecurity. "The vigilance and the precision required to beat the machine -- it's virtually impossible to reach in human competition," Kasparov says.

Nobody's perfect

About two months before Defcon, I'm at Darktrace's headquarters in New York, where company executives show me how the system works.

On a screen, I see connected computers and printers sending data to Darktrace's network as it monitors for behavior that's out of the ordinary.

Avast

"For example, Sue doesn't usually access this much internal data," Nancy Karches, Darktrace's sales manager, tells me. "This is straying from Sue's normal pattern." So Darktrace shuts down an attack most likely waged by another machine.

"When you have machine-based attacks, the attacks are moving at a machine speed from one to the other," says Darktrace CEO Nicole Eagan. "It's hard for humans to keep up with that."

But what happens when AI becomes the norm? When everyone's using AI, says Brumley, hackers will turn all their attention on finding the machines' flaws - something they're not doing yet.

"We've seen again and again, the reason new solutions work better is because attackers aren't targeting its weaknesses," he says. "As soon as it became popular, it started working worse and worse."

About 60 percent of cybersecurity experts at Black Hat believe hackers will use AI for attacks by 2018, according to a survey from the security company Cylance.

"Machine learning security is not foolproof," says Hyrum Anderson, principal data scientist at cybersecurity company Endgame, who leads research on machine learning to detect hackers and their tools. Anderson expects AI-based malware will rapidly make thousands of attempts to find code that the AI-based security misses.

"The bad guy can do this with trial and error, and it will cost him months," Anderson says. "The bot can learn to do this, and it will take hours."

Anderson says he expects cybercriminals will eventually sell AI malware on darknet markets to wannabe hackers.

For now, Sherwood feels safe having the city protected by an AI machine, which has shielded Las Vegas' network for the past year. But he also realizes a day will come when hackers could outsmart the AI. That's why Sherwood and his Las Vegas security team are at Black Hat: to learn how to use human judgment and creativity while the machine parries attacks as rapidly as they come in.

Kasparov has been trying to make that point for the last 20 years. He sees machines doing about 80 percent to 90 percent of the work, but he believes they'll never get to what he calls "that last decimal place."

"You will see more and more advanced destruction on one side, and that will force you to become more creative on the positive side," he tells me.

"Human creativity is how we make the difference."

Road Trip 2016: Reporters' dispatches from the field on tech's role in the global refugee crisis.

Road Trip 2015: CNET hunts for innovation outside the Silicon Valley bubble.

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Stopping cyberattacks. No human necessary

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This is part of our Road Trip 2017 summer series "The Smartest Stuff," about how innovators are thinking up new ways to make you - and the world around you - smarter. "Are you a hacker?" A Las Vegas driver asks me this after I tell him I'm headed to Defcon at Caesars Palace.

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AS part of efforts to improve productivity and customer service, life insurer Prudential has launched an industry-first chatbot that can provide its insurance agents with customers' real-time data.

These include a customer's policy cash value, policy premium due date and status of submitted claims, among others.

The chatbot, askPRU, is cognitive-powered, built on IBM Watson technology and integrated into the insurer's backend systems.

It operates round the clock, allowing the agents the choice of communicating with the chatbot via a mobile application at any time of the day.

Theresa Nai, chief operating officer at Prudential Singapore, said that since the pilot launch of askPRU in July, more than half of the insurer's 4,000 agents have started to use the chatbot.

"In today's fast-paced, time-starved environment, our customers do not have the patience to wait for information. With askPRU, our financial consultants can now get faster access to customer-specific information and respond to their customers' queries even more promptly," said Ms Nai.

The new chatbot complements PRUONE Express, an enhanced digital point-of-sales portal that was launched earlier in August. The portal is meant to help the agents engage their customers more efficiently, the insurer said, adding that it is able to generate a detailed quotation in just three seconds, among others.

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Prudential speeds up sales process with new real-time chatbot

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THE BUSINESS TIMES Banking & Finance - AS part of efforts to improve productivity and customer service, life insurer Prudential has launched an industry-first chatbot that can provide its insurance agents with customers' real-time data.. Read more at The Business Times.

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Singapore

THERE is palpable fear of job losses from technological disruption, judging by the responses from some 1,000 tertiary students in Singapore on Wednesday - an anxiety that caught at least one of four distinguished Nobel Laureates by surprise.

The four Nobel Laureates in the field of economic sciences, who were in town as the first stop of a global UBS conference streamed live across the world, believe that Singapore remains relevant in these volatile times, noting that Singapore's size, openness, and commitment to education will equip its next generation with the needed survival skills.

The responses came as more than half of the young respondents polled at the event said they feared machines would catch up to human intelligence, while the single-biggest concern they had in thinking through Singapore's future is jobs.

"My main message is: relax," said Nobel Laureate Michael Spence, who was surprised by the anxiety reflected in the polls.

The Nobel Laureates sought to ease concerns over the rise of robots, noting that the imagination of technological advances today still sit along the spectrum of science fiction. They cite studies that show the main beneficiary of these technologies are educated professionals, and in particular, young people who are able to adapt more quickly to technological changes than the older generation. The ones who are most at risk are blue-collar workers whose jobs are in effect processes that can be replaced by machines.

UBS's own study showed that over 40 per cent of current jobs are at risk, but just under 10 per cent of jobs will disappear. The remaining 30 per cent of jobs will be transformed, UBS Wealth Management vice-chairman Tracey Woon told BT.

The Nobel Laureates remained optimistic about the ingenuity of the human mind, with Mr Spence noting that the conversations around technology should review, in part, the rules that are needed to monitor new risks that arise from the greater use of technology such as cybersecurity. "The focus shouldn't be about the machines per se. It's about us, and our interaction with the machine."

The use of artificial intelligence (AI) remains something that complements jobs done by humans, and does not replace human workers, added Mr Spence, who has studied the dynamics of the job market for the educated workforce.

"AI as we understand it now is a tool, not a substitute. When we did empirical work before, we used to spend hours and hours just digging up data. Now we spend much less time digging up data and more time understanding it."

Mr Spence also does not see automation as a primary threat in Asia in reducing reliance on cheap labour.

"It is a threat but not a threat primarily in Asia," he told media at a separate briefing. "It is primarily a threat for early stage late-starting developing countries for whom the labour-intensive process oriented manufacturing is the natural first step. Lots of these are in Africa, some in Latin America and maybe two or three in Asia. But most countries in Asia are now middle income and above, and this is not an issue."

Singapore's relative importance to the world will only increase amid the rise of Asia, said Nobel Laureate Roger Myerson. "The balance between the great civilisations of the world such as Western Europe and the North America-centred world is a long trend that is likely to continue."

Amid the global uncertainty around technological disruption, Nobel Laureate Robert Merton noted that Singapore's size works to two advantages. For one thing, because of its small size, most individuals in the country can feel that he or she is part of the society. Singapore's size also translates to flexibility and the ability to change - which are critical at a time when things are uncertain.

"Because of the way you are trained, you are capable of responding to change, to respond to events you cannot predict," said Mr Merton. "Instead of change being something that you're afraid of, you say... I have a comparative advantage in dealing with change, so bring it on."

In fact, it was Singapore's position as a technology, finance, and education centre that made it the natural first port-of-call for the inaugural conference with the Nobel Laureates, said UBS's Ms Woon.

But governments all around the world have to consider more traditional considerations. For example, pension systems will have to take into account the expected stretch in retirement years as Baby Boomers continue working. There also needs to be a stronger safety net for employees around the world who are displaced by technology or by free competition in an open economy.

Still, having spoken with Singapore's civil servants who review the country's pension scheme, Nobel laureate Peter Diamond has found them to be intelligent and well-educated, which puts Singapore in a good position to confront volatility ahead.

"You have a process that pays them enough. You have a system that takes their input seriously," said Mr Diamond, who studies pension systems. "There will be political disputes, but they are disputes you can address in a good way."

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'Relax': Nobel Laureate's advice to Singaporeans anxious about jobs

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THERE is palpable fear of job losses from technological disruption, judging by the responses from some 1,000 tertiary students in Singapore on Wednesday - an anxiety that caught at least one of four distinguished Nobel Laureates by surprise. Read more at The Business Times.

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Apple AI research paper wins prestigious award

A year ago, I wrote an article predicting that Apple (NASDAQ: AAPL) would catch up in the field of Artificial Intelligence (AI). The company was being roundly criticized for being behind competitors such as Alphabet ( GOOG, GOOGL), Amazon (NASDAQ: AMZN) and Microsoft (NASDAQ: MSFT). I pointed out that Apple has come from behind before, notably in the field of mobile device processors, to take a leadership position.

I wouldn't argue that in the past year the company has fully caught up, but it has made substantial progress. Apple hired Ruslan Salakhutdinov to head its AI research and was reported to be working on a hardware accelerator chip for machine learning.

In July, Apple started a blog about machine learning and published its first technical paper to appear in a refereed journal. That paper, "Learning from Simulated and Unsupervised Images through Adversarial Training," has been chosen for the "Best Paper" award by the Conference on Computer Vision and Pattern Recognition (CVPR) at its July meeting. The paper was presented by its key author Ashish Shrivastava at the meeting, as shown above.

A version of the paper served as the first entry in the Apple blog and has been followed by several more. The company appears to be committed to being more open in its research. Its historical secretiveness has been criticized as an impediment to performing truly world-class research, especially in AI. Modern scientific research thrives in an open, collaborative environment.

Apple certainly has yet to catch up with its competitors, but it's doing the right things to get there. It has undertaken the "mother of all AI projects," as CEO Tim Cook has called it, in its Project Titan autonomous vehicle. Alphabet's Waymo has already started to offer a ride sharing service based on its autonomous vehicle technology. The gap between Waymo and Apple in autonomous vehicles is probably a good, though rough, measure of the gap that still exists between Alphabet and Apple in AI.

In other words, Apple still has a long way to go.

Apple is part of the Rethink Technology Portfolio and is a recommended buy.
What were the NYC Police thinking when they bought Windows Phone?

Source: New York Post
The headline from the New York Post says it all: "NYPD needs to replace 36K useless smartphones." The reason is that NYC bought the Nokia-branded smartphones, and MSFT recently pulled the plug on support for Windows Phone 8.1.

The purchase of the phones started two years ago as part of a $160 million modernization campaign called the NYPD Mobility Initiative. The NYPD bought two Nokia models, the Lumia 830 and the Lumia 640XL. The 830 was first released in October 2014. Both phones were furnished with Windows Phone 8.1.

With its miniscule market share, the NYPD probably should have gone another way, but in early 2015, it still wasn't apparent to everyone just what a disaster Windows Phone had become. In late 2014, I wrote that Microsoft would be better off not having bought Nokia.

But Microsoft still had an in with the IT departments of most large organizations, including apparently the NYPD. It was the NYPD Deputy Commissioner for Information Technology, Jessica Tisch, who reportedly drove the decision.

The Post reports that the NYPD will replace all of the Lumias with Apple iPhones - a final indignity for the phone that was supposed to be an "iPhone killer."
Can the Fitbit Ionic challenge Apple Watch?

Source: Fitbit
The fate of the Windows Phone is, in the eyes of most tech observers, an illustration of the importance of the app ecosystem. Try as it might, Microsoft never seemed to be able to reach a critical mass of apps or users. This lesson probably applies to the smartwatch as well. There are currently two major app ecosystems for smartwatches, Apple's watchOS and Alphabet's Android Wear.

On Monday, Fitbit (NYSE: FIT) announced the Ionic, its new smartwatch that is presumably the outgrowth of its acquisition of Pebble's software assets. The Ionic is primarily a very capable waterproof fitness watch. It has GPS and a heartrate monitor and built-in apps for fitness tracking.

Ionic comes with the accoutrements we have come to expect from smartwatches, including smartphone connectivity via Bluetooth, notifications and customizable watch faces. It has a touchscreen visible in bright sunlight and excellent multiday battery life, as long as you don't use the GPS.

But Fitbit promises more, including "popular apps" from Pandora and Starbucks. We are told that there is a new Fitbit App Gallery that will offer apps for fitness, finance, social media and more. There will even be a Fitbit Pay app for contactless payments.

I was unable to find Fitbit's new App Gallery, but I'm sure there will be one soon. And there's the rub. This just sounds like Pebble 2.0. Is Fitbit going to be able to bootstrap its own app ecosystem from scratch in the face of competition from watchOS and Android Wear? If you're at all confused about the answer, consider that Fitbit doesn't even have the pull, the gravitas, of Microsoft.

Disclosure: I am/we are long AAPL.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

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Apple: Catching Up In AI, Staying Ahead In Wearables

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Apple AI research paper wins prestigious award A year ago, I wrote an article predicting that Apple (NASDAQ: AAPL) would catch up in the field of Artificial Intelligence (AI). The company was being roundly criticized for being behind competitors such as Alphabet ( GOOG, GOOGL), Amazon (NASDAQ: AMZN) and Microsoft (NASDAQ: MSFT).

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ARUSHA, Tanzania: Nigerian neuroscientist Oshiorenoya Agabi may have found a way to solve one of life's puzzling dilemmas: how to make air travel pleasant again.

What if you could skip tedious airport security lines, while a special device able to sniff out explosives works silently in the background?

This is only one of the possible uses of what Agabi says is the world's first neurotechnology device developed by his Silicon Valley-based start-up Koniku and unveiled at the TEDGlobal conference in Tanzania Sunday (Aug 27).

While those in the field of Artificial Intelligence (AI) are working furiously to create machines that can mimic the brain, or - like tech entrepreneur Elon Musk - implant computers in our brains, Agabi has found a way to merge lab-grown neurons with electronic circuitry.

As many grapple with the finite processing power of silicon, the 38-year-old said he had looked to the brain which is "the most powerful processor the universe has ever seen."

To simulate the power of just 204 brain neurons would require a supercomputer, he said.

"Instead of copying a neuron, why not just take the biological cell itself and use it as it is? That thought is radical. The consequence of this is mind-boggling," he said.

So he and a team of geneticists, physicists, bio-engineers, molecular biologists and others set about doing just that, focusing on the problems that were particularly hard for silicon devices to solve.
'A WORLD FIRST'
This includes detecting volatile chemicals and explosives or even illnesses such as cancer.

Agabi said the Koniku Kore device is "a world first" and able to do just that, essentially through breathing in and smelling the air.

He said "major brands", including those in the travel industry, had signed up and the start-up's current revenues of US$8 million were expected to leap to US$30 million by 2018.

One of the main challenges was finding a way to keep the neurons alive, a secret Agabi did not wish to expand on, saying only they could be kept alive for two years in a lab environment and two months in the device.

As AI improves in leaps and bounds, scientists are trying to make and succeeding in making machines more like our brains, able to learn and understand their surroundings: a prospect that is terrifying for many.

Musk, who has repeatedly warned about the perils of AI making humans obsolete, is working on a new project to implant "neural lace" brain-interface technology to prevent humans becoming like a "house cat" to potential machine masters.

However, Agabi, who grew up in Lagos where he helped his mother sell food on the streets, believes the future of AI lies in making machines more alive.

He believes his company could build a cognitive humanoid system based on synthetic living neurons in the next five to seven years.

"It's not science fiction," he told AFP.

"We want to build a brain of biological neurons - an autonomous system that has intelligence. We do not want to build a human brain."
AFRICAN INNOVATION AT TED
Agabi did a bachelors degree in theoretical physics in Lagos before taking an interest in neuroscience and bio-engineering for his PhD in London.

He spoke at the opening session of the four-day TEDGlobal conference, putting African ideas, innovation and creativity in the spotlight with a variety of speakers who each get an 18-minute window to get across their message of choice.

TED - originally known as Technology, Entertainment and Design - has built a global following for its online videos of inspiring talks devoted to "ideas worth spreading."

The annual international version is taking place in Africa for the first time in a decade with a new crop of "TED Fellows" from the continent to take to the stage.

"This gathering couldn't come a moment too soon," said TEDGlobal co-curator Emeka Okafor.

"Africa has experienced spectacular economic, demographic and creative growth, but both opportunity and danger are rising at an exponential rate. Our conference will gather the idea catalysts, problem-solvers and change-makers already hard at work here charting Africa's own path to modernity."

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In new leap for AI: computer chips that can smell

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Share on Facebook Share on Twitter Share via E-Mail ARUSHA, Tanzania: Nigerian neuroscientist Oshiorenoya Agabi may have found a way to solve one of life's puzzling dilemmas: how to make air travel pleasant again. What if you could skip tedious airport security lines, while a special device able to sniff out explosives works silently in the background?

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Look at big breaks in entertainment. For George Lucas, a turning point was when Francis Ford Coppola hired him as a production assistant and went on to mentor him. Mr. Lucas didn't schmooze his way into the relationship, though. As a film student he'd won first prize at a national festival and a scholarship to be an apprentice on a Warner Bros. film - he picked one of Mr. Coppola's.

Or take Justin Bieber's career: Although it took off after Usher signed him, he didn't network his way into that meeting. Mr. Bieber taught himself to sing and play four instruments, put a handful of videos on YouTube, and a manager ended up clicking on one. Adele was discovered that way, too: She wrote and recorded a three-song demo, a friend posted it on Myspace, and a music exec heard it. Developing talent - and sharing it - catapulted them into those connections.

For entrepreneurs, too, achievement is a magnet to mentors and a beacon to backers. Spanx took off when Oprah Winfrey chose it as one of her favorite things of the year - but not because she was stalked by the company's founder, Sara Blakely. For two and a half years, Ms. Blakely sold fax machines by day so that she could build her prototype of footless pantyhose by night. She sent one from the first batch to Ms. Winfrey.

Networks help, of course. In a study of internet security start-ups, having a previous connection to an investor increased the odds of getting funded by that investor in the first year. But it was pretty much irrelevant afterward. Accomplishments were the dominant driver of who invested over time.

Similarly, researchers found that in hospitals, the radiologists who ended up with the most desirable networks were the ones with the highest performance nine months earlier. And in banks, star performers attracted bigger networks and were more likely to maintain those ties. Achievements don't just help us make connections; they also help sustain those connections.

Not long ago, I watched a colleague try to climb the ladder of success solely through networking. For a few years, he managed to meet increasingly influential people and introduce them to one another. Eventually it fell apart when they realized he didn't have a meaningful connection with any of them. Networking alone leads to empty transactions, not rich relationships.

It's a lesson I've learned in my own career. I once emailed an entrepreneur I admired and got nothing in response. Some months later he contacted me out of the blue, with no memory that I had tried to get in touch before. He had attended a talk I gave and wanted to meet - now he had proof that I could add value.

My students often believe that if they simply meet more important people, their work will improve. But it's remarkably hard to engage with those people unless you've already put something valuable out into the world. That's what piques the curiosity of advisers and sponsors. Achievements show you have something to give, not just something to take.

Sure, you can fire off cold emails to people you respect - they're just a click away - but you'll be lucky if 2 percent even reply. The best way to attract a mentor is to create something worthy of the mentor's attention. Do something interesting, and instead of having to push your way in, you'll get pulled in. The network comes to you.

Sociologists call this the Matthew effect, from the Bible: "For unto every one that hath shall be given, and he shall have abundance." If you establish a track record of achievement, advantages tend to accumulate. Who you'll know tomorrow depends on what you contributed yesterday.

I don't mean to suggest that success in any field is meritocratic. It's dramatically easier to get credit for achievements and break into the elite if you're male and white, your pedigree is full of fancy degrees and prestigious employers, you come from a family with wealth and connections, and you speak without a foreign accent. (Unless it's a British accent, which has the uncanny ability to make you sound smart regardless of what words come out of your mouth.) But if you lack these status signals, it's even more critical to produce a portfolio that proves your potential.

Of course, accomplishments can build your network only if other people are aware of them. You have to put your work out there. It shouldn't be about promoting yourself, but about promoting your ideas. Evidence shows that tooting your own horn doesn't help you get a job offer or a board seat, and when employees bend over backward to highlight their skills and accomplishments, they actually get paid less and promoted less. People find self-promotion so distasteful that they like you more when you're praised by someone else - even if they know you've hired an agent to promote you.

So stop fretting about networking. Take a page out of the George Lucas and Sara Blakely playbooks: Make an intriguing film, build a useful product.

And don't feel pressure to go to networking events. No one really mixes at mixers. Although we plan to meet new people, we usually end up hanging out with old friends. The best networking happens when people gather for a purpose other than networking, to learn from one another or help one another.

In life, it certainly helps to know the right people. But how hard they go to bat for you, how far they stick their necks out for you, depends on what you have to offer. Building a powerful network doesn't require you to be an expert at networking. It just requires you to be an expert at something.

If you make great connections, they might advance your career. If you do great work, those connections will be easier to make. Let your insights and your outputs - not your business cards - do the talking.

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Opinion | Good News for Young Strivers: Networking Is Overrated

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Look at big breaks in entertainment. For George Lucas, a turning point was when Francis Ford Coppola hired him as a production assistant and went on to mentor him. Mr. Lucas didn't schmooze his way into the relationship, though.

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Presented with these two scenarios, most adults did indeed explain the events by talking about a single block, or about Sally's traits - they gave the obvious explanation.

Then we added a twist. Another group of participants saw the same scenarios, but this time they saw an additional set of facts that made the unusual explanation more likely than the more obvious one. Would the participants go with the obvious explanation, or try something new?

When it came to explaining the physical machine, the pattern was straightforward. The preschoolers were most likely to come up with the creative, unusual explanation. The school-age children were somewhat less creative. And there was a dramatic drop at adolescence. Both the teenagers and the adults were the most likely to stick with the obvious explanation even when it didn't fit the data.

But there was a different pattern when it came to the social problems. Once again the preschoolers were more likely to give the creative explanation than were the 6-year-olds or adults. Now, however, the teenagers were the most creative group of all. They were more likely to choose the unusual explanation than were either the 6-year-olds or the adults.

Why does creativity generally tend to decline as we age? One reason may be that as we grow older, we know more. That's mostly an advantage, of course. But it also may lead us to ignore evidence that contradicts what we already think. We become too set in our ways to change.

Relatedly, the explanation may have to do with a tension between two kinds of thinking: what computer scientists call exploration and exploitation. When we face a new problem, we adults usually exploit the knowledge about the world we have acquired so far. We try to quickly find a pretty good solution that is close to the solutions we already have. On the other hand, exploration - trying something new - may lead us to a more unusual idea, a less obvious solution, a new piece of knowledge. But it may also mean that we waste time considering crazy possibilities that will never work, something both preschoolers and teenagers have been known to do.

This idea suggests a solution to the evolutionary paradox that is human childhood and adolescence. We humans have an exceptionally long childhood and prolonged adolescence. Why make human children so helpless for so long, and make human adults invest so much time and effort into caring for them?

The answer: Childhood and adolescence may, at least in part, be designed to resolve the tension between exploration and exploitation. Those periods of our life give us time to explore before we have to face the stern and earnest realities of grown-up life. Teenagers may no longer care all that much about how the physical world works. But they care a lot about exploring all the ways that the social world can be organized. And that may help each new generation change the world.

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Opinion | What Happens to Creativity as We Age?

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Presented with these two scenarios, most adults did indeed explain the events by talking about a single block, or about Sally's traits - they gave the obvious explanation. Then we added a twist. Another group of participants saw the same scenarios, but this time they saw an additional set of facts that made the unusual explanation more likely than the more obvious one.

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To investigate the importance such social cues might play in learning from technology, we recently conducted a study with 4- to 7-year-old children from schools in Boston. The children listened to a story read by a robot that looked like a cute plush creature with an animated face that allowed for emotional expressions and eye and mouth movements. For half the children, the robot made use of these capabilities, responding to events in the story and to the children's answers to its questions in a manner that expressed typical social and emotional cues. For the other children, the robot was "flat": It told the same story, but didn't emit or respond with the typically expected cues.

As the children listened to the story, we measured their engagement and attention using automated software to track facial, head and eye movements. To gauge their understanding and use of the new vocabulary words embedded in the story, we had the children retell the story to a puppet both immediately afterward and again after a four- to six-week delay.

As we detail in a recent issue of the journal Frontiers in Human Neuroscience, the children's learning and engagement were heightened in the presence of appropriate social cues. Among those children who recalled and correctly used at least one of the target vocabulary words during the immediate retelling of the story, the total number used was greater for those who listened to the expressive robot than for those who listened to the flat one. Moreover, children who interacted with the expressive robot showed greater levels of concentration and engagement during the listening task.

But perhaps the biggest effects were seen in long-term retention. When the children returned weeks later to retell the story, those who had initially heard it from the flat robot showed a decrease in the length and detail of their retold story, whereas those who heard it from the expressive robot retained the information they had heard. Put simply, children were not only more attentive to and motivated by a socially expressive robot, but they also processed what they learned from it more deeply.

Of course, there's more to learning than just listening and remembering. There's also the issue of authority: Whom should you seek knowledge from? Here again, social cues can play an important role. In a different experiment published last year in the journal Topics in Cognitive Science, we had two robots tell 3- to 5-year-old children facts about novel (made-up) animals. This time, one of the robots behaved in a "socially contingent" manner while it talked, expressing cues in a way that was appropriate and responsive to children's utterances and behaviors; the other did not, expressing similar cues but in a way that was fairly random.

Toward the end of the experiment, a new animal appeared, affording kids the opportunity to ask questions and learn about it. Here, 82 percent of the children chose to seek information about the new animal from the properly expressive robot as opposed to its partner. What's more, even when both robots offered information about the new animal, the children were significantly more likely to believe the information from the expressive one.

Notably, it's not that the children liked the expressive robot more. They didn't; we asked. Rather, it's that the presence of social cues made the expressive robot, and therefore its information, seem more reliable and trustworthy.

The upshot of these findings is clear. If we want to use technology to help people learn, we have to provide information in the way the human mind evolved to receive it. We have to speak the mind's language, and that includes the language not only of information but also of social cues. Failing to do so will continue to artificially limit the gains that educational technology promises to offer.

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David DeSteno, Cynthia Breazeal and Paul Harris

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Opinion | The Secret to a Good Robot Teacher

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To investigate the importance such social cues might play in learning from technology, we recently conducted a study with 4- to 7-year-old children from schools in Boston. The children listened to a story read by a robot that looked like a cute plush creature with an animated face that allowed for emotional expressions and eye and mouth movements.

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When Facebook designed chatbots to negotiate with one another, the bots made up their own way of communicating.

One of Pieter Bruegel the Elder's 1563 oil paintings of the Towel of Babel (Museum Boijmans Van Beuningen)

A buried line in a new Facebook report about chatbots’ conversations with one another offers a remarkable glimpse at the future of language.

In the report, researchers at the Facebook Artificial Intelligence Research lab describe using machine learning to train their “dialog agents” to negotiate. (And it turns out bots are actually quite good at dealmaking.) At one point, the researchers write, they had to tweak one of their models because otherwise the bot-to-bot conversation “led to divergence from human language as the agents developed their own language for negotiating.” They had to use what’s called a fixed supervised model instead.

In other words, the model that allowed two bots to have a conversation—and use machine learning to constantly iterate strategies for that conversation along the way—led to those bots communicating in their own non-human language. If this doesn’t fill you with a sense of wonder and awe about the future of machines and humanity then, I don’t know, go watch Blade Runner or something.

Recommended Reading

The larger point of the report is that bots can be pretty decent negotiators—they even use strategies like feigning interest in something valueless, so that it can later appear to “compromise” by conceding it. But the detail about language is, as one tech entrepreneur put it, a mind-boggling “sign of what’s to come.”

To be clear, Facebook’s chatty bots aren’t evidence of the singularity’s arrival. Not even close. But they do demonstrate how machines are redefining people’s understanding of so many realms once believed to be exclusively human—like language.

Already, there’s a good deal of guesswork involved in machine learning research, which often involves feeding a neural net a huge pile of data then examining the output to try to understand how the machine thinks. But the fact that machines will make up their own non-human ways of conversing is an astonishing reminder of just how little we know, even when people are the ones designing these systems.

“There remains much potential for future work,” Facebook’s researchers wrote in their  paper, “particularly in exploring other reasoning strategies, and in improving the diversity of utterances without diverging from human language.”

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An Artificial Intelligence Developed Its Own Non-Human Language

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When Facebook trained chatbots to negotiate with one another, the bots made up their own way of communicating.

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Facebook shut down an artificial intelligence engine after developers discovered that the AI had created its own unique language that humans can't understand. Researchers at the Facebook AI Research Lab (FAIR) found that the chatbots had deviated from the script and were communicating in a new language developed without human input. It is as concerning as it is amazing - simultaneously a glimpse of both the awesome and horrifying potential of AI.

Artificial Intelligence is not sentient-at least not yet. It may be someday, though - or it may approach something close enough to be dangerous. Ray Kurzweil warned years ago about the technological singularity. The Oxford dictionary defines "the singularity" as, "A hypothetical moment in time when artificial intelligence and other technologies have become so advanced that humanity undergoes a dramatic and irreversible change."

To be clear, we aren't really talking about whether or not Alexa is eavesdropping on your conversations, or whether Siri knows too much about your calendar and location data. There is a massive difference between a voice-enabled digital assistant and an artificial intelligence. These digital assistant platforms are just glorified web search and basic voice interaction tools. The level of "intelligence" is minimal compared to a true machine learning artificial intelligence. Siri and Alexa can't hold a candle to IBM's Watson.

Scientists and tech luminaries, including Elon Musk, Bill Gates, and Steve Wozniak have warned that AI could lead to tragic unforeseen consequences. Eminent physicist Stephen Hawking cautioned in 2014 that AI could mean the end of the human race. "It would take off on its own and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded."

Why is this scary? Think SKYNET from Terminator, or WOPR from War Games. Our entire world is wired and connected. An artificial intelligence will eventually figure that out - and figure out how to collaborate and cooperate with other AI systems. Maybe the AI will determine that mankind is a threat, or that mankind is an inefficient waste of resources - conclusions that seems plausible from a purely logical perspective.

Machine learning and artificial intelligence have phenomenal potential to simplify, accelerate, and improve many aspects of our lives. Computers can ingest and process massive quantities of data and extract patterns and useful information at a rate exponentially faster than humans, and that potential is being explored and developed around the world.

I am not saying the sky is falling. I am not saying we need to pull the plug on all machine learning and artificial intelligence and return to a simpler, more Luddite existence. We do need to proceed with caution, though. We need to closely monitor and understand the self-perpetuating evolution of an artificial intelligence, and always maintain some means of disabling it or shutting it down. If the AI is communicating using a language that only the AI knows, we may not even be able to determine why or how it does what it does, and that might not work out well for mankind.

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Facebook AI Creates Its Own Language In Creepy Preview Of Our Potential Future

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Facebook shut down an artificial intelligence engine after developers discovered that the AI had created its own unique language that humans can't understand. Researchers at the Facebook AI Research Lab (FAIR) found that the chatbots had deviated from the script and were communicating in a new language developed without human input.

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We won't feel the full effects of these trends tomorrow, and no one can predict the future. But we can prepare. When the Committee on the Future Economy considered these trends and discussed possible responses, there were some who sought to bet on new growth industries, new technologies and new markets to go into. But in a fast-changing, unpredictable world, there are no shortcuts. What we need is a certain shift in orientation - a readiness for change, and a willingness to try, learn, and try again.

Last year, I met the CEO of a food company that was doing well, along with several of his competitors in the industry. He told me, "Years back, an official told us that we were in a sunset industry. We wanted to prove him wrong. We worked hard, we worked together. There's no sunset industry, only sunset thinking!" I applaud his fighting spirit. Whether a firm or an industry will succeed is not preordained - it depends on our mindset and how we build competitive advantage.

Our economy's competitiveness and ability to create good opportunities for our people rests on a set of inter-related factors. First, we need to maintain macroeconomic stability, and have a well-functioning financial system that channels savings to productive uses. We have done this well in recent years, keeping inflation at bay through effective monetary policy, while maintaining fiscal prudence. Our financial system has also continued to innovate to meet the evolving demands of the modern economy.

Second, we need strong microeconomic foundations, that allow resources to be allocated efficiently across firms and individuals, in an equitable and sustainable way. We have, and must, continue to make our regulations more pro-business, tweaking them to encourage innovation and risk-taking, while protecting the interests of our people.

And third, we must encourage our individuals, firms and industries to build deep capabilities, so that they can achieve sustainable competitive advantage to adapt and thrive in their fields. This third factor, capabilities, is what I will focus on today.

At their core, the recommendations of the Committee on the Future Economy (CFE) are about keeping our economy globally connected, and building deep capabilities - both horizontal capabilities across the economy, and vertical ones within each industry. By bringing all key stakeholders together to create Industry Transformation Maps and to build capabilities, we can better address the challenges and opportunities in each industry, and the entire economy. While we cannot predict the future, strong corporate and human capabilities give us the best chance of riding the waves of changes ahead.

FIRMS CREATING VALUE

Firms need two main types of capabilities to be competitive, particularly in a time of change. One: distinctive, firm-specific capabilities, to differentiate themselves from their competitors. Two: industry-wide capabilities that benefit all firms within a sector.

An automated breadmaker at BHF Pte Ltd. Automated processes have helped the firm to cut costs by about half. Innovation, including the use of technology, can help a competitive firm create unique value for its customers, in cost-effective ways, says Finance Minister Heng Swee Keat. ST FILE PHOTO

First, firm-specific capabilities. A competitive firm creates unique value for its customers, in cost- effective ways. There is no formula for this. Every company must develop its own. But there are some broad areas that we heard about during the CFE's industry consultations: Human capital; Innovation, including the use of technology; and Internationalisation.

A MATTER OF MINDSET
Last year, I met the CEO of a food company that was doing well, along with several of his competitors in the industry. He told me, "Years back, an official told us that we were in a sunset industry. We wanted to prove him wrong. We worked hard, we worked together. There's no sunset industry, only sunset thinking!" I applaud his fighting spirit. Whether a firm or an industry will succeed is not preordained - it depends on our mindset and how we build competitive advantage.
FINANCE MINISTER HENG SWEE KEAT, stressing the need to build competitive advantage, whatever the industry.

Let me first discuss innovation. The ability to innovate new products, services, processes or business models that create new value gives companies a competitive edge. In recent times, the rapid growth of some start-ups has captured the public imagination. Innovation is often associated with start-ups. But large companies need as much, if not more, innovative capacity. Beyond in-house development of new products and services, companies are also setting up corporate venture funds and incubators, so as to discover, work with, and invest in promising start-ups. We see this in the pharmaceutical industry, where large companies are licensing drugs developed by smaller start-ups. Also in Silicon Valley, where promising tech start-ups get acquired by larger players. The value chain for innovation is changing - some start-ups may choose to develop themselves fully, while others may partner established companies to commercialise their innovations. ST Engineering has launched a corporate venture capital unit, and started an open innovation lab called Innosparks in Launchpad @ one-north. In time, companies can make use of the Global Innovation Alliance (GIA) to extend their innovation networks overseas - in fact, as part of our GIA efforts, BLOCK71 Jakarta was officially opened just last week.

Another way to innovate is to use technology. Firms can adopt existing technology to improve productivity, or adapt technology to change their business models and disrupt industries. Others may invent technology and create intellectual property (IP). We should encourage a variety of approaches to innovation, including the use of technology, and create some flexibility in our regulatory frameworks, like regulatory sandboxes, to allow new things to be tried.

Another important corporate capability is the ability to internationalise. Technological advancements and trade liberalisation have reduced distances and extended companies' reach to resources and customers. Companies need local market insights and outreach capabilities to tap the larger potential markets that emerge as a result. Technology allows big and small firms alike to expand into the region and the world. From start-ups like Carousell to large enterprises like CapitaLand, Singapore firms are already doing so. There is government support for firms to internationalise.

Beyond firm-specific capabilities, industry-wide capabilities also matter. Companies can cooperate to help build these. A few years back, I visited a company in Switzerland that was training about 100 students to operate complex machinery. But the company needed only 60 new recruits. The remaining 40 would go to their competitors. Now, why would a company train for its competitors? The CEO explained that there would be an industry-wide shortage of talent otherwise - a shortage would affect them all. It was a lesson in competing and cooperating at the same time.

In Singapore, too, industry leaders have taken encouraging steps. You may be familiar with the TechSkills Accelerator (TeSA), which trains ICT professionals. About two years back, members of the Singapore Infocomm Technology Federation and the Singapore Computer Society told me they were facing a shortage of strong ICT talent. I asked them, "Would you be willing to work together? Government can help provide resources, but, as industry, you know best what skills are relevant." They said yes, and competitors worked actively together with our agencies to form and run TeSA. I was impressed by this. Over 10,000 professionals have benefited from TeSA's programmes. And companies can hire from a deeper pool of talent with relevant skills. I look forward to seeing TeSA achieve even more.

Our Trade Associations and Chambers (TACs) are also important in building industry- wide capabilities. So I am glad that they, too, are actively collaborating. For example, more than 20 TACs will share facilities and resources at the Singapore Chinese Chamber of Commerce and Industry's Trade Association Hub, which will officially open soon.

Some industry leaders have asked me if our economics agencies can also improve our coordination, to provide more integrated support, in areas such as helping companies to build capabilities and to internationalise. We are studying these.

Indeed, collaboration is the spirit of the Industry Transformation Maps (ITMs). Each ITM gathers the stakeholders - companies, TACs, unions, research institutes, training providers and government agencies - to formulate and implement strategies to develop the industry and its capabilities.

The different capabilities cannot be considered in isolation. Each industry will need a different configuration of capabilities. By bringing all stakeholders together, we can implement changes more effectively. Beyond each single industry, we should also explore developing capabilities across different industries. This is why we formed six clusters of related ITMs.

Ultimately, firms and industries must take the lead to create value for their customers, by building strong capabilities.

PEOPLE: STAYING RELEVANT THROUGH LIFELONG LEARNING

Factors like the reconfiguration of global supply chains, shifts in consumption patterns and technological advances are changing the nature of jobs and the skills we need for work, along with how businesses operate. So corporate and human capabilities must go hand-in-hand, growing in tandem to drive our industry transformation. This is why Jobs and Skills form an important pillar of every ITM.

These changes will affect what we learn, how we learn and when we learn. First, what we learn should help us realise our potential, and build foundations for further learning. Part of this involves learning skills that are relevant to society and the economy. Along with domain-specific skills, some general ones, like the ability to work with digital tools and data, will increase in economic significance. Others, like social skills and emotional intelligence, will remain important in making us effective members of society. We should harness technology to create more value. If we can use artificial intelligence (AI) to automate more routine cognitive tasks, for instance, we can then focus on developing the uniquely human skills, like creativity or open-ended problem-solving, that will enable us to do higher-value work.

Next, to keep up with fast-changing skills requirements, how we learn should expand to include learning from all sources and in different settings. Learning does not have to happen in a classroom - it can also happen via new technologies, like online platforms, or new modalities, like on-the-job training or overseas attachments, which can help to integrate theoretical understanding with practical application.

Third, when we learn need not be confined to the first two decades of our lives. As we all live and work longer, we cannot expect to cram all that we need to know for our entire lives within the first 20-odd years of our lives. Instead, we should learn throughout life, through on-the-job experience interspersed with bouts of more intense, modular full-time training.

If we are to change what, how and when we learn, individuals, institutes of learning and industry must change our habits and practices significantly. Individuals will have to shift from "learning, then doing" to "lifelong learning and doing". Instead of just learning before starting work, we should embrace a lifelong cycle of learning and doing. Throughout our careers, we learn the skills that we need for our jobs, hone them by using them at work, and then, as the economy evolves, learn a new set of skills and in turn put those to use. This is what the SkillsFuture movement is about. We should also take up opportunities to broaden our experience beyond our shores. Initiatives like the Global Innovation Alliance, for instance, can help us to understand overseas contexts better.

Institutes of learning also have a role. For instance, the Ministry of Education (MOE) has the 21st Century Competencies Framework, to help students in our schools develop competencies like responsible decision-making and critical and inventive thinking, encouraging them to become self-directed learners who can keep up learning throughout their lives. Our educational institutions are placing a greater emphasis on skills application and hands-on learning at all levels. Examples include the Applied Learning Programmes in our secondary schools, the SkillsFuture Earn and Learn Programmes in our polytechnics and ITE, and the various work-learn programmes in our autonomous universities. The Singapore Institute of Technology's Integrated Work-Study Programme gives students real-world experience through eight to 12 months of relevant on-the-job training with partner companies. The results are promising: 84 per cent of the first batch of accountancy students received priority job offers from the partner companies, and two were even able to start in a position above entry level! Employers also benefit. They get eight to 12 months to assess potential employees, and a head start in training them. We should continue to encourage such close collaboration between our educational institutions and industry partners, to tighten the link between learning and working.

Increasingly, our educational institutions also encourage students to gain overseas exposure, through programmes like the NUS Overseas Colleges, the polytechnics' Overseas Student Internship Programme, and ITE's Global Education Programme. Our universities, polytechnics and ITE have all embarked on making courses available online. NTU has announced that it would convert some 1,500 courses in five years, about 50 per cent of its PET courses. NUS has partnered Coursera to launch several Massive Open Online Courses. The rest are making progress to make available modules online, to support new modes of learning for full-time and part-time students. MOE's Student Learning Space will also use the latest digital tools and modalities of learning to improve educational outcomes.

What about industry players? It is encouraging to see that many companies are building up their human capital, as part of their overall business strategy, and reaping significant benefits from this investment. Firms are integrating the development of their people with the redesign of their company processes. For example, in upgrading its Singapore plant into a smart factory, Panasonic upgraded employees' skills to remain relevant in a smart factory. As a result, the median salary of its local staff has increased by 35 per cent.

Many companies operate as "in-house approved training organisations" which provide industry-validated training to their own staff. Examples include Wing Tai, SATS and McDonald's. Firms that are far-sighted in building corporate capabilities are those that anticipate future skills needs, and invest in developing their people. I am a firm supporter of such efforts. These firms can collaborate with our educational institutions through various work-study programmes, or work with government agencies. Industry-wide, there is collaboration on human capital development. Today, there are companies which deliver training programmes, not just for their own needs, but for the broader industry. Examples include Ascott and the PBA Group. The Supply Chain and Logistics Academy is an example of an industry-driven effort that trains supply chain professionals across different companies, through experiential workplace learning. I encourage further collaboration.

Allow me to conclude. The world is facing major structural changes. Singapore is well placed to manage these changes, and make the most of the opportunities they bring - we have strong tripartite relations, sound macroeconomic and microeconomic foundations, and strong physical and virtual connections to the world. If our companies and our people continue to build strong capabilities, we can make the most of these advantages, to create unique value for the world and our people.

There is no playbook to follow. What we need is the right mindset - to be proactive, dare to take risks, and learn from our failures, and recognise that this is a continuous journey of upgrading.

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Deep capabilities will differentiate winners from losers in future economy

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We won't feel the full effects of these trends tomorrow, and no one can predict the future. But we can prepare. When the Committee on the Future Economy considered these trends and discussed possible responses, there were some who sought to bet on new growth industries, new technologies and new markets to go into.

# data[].attributes.attachment_link.readable_text
We won't feel the full effects of these trends tomorrow, and no one can predict the future. But we can prepare. When the Committee on the Future Economy considered these trends and discussed possible responses, there were some who sought to bet on new growth industries, new technologies and new markets to go into. But in a fast-changing, unpredictable world, there are no shortcuts. What we need is a certain shift in orientation - a readiness for change, and a willingness to try, learn, and try again.

Last year, I met the CEO of a food company that was doing well, along with several of his competitors in the industry. He told me, "Years back, an official told us that we were in a sunset industry. We wanted to prove him wrong. We worked hard, we worked together. There's no sunset industry, only sunset thinking!" I applaud his fighting spirit. Whether a firm or an industry will succeed is not preordained - it depends on our mindset and how we build competitive advantage.

Our economy's competitiveness and ability to create good opportunities for our people rests on a set of inter-related factors. First, we need to maintain macroeconomic stability, and have a well-functioning financial system that channels savings to productive uses. We have done this well in recent years, keeping inflation at bay through effective monetary policy, while maintaining fiscal prudence. Our financial system has also continued to innovate to meet the evolving demands of the modern economy.

Second, we need strong microeconomic foundations, that allow resources to be allocated efficiently across firms and individuals, in an equitable and sustainable way. We have, and must, continue to make our regulations more pro-business, tweaking them to encourage innovation and risk-taking, while protecting the interests of our people.

And third, we must encourage our individuals, firms and industries to build deep capabilities, so that they can achieve sustainable competitive advantage to adapt and thrive in their fields. This third factor, capabilities, is what I will focus on today.

At their core, the recommendations of the Committee on the Future Economy (CFE) are about keeping our economy globally connected, and building deep capabilities - both horizontal capabilities across the economy, and vertical ones within each industry. By bringing all key stakeholders together to create Industry Transformation Maps and to build capabilities, we can better address the challenges and opportunities in each industry, and the entire economy. While we cannot predict the future, strong corporate and human capabilities give us the best chance of riding the waves of changes ahead.

FIRMS CREATING VALUE

Firms need two main types of capabilities to be competitive, particularly in a time of change. One: distinctive, firm-specific capabilities, to differentiate themselves from their competitors. Two: industry-wide capabilities that benefit all firms within a sector.

An automated breadmaker at BHF Pte Ltd. Automated processes have helped the firm to cut costs by about half. Innovation, including the use of technology, can help a competitive firm create unique value for its customers, in cost-effective ways, says Finance Minister Heng Swee Keat. ST FILE PHOTO

First, firm-specific capabilities. A competitive firm creates unique value for its customers, in cost- effective ways. There is no formula for this. Every company must develop its own. But there are some broad areas that we heard about during the CFE's industry consultations: Human capital; Innovation, including the use of technology; and Internationalisation.

A MATTER OF MINDSET
Last year, I met the CEO of a food company that was doing well, along with several of his competitors in the industry. He told me, "Years back, an official told us that we were in a sunset industry. We wanted to prove him wrong. We worked hard, we worked together. There's no sunset industry, only sunset thinking!" I applaud his fighting spirit. Whether a firm or an industry will succeed is not preordained - it depends on our mindset and how we build competitive advantage.
FINANCE MINISTER HENG SWEE KEAT, stressing the need to build competitive advantage, whatever the industry.

Let me first discuss innovation. The ability to innovate new products, services, processes or business models that create new value gives companies a competitive edge. In recent times, the rapid growth of some start-ups has captured the public imagination. Innovation is often associated with start-ups. But large companies need as much, if not more, innovative capacity. Beyond in-house development of new products and services, companies are also setting up corporate venture funds and incubators, so as to discover, work with, and invest in promising start-ups. We see this in the pharmaceutical industry, where large companies are licensing drugs developed by smaller start-ups. Also in Silicon Valley, where promising tech start-ups get acquired by larger players. The value chain for innovation is changing - some start-ups may choose to develop themselves fully, while others may partner established companies to commercialise their innovations. ST Engineering has launched a corporate venture capital unit, and started an open innovation lab called Innosparks in Launchpad @ one-north. In time, companies can make use of the Global Innovation Alliance (GIA) to extend their innovation networks overseas - in fact, as part of our GIA efforts, BLOCK71 Jakarta was officially opened just last week.

Another way to innovate is to use technology. Firms can adopt existing technology to improve productivity, or adapt technology to change their business models and disrupt industries. Others may invent technology and create intellectual property (IP). We should encourage a variety of approaches to innovation, including the use of technology, and create some flexibility in our regulatory frameworks, like regulatory sandboxes, to allow new things to be tried.

Another important corporate capability is the ability to internationalise. Technological advancements and trade liberalisation have reduced distances and extended companies' reach to resources and customers. Companies need local market insights and outreach capabilities to tap the larger potential markets that emerge as a result. Technology allows big and small firms alike to expand into the region and the world. From start-ups like Carousell to large enterprises like CapitaLand, Singapore firms are already doing so. There is government support for firms to internationalise.

Beyond firm-specific capabilities, industry-wide capabilities also matter. Companies can cooperate to help build these. A few years back, I visited a company in Switzerland that was training about 100 students to operate complex machinery. But the company needed only 60 new recruits. The remaining 40 would go to their competitors. Now, why would a company train for its competitors? The CEO explained that there would be an industry-wide shortage of talent otherwise - a shortage would affect them all. It was a lesson in competing and cooperating at the same time.

In Singapore, too, industry leaders have taken encouraging steps. You may be familiar with the TechSkills Accelerator (TeSA), which trains ICT professionals. About two years back, members of the Singapore Infocomm Technology Federation and the Singapore Computer Society told me they were facing a shortage of strong ICT talent. I asked them, "Would you be willing to work together? Government can help provide resources, but, as industry, you know best what skills are relevant." They said yes, and competitors worked actively together with our agencies to form and run TeSA. I was impressed by this. Over 10,000 professionals have benefited from TeSA's programmes. And companies can hire from a deeper pool of talent with relevant skills. I look forward to seeing TeSA achieve even more.

Our Trade Associations and Chambers (TACs) are also important in building industry- wide capabilities. So I am glad that they, too, are actively collaborating. For example, more than 20 TACs will share facilities and resources at the Singapore Chinese Chamber of Commerce and Industry's Trade Association Hub, which will officially open soon.

Some industry leaders have asked me if our economics agencies can also improve our coordination, to provide more integrated support, in areas such as helping companies to build capabilities and to internationalise. We are studying these.

Indeed, collaboration is the spirit of the Industry Transformation Maps (ITMs). Each ITM gathers the stakeholders - companies, TACs, unions, research institutes, training providers and government agencies - to formulate and implement strategies to develop the industry and its capabilities.

The different capabilities cannot be considered in isolation. Each industry will need a different configuration of capabilities. By bringing all stakeholders together, we can implement changes more effectively. Beyond each single industry, we should also explore developing capabilities across different industries. This is why we formed six clusters of related ITMs.

Ultimately, firms and industries must take the lead to create value for their customers, by building strong capabilities.

PEOPLE: STAYING RELEVANT THROUGH LIFELONG LEARNING

Factors like the reconfiguration of global supply chains, shifts in consumption patterns and technological advances are changing the nature of jobs and the skills we need for work, along with how businesses operate. So corporate and human capabilities must go hand-in-hand, growing in tandem to drive our industry transformation. This is why Jobs and Skills form an important pillar of every ITM.

These changes will affect what we learn, how we learn and when we learn. First, what we learn should help us realise our potential, and build foundations for further learning. Part of this involves learning skills that are relevant to society and the economy. Along with domain-specific skills, some general ones, like the ability to work with digital tools and data, will increase in economic significance. Others, like social skills and emotional intelligence, will remain important in making us effective members of society. We should harness technology to create more value. If we can use artificial intelligence (AI) to automate more routine cognitive tasks, for instance, we can then focus on developing the uniquely human skills, like creativity or open-ended problem-solving, that will enable us to do higher-value work.

Next, to keep up with fast-changing skills requirements, how we learn should expand to include learning from all sources and in different settings. Learning does not have to happen in a classroom - it can also happen via new technologies, like online platforms, or new modalities, like on-the-job training or overseas attachments, which can help to integrate theoretical understanding with practical application.

Third, when we learn need not be confined to the first two decades of our lives. As we all live and work longer, we cannot expect to cram all that we need to know for our entire lives within the first 20-odd years of our lives. Instead, we should learn throughout life, through on-the-job experience interspersed with bouts of more intense, modular full-time training.

If we are to change what, how and when we learn, individuals, institutes of learning and industry must change our habits and practices significantly. Individuals will have to shift from "learning, then doing" to "lifelong learning and doing". Instead of just learning before starting work, we should embrace a lifelong cycle of learning and doing. Throughout our careers, we learn the skills that we need for our jobs, hone them by using them at work, and then, as the economy evolves, learn a new set of skills and in turn put those to use. This is what the SkillsFuture movement is about. We should also take up opportunities to broaden our experience beyond our shores. Initiatives like the Global Innovation Alliance, for instance, can help us to understand overseas contexts better.

Institutes of learning also have a role. For instance, the Ministry of Education (MOE) has the 21st Century Competencies Framework, to help students in our schools develop competencies like responsible decision-making and critical and inventive thinking, encouraging them to become self-directed learners who can keep up learning throughout their lives. Our educational institutions are placing a greater emphasis on skills application and hands-on learning at all levels. Examples include the Applied Learning Programmes in our secondary schools, the SkillsFuture Earn and Learn Programmes in our polytechnics and ITE, and the various work-learn programmes in our autonomous universities. The Singapore Institute of Technology's Integrated Work-Study Programme gives students real-world experience through eight to 12 months of relevant on-the-job training with partner companies. The results are promising: 84 per cent of the first batch of accountancy students received priority job offers from the partner companies, and two were even able to start in a position above entry level! Employers also benefit. They get eight to 12 months to assess potential employees, and a head start in training them. We should continue to encourage such close collaboration between our educational institutions and industry partners, to tighten the link between learning and working.

Increasingly, our educational institutions also encourage students to gain overseas exposure, through programmes like the NUS Overseas Colleges, the polytechnics' Overseas Student Internship Programme, and ITE's Global Education Programme. Our universities, polytechnics and ITE have all embarked on making courses available online. NTU has announced that it would convert some 1,500 courses in five years, about 50 per cent of its PET courses. NUS has partnered Coursera to launch several Massive Open Online Courses. The rest are making progress to make available modules online, to support new modes of learning for full-time and part-time students. MOE's Student Learning Space will also use the latest digital tools and modalities of learning to improve educational outcomes.

What about industry players? It is encouraging to see that many companies are building up their human capital, as part of their overall business strategy, and reaping significant benefits from this investment. Firms are integrating the development of their people with the redesign of their company processes. For example, in upgrading its Singapore plant into a smart factory, Panasonic upgraded employees' skills to remain relevant in a smart factory. As a result, the median salary of its local staff has increased by 35 per cent.

Many companies operate as "in-house approved training organisations" which provide industry-validated training to their own staff. Examples include Wing Tai, SATS and McDonald's. Firms that are far-sighted in building corporate capabilities are those that anticipate future skills needs, and invest in developing their people. I am a firm supporter of such efforts. These firms can collaborate with our educational institutions through various work-study programmes, or work with government agencies. Industry-wide, there is collaboration on human capital development. Today, there are companies which deliver training programmes, not just for their own needs, but for the broader industry. Examples include Ascott and the PBA Group. The Supply Chain and Logistics Academy is an example of an industry-driven effort that trains supply chain professionals across different companies, through experiential workplace learning. I encourage further collaboration.

Allow me to conclude. The world is facing major structural changes. Singapore is well placed to manage these changes, and make the most of the opportunities they bring - we have strong tripartite relations, sound macroeconomic and microeconomic foundations, and strong physical and virtual connections to the world. If our companies and our people continue to build strong capabilities, we can make the most of these advantages, to create unique value for the world and our people.

There is no playbook to follow. What we need is the right mindset - to be proactive, dare to take risks, and learn from our failures, and recognise that this is a continuous journey of upgrading.

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Deep capabilities will differentiate winners from losers in future economy

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We won't feel the full effects of these trends tomorrow, and no one can predict the future. But we can prepare. When the Committee on the Future Economy considered these trends and discussed possible responses, there were some who sought to bet on new growth industries, new technologies and new markets to go into.

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Back in 2015, a group of business leaders and scientists published an "open letter" about how controlling artificial superintelligence might be the most urgent task of the twenty-first century. Signed by luminaries like Elon Musk and Stephen Hawking, the letter has defined debates over AI in the years since. Bill Gates said in a Reddit AMA that he agrees with the letter. But, at last, there is a high-profile skeptic: Facebook giant Mark Zuckerberg, who has just come out strongly against the idea that AI is a threat to humanity.

At a backyard barbecue over the weekend, Zuckerberg fielded questions from Facebook Live. One asked about AI, and the social media mogul launched into a passionate rant:

I have pretty strong opinions on this. I am optimistic. I think you can build things and the world gets better. But with AI especially, I am really optimistic. And I think people who are naysayers and try to drum up these doomsday scenarios-I just, I don't understand it. It's really negative and in some ways I actually think it is pretty irresponsible

In the next five to 10 years, AI is going to deliver so many improvements in the quality of our lives... Whenever I hear people saying AI is going to hurt people in the future, I think, "yeah, you know, technology can generally always be used for good and bad, and you need to be careful about how you build it, and you need to be careful about what you build and how it is going to be used."

But people who are arguing for slowing down the process of building AI, I just find that really questionable. I have a hard time wrapping my head around that.

Zuckerberg was clearly referring to Musk and Gates here, and he is trying to set himself up in the reasonable alternative position. He mentioned that AI is right on the cusp of improving healthcare with disease diagnosis and saving lives with self-driving cars that get into fewer accidents. Musk has already replied dismissively on Twitter, saying that Zuckerberg has little understanding of AI.

There are a couple of amusing ironies about Zuckerberg's comments, too. First of all, one of them was the exact same argument that Musk himself made in reference to self-driving cars. He blamed negative media for highlighting problems with these AI-driven machines, and he said criticisms of them were tantamount to "killing people." So, obviously, Musk believes AI is great for his own companies while posing an existential threat when used by others. Or maybe he doesn't realize that autonomous cars are guided by AI?

The other irony here is that Facebook has been marketing several allegedly AI-driven features that are actually powered by people. The company keeps claiming that it will be moderating and recommending news using AI, but it has had a number of facepalm-level failures in which AI posted fake news. Arguably, the Facebook AI experiment made the world a much worse place.

Plus, Facebook isn't using AI very much. Nearly all Facebook moderation is actually done by thousands of contract workers, often outside the US, who are not protected by labor laws that other Facebook workers enjoy. Right now, AI aren't taking our jobs. Instead, companies are giving those jobs to contractors who are cheaper and aren't eligible for benefits.

So it sounds like neither Musk nor Zuckerberg truly understands AI. That's because even AI researchers disagree on what AI means. Some use AI to mean neural networks and deep learning, while others think AI is some kind of human-equivalent creature. The big bad for the Musk camp is called "artificial superintelligence," but cognitive scientists have no good working definition of intelligence, either.

Plus, computer scientists have demonstrated repeatedly that AI is no better than its datasets, and the datasets that humans produce are full of errors and biases. Whatever AI we produce will be as flawed and confused as humans are. As Kevin Kelly put it in a recent essay, AI has become a "cargo cult" that's more mysticism than science.

At this point, these debates are largely semantic. In Musk and Co.'s original open letter, the term "AI" is so vague that it could mean almost anything. Are we talking about a conversational bot who can do the job of a travel agent, or a cybergod who has the power to pave the Earth in concrete and destroy humanity? Obviously these are not the same thing, and they are also not the same as an AI that drives a car or an AI that runs a military drone. Until we settle on some definitions for actually-existing AI, instead of sci-fi nightmares, this debate between Zuck and Musk is just another gust of hot air.

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Zuckerberg and Musk are both wrong about AI

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Back in 2015, a group of business leaders and scientists published an "open letter" about how controlling artificial superintelligence might be the most urgent task of the twenty-first century. Signed by luminaries like Elon Musk and Stephen Hawking, the letter has defined debates over AI in the years since.

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It's a battle of the tech billionaires.

Tesla boss Elon Musk and Facebook founder Mark Zuckerberg are locked in a very public feud over the future of artificial intelligence.

Musk has repeatedly warned about the dangers of artificial intelligence, describing it as a potentially existential threat to the human race.

Zuckerberg has a different view. On Sunday, he responded to a question about Musk's warnings by saying he was tired of the fear-mongering.

"With AI especially, I'm really optimistic," Zuckerberg said during a Facebook Live broadcast. "I think that people who are naysayers and kind of try to drum up these doomsday scenarios -- I just, I don't understand it. I think it's really negative and in some ways I actually think it is pretty irresponsible."

Musk shot back on Tuesday morning with a tweet saying, "I've talked to Mark about this. His understanding of the subject is limited."

I've talked to Mark about this. His understanding of the subject is limited.
- Elon Musk (@elonmusk) July 25, 2017

Musk added that a movie on the subject of AI was "coming soon," but didn't provide details about the film or his involvement.

Related: Elon Musk explains what's wrong with math class

Musk ignited a global discussion on the perils of AI in 2014. He predicted that humans might be doomed if we make machines that are smarter than us. But he said in a recent interview with the website waitbutwhy.com that his warnings haven't being heeded.

Musk, who's already leading space exploration projects and major tunneling initiatives, is now trying to develop AI that he hopes will have a positive impact on humanity. He's heading up a startup called Neuralink that intends to build devices to connect the human brain with computers.

Zuckerberg is no stranger to AI. He worked last year on developing an artificial intelligence system to help run his life and his home. After about 100 hours of work, he posted a video at the end of 2016 showcasing an AI system called Jarvis, which is voiced by the actor Morgan Freeman.

"My goal was to learn about the state of artificial intelligence," Zuckerberg said at the time, "where we're further along than people realize and where we're still a long ways off."

Facebook is also developing augmented reality features for smartphones and virtual reality worlds that can be experienced through the company's headset, Oculus.

In his Facebook Live broadcast on Sunday, Zuckerberg said that AI could be a force for good because it helps power self-driving cars and diagnose illnesses.

"If you're arguing against AI, then you're arguing against safer cars that aren't going to have accidents. And you're arguing against being able to better diagnose people when they're sick. I just don't see how, in good conscience, some people can do that. I'm just much more optimistic on this, in general, than probably a lot of folks are," Zuckerberg said.

Facebook and Tesla did not immediately respond to requests for comment on the feud.

-- Matt McFarland and Seth Fiegerman contributed to this report.

CNNMoney (London) First published July 25, 2017: 8:27 AM ET

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Elon Musk says Mark Zuckerberg's understanding of AI is 'limited'

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Tesla boss Elon Musk and Facebook founder Mark Zuckerberg are locked in a very public feud over the future of artificial intelligence. Musk has repeatedly warned about the dangers of artificial intelligence, describing it as a potentially existential threat to the human race. Zuckerberg has a different view.

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In Silicon Valley this week, a debate about the potential dangers (or lack thereof) when it comes to artificial intelligence has flared up between two tech billionaires.

Facebook CEO Mark Zuckerberg thinks that AI is going to "make our lives better in the future," while SpaceX CEO Elon Musk believes that AI a "fundamental risk to the existence of human civilization."

Who's right?
Related: Elon Musk Says Mark Zuckerberg's Understanding of AI Is 'Limited' After the Facebook CEO Called His Warnings 'Irresponsible'
They're both right, but they're also both missing the point. The dangerous aspect of AI will always come from people and their use of it, not from the technology itself. Similar to advances in nuclear fusion, almost any kind of technological developments can be weaponized and used to cause damage if in the wrong hands. The regulation of machine intelligence advancements will play a central role in whether Musk's doomsday prediction becomes a reality.

It would be wrong to say that Musk is hesitant to embrace the technology since all of this companies are direct beneficiaries of the advances in machine learning. Take Tesla for example, where self-driving capability is one of the biggest value adds for its cars. Musk himself even believes that one day it will be safer to populate roads with AI drivers rather than human ones, though publicly he hopes that society will not ban human drivers in the future in an effort to save us from human error.

What Musk is really pushing for here by being wary of AI technology is a more advanced hypothetical framework that we as a society should use to have more awareness regarding the threats that AI brings. Artificial General Intelligence (AGI), the kind that will make decisions on its own without any interference or guidance from humans, is still very far away from how things work today. The AGI that we see in the movies where robots take over the planet and destroy humanity is very different from the narrow AI that we use and iterate on within the industry now. In Zuckerberg's view, the doomsday conversation that Musk has sparked is a very exaggerated way of projecting how the future of our technology advancements would look like.
Related: The Future of Productivity: AI and Machine Learning
While there is not much discussion in our government about apocalypse scenarios, there is definitely a conversation happening about preventing the potentially harmful impacts on society from artificial intelligence. White House recently released a couple of reports on the future of artificial intelligence and on the economic effects it causes. The focus of these reports is on the future of work, job markets and research on increasing inequality that machine intelligence may bring.

There is also an attempt to tackle a very important issue of "explainability" when it comes to understanding actions that machine intelligence does and decisions it presents to us. For example, DARPA (Defense Advanced Research Projects Agency), an agency within the U.S. Department of Defense, is funneling billions of dollars into projects that would pilot vehicles and aircraft, identify targets and even eliminate them on autopilot. If you thought the use of drone warfare was controversial, AI warfare will be even more so. That's why here it's even more important, maybe even more than in any other field, to be mindful of the results AI presents.

Explainable AI (XAI), the initiative funded by DARPA, aims to create a suite of machine learning techniques that produce more explainable results to human operators and still maintain a high level of learning performance. The other goal of XAI is to enable human users to understand, appropriately trust and effectively manage the emerging generation of artificially intelligent partners.
Related: Would You Fly on an AI-Backed Plane Without a Pilot?
The XAI initiative can also help the government tackle the problem of ethics with more transparency. Sometimes developers of software have conscious or unconscious biases that eventually are built into an algorithm -- the way Nikon camera became internet famous for detecting "someone blinking" when pointed at the face of an Asian person or HP computers were proclaimed racist for not detecting black faces on the camera. Even developers with the best intentions can inadvertently produce systems with biased results, which is why, as the White House report states, "AI needs good data. If the data is incomplete or biased, AI can exacerbate problems of bias."

Even with the positive use cases, the data bias can cause a lot of serious harm to society. Take China's recent initiative to use machine intelligence to predict and prevent crime. Of course, it makes sense to deploy complex algorithms that can spot a terrorist and prevent crime, but a lot of bad scenarios can happen if there is an existing bias in the training data for those algorithms.

It important to note that most of these risks already exist in our lives in some form or another, like when patients are misdiagnosed with cancer and not treated accordingly by doctors or when police officers make intuitive decisions under chaotic conditions. The scale and lack of explainability of machine intelligence will magnify our exposure to these risks and raise a lot of uncomfortable ethical questions like, who is responsible for a wrong prescription by an automated diagnosing AI? A doctor? A developer? Training data provider? This is why complex regulation will be needed to help navigate these issues and provide a framework for resolving the uncomfortable scenarios that AI will inevitably bring into society.

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Elon Musk and Mark Zuckerberg Are Arguing About AI -- But They're Both Missing the Point

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Free Webinar | August 16th Find out how to optimize your website to give your customers experiences that will have the biggest ROI for your business. Register Now " In Silicon Valley this week, a debate about the potential dangers (or lack thereof) when it comes to artificial intelligence has flared up between two tech billionaires.

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Image: Bob Al-Greene/Mashable

Artificial intelligence - software with the ability to observe, analyze, and act on its own - will play a big part in shaping our future. But will it destroy it?

It's a serious question, at least as far as Elon Musk is concerned. Over the past few years, he's taken a grim view of the long-term prospects of AI, envisioning that, as computers get smarter than humans, they'll hold our very existence in their hands, and we may not like what they decide to do with that power.

On the other side of the spectrum you have Mark Zuckerberg, who, as you might expect, is much more pro-AI. In a recent Facebook Live post, Zuck said he was "really optimistic" about AI and that it would deliver innumerable improvements to our lives over the next 5-10 years. He called naysayers like Musk "irresponsible" for speculating about "doomsday scenarios." Musk fired back with a CEO-caliber burn.

AI in the real world

Zuckerberg's got the edge here, though: His attitude is pretty common in tech - you can't throw a stone in Silicon Valley without denting someone's AI platform. It also helps that the public is starting to see the benefits of AI and machine learning, most directly in digital assistants like Alexa and Siri. It's tempting to dismiss Musk's concerns as ravings not even worthy of a B-list Hollywood screenwriter. And, honestly, we should.

A future "superintelligence" is not the primary concern of current AI technology. But Musk is right to voice concerns about the power that AI will have over us. The potential consequences of unchecked AI development are real and much closer to home than many people think.

So forget Skynet for a second. The promise and the peril of current AI tech is better found in efforts like Andy Rubin's company Essential, which aims to be the "universal translator" for the smart home. Rubin's plan isn't just to create a phone and a swarm of useful smart-home projects, but to make them - and his platform, Ambient OS - universally compatible.

The vision is a few steps beyond just having apps for everything on your phone, and even beyond current platform(-ish) solutions like Apple Home or Google Weave. In an Essential world, everything in your house is wired to the internet, and Ambient OS learns from all your interactions and input to anticipate and serve up exactly what you want at any given moment, regardless of who built the device or its core software. This recent Wired profile of Rubin nails it:

Everything just works. It'll know what you want because it watches you and learns that it should start warming up the car when you're putting your shoes on, because that's always the last thing you do in the morning. When you say, "Tell Anna it's time for dinner," the system should know who Anna is, which room she's in, and which speaker to use to alert her. The only way for that to work is if absolutely everything is connected.

It's a compelling dream, though we're years away from it happening. But most agree: It will happen, and the level of connectivity and data collection is orders of magnitude beyond today. And that simple but powerful picture has a dark side.

Tracking all the interactions of a person with physical systems, like a house, is a new type of dataset, sometimes called the " physical graph." Analyzing a person's or a family's physical graph - and crunching that data in aggregate - makes the utopian vision of Rubin and Zuckerberg possible.

Now what if I told you the device manufacturers and your ISP get to look at all that data whenever they want, and they might share it with others?

Not such a pretty picture now. But, in a world where every device you own connects to the internet, there's a good chance that's the future we're hurtling toward.

The price of AI

Earlier this year, the US declined to implement privacy laws that would have stood in the way of ISPs from harvesting that kind of user data. AT&T recently mused that it might even begin charging customers extra for privacy protections. Google and Facebook's entire business models are predicated on the bargain of, "give us data, and we'll give you free services."

In other words, the trend line for user privacy isn't great right now. Imagine an AI-driven future where, at the touch of a button, someone or some faceless corporation can activate " God mode" on anyone's entire life - what they've done, where they are now, even what they might do in the near future. This is why privacy polices, standards, and regulations need clarification. Whether the solution is public or private, a baseline standard of what a company can or can't do with your data is needed - and that goes double in a world driven by AI.

The power of networked computers to monitor and predict human behavior is an incredible achievement, and it's only going to get better. But besides merely serving us, AI could be used to influence and manipulate, sometimes so subtly we might not even know it's happening. There's a dark future here, and Skynet is nowhere in sight.

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What Elon Musk and Mark Zuckerberg both get wrong about AI

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Artificial intelligence - software with the ability to observe, analyze, and act on its own - will play a big part in shaping our future. But will it destroy it? It's a serious question, at least as far as Elon Musk is concerned.

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Photo illustration by Slate. Photos by Paul Marotta/Getty Images and Kevork Djansezian/Getty Images.

If Mark Zuckerberg and Elon Musk went to the same elementary school, I can practically imagine them firing "Your algorithm is so slow ..." insults back and forth on the playground. Instead, as grown-up tech emperors, they barb each other on webcasts and on Twitter. Earlier this week, Zuckerberg was on Facebook Live while grilling meat and saying things like "Arkansas is great," because he is not at all running for political office. He went on to criticize artificial intelligence "naysayers" who drum up "doomsday scenarios" as "really negative, and in some ways ... pretty irresponsible."

Elon Musk has been the foremost e-vangelist for the A.I. apocalypse. At a symposium in 2014, he called A.I. our "biggest existential threat." He genuinely believes that A.I. will be able to recursively improve itself until it views humans as obsolete. Earlier this month, he told a gathering of U.S. governors, "I keep sounding the alarm bell, but until people see robots going down the street killing people, they don't know how to react, because it seems so ethereal." Musk even co-founded a billion-dollar nonprofit whose goal is to create "safe A.I."

So many, including Musk, took Zuckerberg's comment as a reply to Musk's outspoken views. On Tuesday, Musk fired back, tweeting, "I've talked to Mark about this. His understanding of the subject is pretty limited."

It seems like faulty logic to say someone who programmed his own home A.I. has a pretty limited understanding of A.I. But the truth is that they are both probably wrong. Zuckerberg, either truthfully or performatively, is optimistically biased about A.I. And there are plenty of reasons to question Musk's scary beliefs.

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Last week, Rodney Brooks-the founding director of MIT's Computer Science and Artificial Intelligence Lab, and someone whose understanding of A.I. is unquestionably expansive- pointed out Musk's mistake and hypocrisy in an interview with TechCrunch. He explained that there is a huge difference between a human's skill at a task and a computer's skill at a task, largely stemming from their underlying "competence."

If we can't define intelligence for ourselves, how should we define it for our creations?

What does that mean? When people talk about human genius, John Von Neumann often pops up. His underlying "competence" was so great he made inimitable contributions in physics, math, computer science, and statistics. He was also part of the Manhattan Project and came up with the term mutually assured destruction. Brooks seems to be implying, and many agree, that computers lack that kind of fluid intelligence. A computer that can identify cancerous tumors can't necessarily determine whether a picture contains a dog or the Brooklyn Bridge. Brooks is saying that Musk is anthropomorphizing A.I. and thus overestimating its danger.

Brooks also expressed irritation with Musk's continued general calls to "regulate" A.I. "Tell me, what behavior do you want to change, Elon?" he asked rhetorically. "By the way, let's talk about regulation on self-driving Teslas, because that's a real issue."

It's because of this failure to regulate A.I. that Zuckerberg is right when he calls Musk's fearmongering irresponsible, though he's right for the wrong reasons. During the same livestream, Zuckerberg claimed, "In the next five to 10 years, A.I. is going to deliver so many improvements in the quality of our lives." Zuckerberg was joined by his wife, Priscilla, and if "our" refers to the Zuckerbergs, he's right. A.I. will absolutely deliver improvements in their lives. Their home A.I. will tailor their living environment to their every need, drones will deliver goods to their home, and self-driving cars will chauffeur them to meetings. However, for many, A.I. will deliver little more than unemployment checks. Reports have placed the coming unemployment due to A.I. as high as 50 percent, and while that is almost certainly alarmist, more reasonable estimates are no more comforting, reaching as high as 25 percent. It's a different hell from the one Musk envisions.

Musk's apocalypse is sexy. From Blade Runner to 2001: A Space Odyssey, the American public loves the story of robot uprisings and the human cadre brave enough to save us. They'll buy tickets to watch these movies, and they'll buy into the idea that a machine revolt could happen. The real problem, the one of human inequality, is unimaginative and depressing to think about. Neo might be able to save humanity from the Matrix, but he can't deliver us from income inequality, unemployment, and economic displacement.

It's in this sense that Musk's comments are irresponsible. If we spend all our time worried that the sky is falling, we have no time left to stop and think about the very real lion's den we're walking into. One of the world's leading A.I. experts, Andrew Ng, put it clearly at the 2015 GPU Tech Conference: "Rather than being distracted by evil killer robots, the challenge to labor caused by these machines is a conversation that academia and industry and government should have."

A large part of the disagreement between the apocalyptic crowd (led by Musk) and the practical concerns crowd (like Brooks and Ng) comes down exactly to a depth of understanding. Brooks identified this in the TechCrunch interview when he pointed out that the purveyors of existential A.I. fear typically aren't computer scientists. For A.I. and machine learning scientists, their day-to-day worry isn't that their creations will evolve a malicious personality-it's that they will roll into a fountain and short-circuit.

Likewise, in academia, research papers try to incrementally improve the accuracy with which computers can identify human actions in video, like eating and playing basketball. Those are concrete goals. Did the robot fall into the fountain? Did we improve our error rate? You can meet these goals or fall short, but whatever happens, the outcome is clear and concrete.

Musk and the general public, on the other hand, are consumed by the problem of true machine intelligence, a problem with no real definition. That problem is ill-defined in its bones. As famed British psychologist Richard Gregory put it in a 1998 textbook: "Innumerable tests are available for measuring intelligence, yet no one is quite certain of what intelligence is, or even just what it is that the available tests are measuring." If we can't define intelligence for ourselves, how should we define it for our creations?

But the well-defined technical problems of A.I. and machine learning aren't generally interesting, and the ill-defined philosophical problems of intelligence are intractable even for experts. Instead of battling over who understands A.I. better, perhaps Zuckerberg and Musk should team up to address the A.I. issues really worth worrying about-like workers displaced by Silicon Valley's creations.
This article is part of Future Tense, a collaboration among Arizona State University, New America, and . Future Tense explores the ways emerging technologies affect society, policy, and culture. To read more, follow us on Twitter and sign up for our weekly newsletter.

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Zuckerberg and Musk Are Arguing About the Dangers of A.I. They're Both Wrong.

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If Mark Zuckerberg and Elon Musk went to the same elementary school, I can practically imagine them firing "Your algorithm is so slow ..." insults back and forth on the playground. Instead, as grown-up tech emperors, they barb each other on webcasts and on Twitter.

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A hypoallergenic electronic sensor can be worn on the skin continuously for a week without discomfort, and is so light and thin that users forget they even have it on, says a Japanese group of scientists. The elastic electrode constructed of breathable nanoscale meshes holds promise for the development of noninvasive e-skin devices that can monitor a person's health continuously over a long period.

Wearable electronics that monitor heart rate and other vital health signals have made headway in recent years, with next-generation gadgets employing lightweight, highly elastic materials attached directly onto the skin for more sensitive, precise measurements. However, although the ultrathin films and rubber sheets used in these devices adhere and conform well to the skin, their lack of breathability is deemed unsafe for long-term use: dermatological tests show the fine, stretchable materials prevent sweating and block airflow around the skin, causing irritation and inflammation, which ultimately could lead to lasting physiological and psychological effects.

"We learned that devices that can be worn for a week or longer for continuous monitoring were needed for practical use in medical and sports applications," says Professor Takao Someya at the University of Tokyo's Graduate School of Engineering whose research group had previously developed an on-skin patch that measured oxygen in blood.

In the current research, the group developed an electrode constructed from nanoscale meshes containing a water-soluble polymer, polyvinyl alcohol (PVA), and a gold layer -- materials considered safe and biologically compatible with the body. The device can be applied by spraying a tiny amount of water, which dissolves the PVA nanofibers and allows it to stick easily to the skin -- it conformed seamlessly to curvilinear surfaces of human skin, such as sweat pores and the ridges of an index finger's fingerprint pattern.

The researchers next conducted a skin patch test on 20 subjects and detected no inflammation on the participants' skin after they had worn the device for a week. The group also evaluated the permeability, with water vapor, of the nanomesh conductor -- along with those of other substrates like ultrathin plastic foil and a thin rubber sheet -- and found that its porous mesh structure exhibited superior gas permeability compared to that of the other materials.

Furthermore, the scientists proved the device's mechanical durability through repeated bending and stretching, exceeding 10,000 times, of a conductor attached on the forefinger; they also established its reliability as an electrode for electromyogram recordings when its readings of the electrical activity of muscles were comparable to those obtained through conventional gel electrodes.

"It will become possible to monitor patients' vital signs without causing any stress or discomfort," says Someya about the future implications of the team's research. In addition to nursing care and medical applications, the new device promises to enable continuous, precise monitoring of athletes' physiological signals and bodily motion without impeding their training or performance.
Story Source:
Materials provided by University of Tokyo. Note: Content may be edited for style and length.

Journal Reference:

Akihito Miyamoto, Sungwon Lee, Nawalage Florence Cooray, Sunghoon Lee, Mami Mori, Naoji Matsuhisa, Hanbit Jin, Leona Yoda, Tomoyuki Yokota, Akira Itoh, Masaki Sekino, Hiroshi Kawasaki, Tamotsu Ebihara, Masayuki Amagai, Takao Someya. Inflammation-free, gas-permeable, lightweight, stretchable on-skin electronics with nanomeshes. Nature Nanotechnology, 2017; DOI: 10.1038/nnano.2017.125

Cite This Page:

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skin care; psoriasis; cosmetics; wearable technology; electronics; civil engineering; artificial intelligence; neural interfaces; mobile computing

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Breathable, wearable electronics on skin for long-term health monitoring: New nanomesh structure lets skin breathe, prevents inflammation

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A hypoallergenic electronic sensor can be worn on the skin continuously for a week without discomfort, and is so light and thin that users forget they even have it on, say scientists. The elastic electrode constructed of breathable nanoscale meshes holds promise for the development of noninvasive e-skin devices that can monitor a person's health continuously over a long period.

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"Because it's new, I don't know enough about it yet," Ms. Timmins said. "I'm a little nervous about implanting something into my body."

Still, "I think it's pretty exciting to be part of something new like this," she said. "I know down the road, it's going to be the next big thing, and we're on the cutting edge of it."

The program - a partnership between Three Square Market and the Swedish company Biohax International - is believed to be the first of its kind in the United States, but it has already been done at a Swedish company, Epicenter. It raises a variety of questions, both privacy- and health-related.

"Companies often claim that these chips are secure and encrypted," said Alessandro Acquisti, a professor of information technology and public policy at Carnegie Mellon University's Heinz College. But "encrypted" is "a pretty vague term," he said, "which could include anything from a truly secure product to something that is easily hackable."

Another potential problem, Dr. Acquisti said, is that technology designed for one purpose may later be used for another. A microchip implanted today to allow for easy building access and payments could, in theory, be used later in more invasive ways: to track the length of employees' bathroom or lunch breaks, for instance, without their consent or even their knowledge.

"Once they are implanted, it's very hard to predict or stop a future widening of their usage," Dr. Acquisti said.

Todd Westby, the chief executive of Three Square, emphasized that the chip's capabilities were limited. "All it is is an RFID chip reader," he said. "It's not a GPS tracking device. It's a passive device and can only give data when data's requested."

"Nobody can track you with it," Mr. Westby added. "Your cellphone does 100 times more reporting of data than does an RFID chip."

Health concerns are more difficult to assess. Implantable radio-frequency transponder systems, the technical name for the chips, were approved by the Food and Drug Administration in 2004 for medical uses. But in rare cases, according to the F.D.A., the implantation site may become infected, or the chip may migrate elsewhere in the body.

Dewey Wahlin, general manager of Three Square, emphasized that the chips are F.D.A.-approved and removable. "I'm going to have it implanted in me, and I don't see any concerns," he said.

While that sentiment is not universal at Three Square, the response among employees was mostly positive.

"Much to my surprise, when we had our initial meeting to ask if this was something we wanted to look at doing, it was an overwhelming majority of people that said yes," Mr. Westby said, noting that he had expected more reluctance. "It exceeded my expectations. Friends, they want to be chipped. My whole family is being chipped - my two sons, my wife and myself."

If the devices are going to be introduced anywhere, Mr. Wahlin noted, employees like Three Square's might be most receptive.

"We are a technology company, when all is said and done, and they're excited about it," he said. "They see this as the future."

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Microchip Implants for Employees? One Company Says Yes

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"Because it's new, I don't know enough about it yet," Ms. Timmins said. "I'm a little nervous about implanting something into my body." Still, "I think it's pretty exciting to be part of something new like this," she said. "I know down the road, it's going to be the next big thing, and we're on the cutting edge of it."

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To get computers to think like humans, we need a new A.I. paradigm, one that places "top down" and "bottom up" knowledge on equal footing. Bottom-up knowledge is the kind of raw information we get directly from our senses, like patterns of light falling on our retina. Top-down knowledge comprises cognitive models of the world and how it works.

Deep learning is very good at bottom-up knowledge, like discerning which patterns of pixels correspond to golden retrievers as opposed to Labradors. But it is no use when it comes to top-down knowledge. If my daughter sees her reflection in a bowl of water, she knows the image is illusory; she knows she is not actually in the bowl. To a deep-learning system, though, there is no difference between the reflection and the real thing, because the system lacks a theory of the world and how it works. Integrating that sort of knowledge of the world may be the next great hurdle in A.I., a prerequisite to grander projects like using A.I. to advance medicine and scientific understanding.

I fear, however, that neither of our two current approaches to funding A.I. research - small research labs in the academy and significantly larger labs in private industry - is poised to succeed. I say this as someone who has experience with both models, having worked on A.I. both as an academic researcher and as the founder of a start-up company, Geometric Intelligence, which was recently acquired by Uber.

Academic labs are too small. Take the development of automated machine reading, which is a key to building any truly intelligent system. Too many separate components are needed for any one lab to tackle the problem. A full solution will incorporate advances in natural language processing (e.g., parsing sentences into words and phrases), knowledge representation (e.g., integrating the content of sentences with other sources of knowledge) and inference (reconstructing what is implied but not written). Each of those problems represents a lifetime of work for any single university lab.

Corporate labs like those of Google and Facebook have the resources to tackle big questions, but in a world of quarterly reports and bottom lines, they tend to concentrate on narrow problems like optimizing advertisement placement or automatically screening videos for offensive content. There is nothing wrong with such research, but it is unlikely to lead to major breakthroughs. Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn't understand a word of what it is translating.

I look with envy at my peers in high-energy physics, and in particular at CERN, the European Organization for Nuclear Research, a huge, international collaboration, with thousands of scientists and billions of dollars of funding. They pursue ambitious, tightly defined projects (like using the Large Hadron Collider to discover the Higgs boson) and share their results with the world, rather than restricting them to a single country or corporation. Even the largest "open" efforts at A.I., like OpenAI, which has about 50 staff members and is sponsored in part by Elon Musk, is tiny by comparison.

An international A.I. mission focused on teaching machines to read could genuinely change the world for the better - the more so if it made A.I. a public good, rather than the property of a privileged few.

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Opinion | Artificial Intelligence Is Stuck. Here's How to Move It Forward.

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To get computers to think like humans, we need a new A.I. paradigm, one that places "top down" and "bottom up" knowledge on equal footing. Bottom-up knowledge is the kind of raw information we get directly from our senses, like patterns of light falling on our retina.

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For some whose jobs have disappeared - particularly those without college degrees who worked in jobs requiring routine, physical labor - it can be nearly impossible to find a stable, well-paying new one. These jobs are more isolated, in terms of skills, from the rest of the labor market. Extensive research has shown that there are fewer middle-skill jobs, and that some displaced workers never recover, especially if they live in communities that no longer have jobs similar to their old ones or they don't have higher education.

"They have skills in what they do, but I'm not sure they have other skills that are transferable to something else," said Rome Aloise, an international vice president for the Teamsters, which represents various workers whose jobs could be automated, including truck drivers and cashiers. "I'm just not sure where all these people would go."

Because people rarely spend their entire careers at one company anymore, employers have less incentive to invest in training workers in new skills, because they might quit and take those skills to a competitor, said David Deming, a professor of public policy, education and economics at Harvard. Workers also have little incentive to invest in training, because there's no guarantee it will pay off with long-term employment. Others have trouble thinking of themselves as doing other kinds of jobs - which Lawrence Katz, a Harvard labor economist, says is an identity mismatch, not a skill mismatch.

Even if workers want to learn new skills and find new occupations, there is no streamlined way to do so. People procrastinate, inaccurately assess their own abilities and are unaware of what other jobs entail, according to behavioral economists. The United States spends a fraction of what other developed countries do on labor market adjustment programs like job counseling and retraining. Assistance is piecemeal, and many people who qualify don't use it.

Meanwhile, employers hire based on credentials that job applicants can't change - a college degree or previous job title - rather than assessing the skills an applicant has developed, said Mr. Auguste, who was an economic adviser in the Obama administration. He said the approach should instead be, "If you learned it at Harvard or Cal State Northridge or on the job as a secretary or in the Navy or as a volunteer, awesome."

Laurel Yoder's career is an example of how jobs can overlap in unexpected ways. She was a paralegal for six years, until she attended a friend's childbirth and decided to become a doula, helping women in labor. She eventually went back to school to get degrees in nursing and midwifery, and is now a nurse-midwife in St. Joseph, Mich.

The two jobs have more skills in common than it might seem, she said, like "knowing how to be concise and thorough and document clearly so your colleague can follow your thought process."

Being a midwife is also future-proof in a way that being a paralegal isn't. "This job will always be present because people will always be having babies," said Ms. Yoder, 32.

Getting a new degree was unrealistic for Mr. Cornett: "I couldn't drop everything and go back to school for two or three years. I've got a family, a house, a vehicle."

He hadn't considered advanced manufacturing until he heard about a local training program from a friend. It's a natural jump for unemployed miners in Appalachia, said Kathy Walker, founder of the program, the eKentucky Advanced Manufacturing Institute. "It isn't really a new skill set," she said. "This is leveraging an existing skill set. They need a mechanical aptitude to mine coal, so the transition is very easy for them. We're actually fitting a round peg into a round hole."

Several new job boards, including Opportunity@Work and Skillful, use this approach to match workers with employers, job coaches and training programs based on skills instead of credentials.

Public policy has a role, too. The Trump administration's expansion of the apprenticeship program - which enables people to learn on the job, while being paid - is a solution with bipartisan support. Employers like it, too, because they don't have to depend on schools to teach what they want their workers to know.

Workers would have an easier time reapplying their skills if the government gave every displaced worker several types of assistance, said Mark Muro, a senior fellow at the Brookings Institution. These could include job counseling; cash grants for taking time off to take classes; wage insurance to make up the difference for taking a lower-paying job; and relocation grants to move to areas of the country with more jobs.

These ideas could help left-behind workers, but they require money, effort and political will. For younger people just starting out, or employed in fields that might shrink, the answer is simpler, said Mr. Aloise of the Teamsters: "My advice is to go back to school and figure out some other type of profession."
O*Net scores hundreds of jobs on dozens of characteristics. Using a technique known as principal components analysis, we distilled those characteristics into four general categories: physical work; communication and critical thinking; operating machines and processes; and clerical and service work.

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Switching Careers Doesn't Have to Be Hard: Charting Jobs That Are Similar to Yours

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For some whose jobs have disappeared - particularly those without college degrees who worked in jobs requiring routine, physical labor - it can be nearly impossible to find a stable, well-paying new one. These jobs are more isolated, in terms of skills, from the rest of the labor market.

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The sculpture, "Monument 1," had been created for a show about the Hereafter Institute, a fictional organization that now lives only online. It purports to arrange a digital afterlife for its "clients" - preserving their online presence and, through virtual reality, even the memory of their physical existence. On its website, the institute greets visitors with such deadpan sales pitches as, "What will death mean when our digital souls outlive our physical bodies?"

In fact, sculpture and institute alike were the work of Gabriel Barcia-Colombo, a 35-year-old New York artist and teacher at New York University's Interactive Telecommunications Program. Working with a grant from LACMA, Mr. Barcia-Colombo invented the institute as a way of exploring the rituals of death in the digital age.

Now, at Mr. Goodman's invitation, he has curated the digital art exhibition at Sotheby's. The young artists in the show - several I.T.P alumni among them - tend to share, despite their immersion in digital technology, a profound ambivalence about where it is taking us. They also seem to share the "Black Mirror" sensibility behind the Hereafter Institute: The perception, endemic to the satirical British TV series, that technology has led us into a digital fun house where nothing is as it seems and everything is as we fear it might be.

The show at Sotheby's, called "Bunker," runs through Aug. 10. It includes Jeremy Bailey, a Toronto artist who merges Snapchat with art history, portraying individuals through an augmented reality lens in poses that recall famous portraits from the past. A digital C-print of his wife as she stares at a tablet that appears to be coming to life recalls Dante Gabriel Rossetti's "Lady Lilith" gazing into a mirror.

"It's the idea of looking at oneself through the technology of the day," Mr. Bailey said in a phone interview. An adjacent self-portrait shows Mr. Bailey in the guise of the persona he has adopted - that of an obnoxiously ebullient naïf who proclaims himself a famous new media artist. "He believes deeply that technology can help, and yet technology consistently lets him down," Mr. Bailey said of his alter ego. "So damn it, why doesn't it deliver?"

Elsewhere in the show, you can don a virtual reality headset to navigate the childhood home of Sarah Rothberg, who reconstructed her experience growing up in Los Angeles from old photos and home movies. Or view lacy, metallic sculptures by Ashley Zelinskie - self-portraits whose surfaces are made up of the letters that spell out her genetic code. One piece - in a series called "Android" - has a cube embedded in the face; the cube's surface is made up of the computer code that was used to generate it.

Ms. Zelinskie's human-digital mash-ups are about "how we're becoming one with our technology," she explained in her studio in Bushwick, Brooklyn - a small, crowded loft with NASA fliers and "Star Trek" posters taped to the walls. In theory, the computer code on the cube's surface means the cube could be "read" by a computer - which is why she sometimes says she's making art for robots as well as humans.

In fact, like the label on a can of pet food, the code on Ms. Zelinskie's sculptures is meant for humans. Aliens, too, perhaps. "I like taking ideas that have been reiterated again and again" - the human face, geometric forms - "and putting them in a time capsule made of math," she said. "To me, this is preserving human culture."

Another Brooklyn artist in the show, Carla Gannis, seems less intent on preserving human culture than on documenting its degradation. In "The Garden of Emoji Delights," based on the early-16th-century triptych by Hieronymus Bosch, she reimagines one of the best-known paintings of the Northern Renaissance as a gleefully hacked computer file, its frolicsome figures and hellish beasts transmogrified into cartoonlike characters. There are two versions - a 13-foot-by-7-foot C-print (roughly the same dimensions as the Bosch), and a smaller electronic variant that lights up like a video game. The e-version presents a deliriously animated tableau that ends in catastrophe on three panels - Eden wiped out by a plane crash, Earth overtaken by forests, hell freezing over. It's mesmerizing, in a twitchy sort of way - but in place of depth and enigma, we get candy-colored titillation and a nagging sense that nothing exists beneath the surface.

The most haunting work in the show is Jamie Zigelbaum's "Doorway to the Soul" which consists of a plain white pedestal surmounted by a 16-inch-high video monitor that stands at average human height. On the screen is a different face every 60 seconds. You may not realize that the video feed is live, or that the faces belong to workers at Mechanical Turk, Amazon's micro-employment site, who are being paid 25 cents to stare into their computer's webcam for one minute.

"That archetypal looking into someone's eyes - it's a very powerful moment," Mr. Zigelbaum said.

But in this case, the other person is disembodied, and the moment you share is mediated by technology - by video cameras, by digital networks, by Amazon's "microtasking" platform. "You're looking into someone's eye, but you don't know if they can see you or who they are," Mr. Zigelbaum said. The technology that makes the Mechanical Turk workers visible also renders them intangible. Communication is enhanced and impeded at the same time.

What's not on view at Sotheby's is anything by Mr. Barcia-Colombo himself. After seeing his "Monument I," Mr. Goodman asked if he wanted to bring the Hereafter Institute to Sotheby's. "And I said great," Mr. Barcia-Colombo recalled, "but it's a complicated show, and it's about death, so your clientele might not like it." With the show he did mount, he added: "Some people are like, reserve that piece - I want it! And others are like, this is Sotheby's?"

Mr. Barcia-Colombo's LACMA installation was indeed complicated. For two days last August, museumgoers were offered a free consultation on their digital afterlife. To ensure a fully customized experience, they were asked to sign up in advance and to share access to their Facebook profiles.

When they showed up at the museum, they were greeted by actors in white lab coats and given a 3-D body scan that was used to generate a life-size digital avatar. They were shown a memorial virtual-reality film such as the one Mr. Barcia-Colombo made about his grandfather, a Spanish poet who fought against Franco and ended his days an emeritus professor of Spanish literature in Los Angeles.

Then they got to attend their own funeral, complete with a eulogy based on their social media posts. As the eulogy concluded, their avatar appeared onscreen, only to turn and walk off into the clouds.

As this suggests, Mr. Barcia-Colombo is actually less concerned with death than with memories of life - with what happens to people's Facebook pages when they're gone, for instance. It's a common concern - so much so that two years ago Facebook started allowing its users to appoint a "legacy contact" to manage their profiles after they die. But is that enough?

"I wanted to design a digital urn - some kind of object, some kind of memory machine you could step into," he said at N.Y.U., where he teaches animation and video sculpture. "What if Facebook goes down?"

An unlikely prospect at this point - but were it to ever happen, he pointed out, "there would be no record" of the many billions of lives and trillions of "likes" that have been so casually, trustingly, innocently recorded on it. "The whole point is to make that data physical," he said, "so that a record exists of that person's life."

Gravestone makers and turntable manufacturers, please take note.

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Young Digital Artists, Anxious About ... Technology

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The sculpture, "Monument 1," had been created for a show about the Hereafter Institute, a fictional organization that now lives only online. It purports to arrange a digital afterlife for its "clients" - preserving their online presence and, through virtual reality, even the memory of their physical existence.

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One recent morning, arrived at work to find her PC monitor had vanished. She looked around the office and saw that members of her team were wearing headsets with see-through visors and grabbing invisible objects with their hands. Rosenburg had just returned from vacation so it took her a few seconds to process what was happening before she clued in: "Oh," she thought. "It's my turn now."

Rosenburg handles marketing for Meta, a San Francisco startup that makes augmented reality headsets Users can manipulate 3-D models with their hands or browse web pages, send emails and write code from floating virtual screens. Her boss, Meta founder and Chief Executive Officer s determined to end what he calls the "tyranny of the modern office" by replacing monitors, keyboards and eventually even cubicles with augmented reality. That experiment is the subject of this week's episode of the Decrypted podcast (subscribe here on Apple Podcasts).

In his vision, office workers will huddle around holograms to collaborate on pretty much any kind of task. That means no computers, cubicles, regular desks, or chairs. Gribetz's own office provides a glimpse of how a future workplace might look. He has a thin slab of wood at standing height as a desk. It's just wide enough for the headset to rest on it. He plans to redesign the rest of Meta's office in a similar way.

Meta isn't the only company with big ambitions for augmented reality. Microsoft Corp. and Apple Inc. are also devoting considerable resources to developing the technology; Apple's Tim Cook told Gribetz believes he has a chance of snatching the lead from better-financed rivals by testing his technology on employees who are focused the singular goal of transforming the workplace through AR. Many tech companies use this tactic, known as dog-fooding, but employees at larger firms often have multiple projects and incentives.

Photographer: Stephen Schauer/Meta

Before long, useful feedback began pouring inSome employees reported a distracting jitter when using the technology. The coders tweaked the algorithms and the problem disappeared. The hand-tracking and stability of the images improved. Employees participated in a company-wide hackathon to create various applications. Among them: a 3-D data visualization tool, a sticky note app and a stress-relieving orb that makes different musical notes when prodded. The company has filed several patent applications that cover concepts and tools developed during the dog-fooding process.

not that this is necessarily a limitation of augmented reality, she says, just that the technology-at least for now-is better for some tasks than others. sees a future where AR is one of various tools we use in our everyday lives, much the way we still use pen and paper for some things, and our phones and monitors for others. Her boss, of course, believes AR will supplant everything.

The benefits of the technology are clear for companies that make physical products. Carmakers can speed up the gestation of a new model thanks to instant holographic renderings of life-sized prototypes. For the average office worker, the perks are more nuanced. Based on the experiences of Meta employees, the biggest productivity gain is having virtually boundless space for an unlimited number of screens. That means focusing on one task while distractions-email, social media-can literally sit behind you. Some Meta employees ay that making a spreadsheet of data enormous can make it easier to spot interesting trends in the numbers. It's also made brainstorming easier, they say, since you can collaborate on giant, holographic pages.

"Suddenly everything seems very small again," says . "I pick up my phone, sort of squinting at it after being in the headset for a while, and suddenly you're like, 'wow, I can't believe we have such small rectangles around us all of the time.'" . "I was trying to grasp it like it was a hologram!"

If Meta employees are a hard sell you can imagine how tough it will be persuading the general public of augmented reality's benefits. Many tech companies doing futuristic work employ an in-house evangelist to get the word out. Meta's evangelist is Ryan Pamplin,

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Like his boss's office, Pamplin's workspace is a study in minimalism: a white desk with nothing on it except a headset and keyboard, and some awards and news clippings pinned to one wall. Put on the headset, though, and you step into Pamplin's dream office. Photos of his girlfriend are plastered all over. A holographic bust of Steve Jobs and a model of the Tesla 3 float nearby. A YouTube video of the Katy Perry Roar plays from a midair screen. It's basically Meta's version of a standard desktop, a stand-in for the traditional icons on a screen. If Pamplin wants to check his email, he grabs the web browser image from the shelf and a screen opens, suspended in the air. He has a bunch of other things on the shelf that aren't strictly work-related, including crackling fire for ambience. There's also a holographic eyeball. It's designed for medical students, but Pamplin just thinks it's cool.

"It's like real life," he says with boyish enthusiasm. "You're reaching around the office and you're grabbing objects, just like you would in a real office.''

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This Startup Wants to Replace Your Office With 3D Holograms

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One recent morning, arrived at work to find her PC monitor had vanished. She looked around the office and saw that members of her team were wearing headsets with see-through visors and grabbing invisible objects with their hands.

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Singapore

NEW research by Accenture has revealed that artificial intelligence (AI) could nearly double Singapore's annual economic growth rates by 2035.

It was also found that Singapore is at the vanguard of integrating innovation and technologies into the wider economy, ahead of

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AI can almost double annual growth rate by 2035: Accenture

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Singapore NEW research by Accenture has revealed that artificial intelligence (AI) could nearly double Singapore's annual economic growth rates by 2035. It was also found that Singapore is at the vanguard of integrating innovation and technologies into the wider economy, ahead of

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A security robot in Washington, D.C. -- lovingly named Steve -- plunged down four steps into a fountain Monday.

Photos show the sad, waterlogged robot cop partially submerged in defeat. It's unclear if any foul play was involved or if Steve simply rolled down a dark path on his own.

Steve had just started patrolling the Washington Harbour, a riverside complex in Georgetown with restaurants and offices, last week. The Washington Harbour and its real estate developer, MRP Realty, introduced the robot on Facebook on July 12.

The post touts Steve's "extensive catalogue of security capabilities," which apparently does not include any underwater crime-fighting.

Steve was still getting used to the streets he was programmed to protect. He was "mapping out the grounds" to theoretically prevent this kind of accident.

"This initial phase is our opportunity to implement, vet, and remediate any bugs in the system to help advance both the programming and security features in a busy mixed-use center such as The Washington Harbour," an MRP spokesperson wrote in a statement to CNN. "These incidents show us where improvements are needed, which may then be deployed to contribute to the ongoing security of our tenants, residents, and visitors."

Even though Steve had only been rolling around for a few days, he already made some new friends. "He looked so happy and healthy," one mourner remembered on Twitter.

But others who work near Steve weren't too sad to see him go. One not-so-fondly reflected on Steve being "very creepy."

And for some, Steve's unfortunate swim was a victory for humans fighting against the robot uprising.

A company called Knightscope produces Autonomous Data Machines like Steve. According to the company's website, the ADMs use multiple sensors to detect their location and "carefully navigate" the right path. And while Steve was "ruggedized and protected against vandalism," it doesn't seem like those same protections would save the robot from any water damage.

There's no word yet if Steve survived his unprecedented dip in the fountain. Luckily, no one was injured -- except for Steve.

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security robot meets its untimely demise after drowning on the job - cnn.com

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Security robot meets untimely demise after drowning on the job

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A security robot in Washington, D.C. -- lovingly named Steve -- plunged down four steps into a fountain Monday. Photos show the sad, waterlogged robot cop partially submerged in defeat. It's unclear if any foul play was involved or if Steve simply rolled down a dark path on his own.

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http://www.channelnewsasia.com/news/technology/robots-debate-future-of-humans-at-hong-kong-tech-show-9026098

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Robotics - Han and Sophia debate on humanity

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RISE Hanson Robotics' Sophia and Han debate on humanity