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What does Alpha Go Teach Us? | Margaret Boden

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ALPHA 2, The World's First Humanoid Robot for the Family

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Reality ain't what it used to be. Augmented reality, virtual reality and mixed reality tools are creating a lot of buzz - as well as a lot of potential headaches for IT departments in everything from networking and security to data retrieval.

Even understanding what each of the terms refers to can be confusing, as they aren't always used consistently. The term "mixed reality" (MR) was coined by researchers Paul Milgram and Fumio Kishino in 1994 to refer to every sensory environment in between the purely physical and the purely virtual. But over time, it fell out of use in favor of the more specific "augmented reality" (AR) for digital information overlaid on the real, physical world and "virtual reality" (VR) for a completely digital environment.

Over the past few years, Microsoft has revived the term "mixed reality" to market its HoloLens product, and vendors now tend to use the term to refer to the projection of 3D virtual objects or holograms into physical space. With AR, the digital overlay usually appears to float on a plane in front of the observer, while with MR it's possible to walk around a virtual object to see it from all sides.

One other difference: While today's VR and holographic MR applications typically require specialized hardware such as glasses or headsets, AR is already being built into apps for ordinary smartphones and tablets, as evidenced by last year's Pokémon Go game craze. Pokémon Go laid a social as well as technological foundation for the adoption of AR, says Tuong Nguyen, a principal research analyst with Gartner. "It made people familiar with the idea that if I hold up my phone, I expect to see some type of digital representation overlaid on the physical world. It caused a behavioral change even if you didn't play the game."

Whatever flavor of reality you prefer, the technologies hold a lot of promise for businesses. "We already have enterprises that are investing in AR," says Mark Sage, executive director of the Augmented Reality for Enterprise Alliance, a nonprofit trade organization promoting the adoption of AR. Companies typically deploy the technology for specific use cases or to solve particular business problems, Sage says. "It's all about improving performance and creating efficiencies."

"When you think both AR and VR, think of them as user interface technologies," says Nguyen. "There's a handful of horizontal applications for AR, like task itemization and pick/pack lists. I put on my [AR] glasses, and it says, 'Hey Tuong, you've got to go down to Aisle 13, Bay 4 and pick this off the shelf' and maybe overlay a little map." And both AR and VR could prove useful for design and collaboration, he says, enabling building engineers and construction workers to visualize how structures are going to look in place.

"The field service world is another one," adds Michael Fauscette, chief research officer at B2B reviews platform G2 Crowd. "Say you service a solar farm and you have to make a repair. If you have AR capability, you could see the work order, see the process, even see an overlay of the repair - there are all sorts of opportunities to put that data into your field of vision that makes it so much faster and easier to do your work."

Bosch Auto Parts

Automotive technicians can use Bosch's AR system to view the part they need to work on highlighted against an image of the actual engine needing repair.

Challenges for IT

With great opportunities come great responsibility, as Uncle Ben might have said if he'd worked in IT. "Like anything else, when you adopt a new technology, it puts a strain on your infrastructure," warns Chad Holmes, principal and cyber CTO at professional services organization EY. "You have to uplift your infrastructure to support a new communication path or channel."

Unfortunately, Holmes continues, many companies don't have the back-office capabilities required to support AR, MR and VR. "The technology is there, but a lot of improvements need to be made to old legacy systems to make sure that these types of technologies can function how they're supposed to," he says. "For most, it's a big investment."

The other big concern cited by Holmes and others is the security issue raised by the addition of new remote devices sharing data wirelessly. "So much is new and untested when it comes to AR and VR," Holmes says. "We don't yet know all the ways in which these solutions can be vulnerable. You've got to think about what they're going to be used for - what they're actually transmitting and looking at."

Nguyen agrees. "We have a head-mounted display, it's a portable device, it probably will have a camera on it, and the camera might be on all the time," he says, recalling that when phones first started getting cameras, IT departments were going crazy.

The constant flow of information also raises privacy issues. "I have this device on, so my employer can track and know that I took 25 minutes on a maintenance task that usually only takes 15 or that I wandered off someplace," he says. "These are things IT departments have to consider."

Time's a-wastin'

AR, VR and MR deployments may seem a long way off, but it won't be long before most enterprises will need to be ready. While consumer AR apps like Pokémon Go may get all the buzz, Nguyen says, "What I'm seeing is that business-to-business AR is ahead of business-to-consumer AR, whereas on the VR side it's flipped." He estimates that enterprise use of AR technology is two or three years ahead of the consumer side. "I expect the maturing and the traction within the enterprise to continue to outpace the consumer side, at least in the next three or so years," he adds.

Fauscette agrees. "Over the next two to three years, you're going to see AR go mainstream around some specific tasks like field service, logistics, warehouse, repairs and design," he says. "VR lags that by 25% or so."

And with Apple's June announcement of its ARKit developer framework for easily creating AR apps, it's clear that the company envisions AR having a significant presence on millions of iPhone and iPad users after it releases iOS 11 this fall. Initially, most of those apps will be consumer-facing, but they'll be entering the workplace on employees' mobile devices whether IT is ready or not. And, of course, ARKit opens up even more opportunities for companies to create worker-facing AR apps.

Corporations such as Boeing and Bosch are already running pilot programs with AR, VR and MR, as are educational institutions such as Case Western Reserve University. We spoke to these three organizations about the benefits and the challenges of mixing realities, and sought advice for IT departments preparing for the coming influx of apps and devices based on these technologies.

Boeing: Assembling satellites with an AR assist

Aviation giant Boeing has been experimenting with augmented reality for nearly 30 years. In the late 1980s, researchers were using headsets that could show a 2D display of a wiring diagram in a complex wiring harness. A more recent effort began about six years ago, starting in Boeing's satellite factory in El Segundo, Calif.

"We've mainly been doing pilots around AR to figure out how we would implement these things on a larger scale," explains Boeing senior technical fellow Paul Davies. "That involves, 'What is the concept of operations? Do workers hold a tablet or do we try a wearable?' We're also trying to figure out how to connect these systems to our back end. The big goal, at least for my group, is reducing the cycle time - the build time - and then improving the quality."

Boeing

Boeing Research & Technology systems engineer Adam Richardson works on design problems for the 777X aircraft using glasses that let him see a 3D virtual reality image of part of the airplane.

Boeing has built its own AR tool based on a commercially available SDK, adding a custom interface. With it, the technicians who assemble Boeing products (satellites, in this case) can receive technical information about the product as they work. "In some cases it could be step-by-step guidance on how to do a job," says Davies. "Or maybe they're seeing a model of a part so they can understand which way to orient it on a satellite panel."

Davies explains how helpful the latter would be when assembling waveguides in a satellite panel. "They look just like bent pieces of tubing," he says, "and it can take a while to figure out which way it goes and how it mates. If we use AR to show that in 3D and it's basically registered and overlaid on the panel, it becomes really quick and easy to understand how to install it."

The 3D model comes from the CAD files created by the programs used to design the products. "We would just export it or convert it into the right format," Davies says. "Sometimes we already have digital 2D tech instructions that are showing the 3D model, so we could bring those into the AR scene as well."

For the company's IT department, one of the biggest concerns is with putting the new devices on the network. "We have very stringent security requirements for what we allow on our network," Davies says, "so when new devices come out from vendors - headsets or whatever - if it's not running our enterprise load of Windows, it can't go on the network. Even if it is, it'll probably still have to go through a review." To that end, the IT department is adding staff familiar with mobile device management and wearables.

Davies emphasizes that Boeing is still in the pilot phase. "We haven't done the ROI, and we don't claim to have saved any time or money or anything like that," he says. "The purpose was for us to learn and figure out how we're going to implement these things on a larger scale. We've done analytical studies and analysis of user groups, and we know what we think we can expect in terms of efficiency improvements."

Case Western Reserve University: Studying virtual bodies

Sue Workman, CIO at Case Western Reserve University (CWRU), was not impressed with her initial experiences with virtual reality. "You put these glasses on, and the data comes to both of your eyes working separately rather than together. It was not very realistic, and it made you feel dizzy or even a little bit sick," she recalls.

When Microsoft invited executives from Cleveland Clinic and CWRU to a demo of its HoloLens "mixed reality" devices in 2014, she expected more of the same. Instead, she came away excited for what HoloLens could do for her school's curriculum.

"We're building a health education campus with Cleveland Clinic," she explains, a 485,000-square-foot building scheduled to open in 2019. "It will house our schools of medicine, nursing and dental medicine, including Cleveland Clinic's Lerner College of Medicine." The school didn't want to include cadaver labs in the new building, so Workman had been looking for ways to teach anatomy without cadavers. The HoloLens MR technology projects 3D anatomic holograms into the room, where students can see them from all angles and interact with them, as shown in the video at the top of this article.

Case Western Reserve University

Case Western Reserve University's medical school is exploring the possibility of using mixed reality to teach anatomy without the need for cadavers.

"We looked at several other technologies," Workman says. "For instance, one option we examined was a table monitor that's horizontal instead of vertical and displays 3D anatomy. But it's still 3D on a 2D screen. [HoloLens] technology has been the one that's really stood out to us and seemed likely to make the most sense for our anatomy faculty."

So far, Workman says, supporting the HoloLens doesn't look like it's going to be a big challenge to the school's IT resources. "The headset is a Windows 10 computer, and you use Unity to develop the applications," she explains. You do need some storage capacity when you're developing, until you can compress the data to actually send it to the HoloLens. "We're currently using Microsoft Azure to serve our devices," Workman says, "but it really doesn't matter - you could use AWS."

That's not to say that deploying several dozen headsets (so far) has presented no challenges. "The infrastructure is where you have to concentrate," Workman says. "The biggest impact has been with Wi-Fi, and we're working through those issues now."

What she's finding is that the devices might connect to the first wireless access point they encounter and stay connected, even when transferring to another access point might be more efficient. "If there are thirty people in the room, all thirty might connect to the same WAP," she says, "or there might be five WAPs in there, but only two of them are really serving." But, she's quick to point out, this isn't a problem unique to or new with the HoloLens - "it also happens in libraries and residence halls with students' cell phones."

So far, security hasn't been an issue because the tests haven't used real patient data or anything identifiable. "Someday, in a clinical situation, there will absolutely be privacy and security concerns," Workman acknowledges, adding that CWRU is working through security and compliance issues related to HoloLens now. But again, she says, it's no different than dealing with those issues on cell phones. "You've got to have best practices in place, you've got to have guidelines, you've got to have storage that's secure and networks you can trust. It's no different than with any other device."

Bosch: Working toward a unified AR interface

Bosch Auto Parts, which produces everything from brakes to batteries, is both a user and a supplier of AR systems, according to Juergen Lumera, director of global technical information systems product management and Innovation at Bosch Automotive Service Solutions. Lumera works with both internal and external customers, helping their service and IT departments understand what they need to be aware of when implementing an AR product. "We do the majority of our applications - service information, training, and end-of-line production solutions - for automotive enterprises with twenty to thirty thousand users," he says.

An example is the first-responder support developed for Daimler vehicles. When a vehicle is involved in an accident, rescue personnel may have to cut into the vehicle to extract passengers, and they need to know which parts of the car are dangerous to cut into. Bosch developed an app for smartphones and tablets that displays the relevant parts, superimposed on the device's camera view of the vehicle, to highlight these locations.

Bosch Auto Parts

In cooperation with Daimler AG, Bosch has developed an app (based on its Common Augmented Reality Platform) that shows first responders where the dangerous parts of a vehicle are.

But Bosch's vision for AR extends beyond information displayed on a smartphone or tablet. "For us, AR will be the future interface," Lumera says. "In the workshop, a technician will have only their AR glasses and will get all the information they need through that. You have a lot of diagnostic systems connected while you're repairing a car - why would I want somebody to leave the car to find out what to do? You'll find equivalent problems in any industry, where a technician is forced to interact with multiple devices. Our goal is to totally eliminate that."

To get there, Lumera warns, IT departments need to solve a lot of problems. Preparing the AR content for Bosch's systems requires access to an enterprise system's CAD data showing its products, but that data isn't usually set up for easy retrieval. "When we do proofs of concept," Lumera says, "the first thing we find is that the data is not structured so that you can easily extract what you need for a given situation. So IT departments need to make it simpler to fetch the right kind of CAD data."

The next problem is how to securely transport the data and ensure that each user gets only the data they need. "I really need to ensure that the 3D data has enough information to represent variants, and that I only see the variants relevant to me," Lumera continues.

He also points to security and privacy issues and the ways AR glasses differ from cell phones. An AR headset has a camera so it knows what data to display, but unlike with a phone, a user might accidentally look in any direction, not just at the product they're working on.

As at Boeing and CWRU, network issues round out the list of challenges. "Ask an IT manager to get a HoloLens on the official network," Lumera says. "I'm pretty sure that 95% have no idea how to do it. Whether Microsoft can technically do it is not the problem. It's more about how I deal with the rights, how I deal with the camera, and what user profiles I get. This is not an easy task."

Ready, set, go

What steps should enterprise IT take now to be ready for these technologies?

Recruit staff with the right expertise. "There are some real holes when companies start to think about both AR and VR," says G2 Crowd's Fauscette. "One of them is the skill set and talent you need. A lot of AR and VR apps are custom add-ins or extensions. The standard skill sets that most IT organizations have aren't going to be the ones they need to do that."

For developing such tools, he suggests thinking about recruiting staff from the gaming industry who understand the technology and the user experience. On the implementation side, take a cue from Boeing and look for workers who have mobile device management expertise.

Get help from the experts. One helpful resource is the Augmented Reality for Enterprise Alliance (AREA), launched in 2015 to help organizations make decisions about these new technologies. (Bosch and Boeing are both members.) The organization has recently developed a set of global enterprise AR hardware and software requirements in conjunction with UI Labs, Lockheed Martin, Caterpillar and Procter & Gamble.

Because of the customization necessary, Fauscette and Gartner analyst Nguyen emphasize the value of partnerships. "There's still a lot of specialized work and integration that needs to be done," says Nguyen. "You have to look for different partners who can help with that. Ideally, you should be able to leverage a lot of the resources you already have."

Accenture is already building a practice around these technologies, says Fauscette, adding that other large systems integrators are probably doing the same or soon will. "The general questions of, 'I need to build this, integrate it, and build the content behind it' could easily be the kind of help you get from a systems integration partner," he says.

Begin a pilot project. AREA's Sage says enterprises should set up an incubator-style project or a "skunkworks" to work on testing and getting ready. Conducting small test programs can help companies ensure that source data is accessible and properly structured; identify trouble spots in networking, storage and security infrastructure; work through systems integration issues; and develop best practices around data privacy and device management.

Whether you line up partners, start talking to integrators, set up a skunkworks, recruit staff with the right expertise - or all of the above - it's not too late to get started. Remember, it was only about three to five years ago that BYOD was regarded as a new trend. For IT departments, reality in any form comes at you fast.

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How IT can prepare for VR, AR and MR in the enterprise

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Reality ain't what it used to be. Augmented reality, virtual reality and mixed reality tools are creating a lot of buzz - as well as a lot of potential headaches for IT departments in everything from networking and security to data retrieval.

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Artefact Automotive: The Future of Semi-Autonomous Cars

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The promise of fully autonomous vehicles is exciting, but before we can sit back and take a nap on our way to the office, the transition period of semi-autonomous vehicles requires a hybrid interaction model for driving a car that blends both past and future and accounts for varying degrees of control and agency.

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what can we find out about our own (human) creativity from those?

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what can we find out about our own (human) creativity from those?

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Nao - a robot created for companionship. Jiuguang Wang/wikimedia, CC BY-SA

Does your car "not want" to start on cold mornings? And does your toaster "like" burning your toast? This kind of intentional language is natural to us and built into the way we interact with the world - even with machines. This is because we have evolved to become extremely social animals, understanding others by forming mental models of what they are thinking. We use these skills to understand the behaviour of anything complicated we interact with, especially robots.

It's almost like we believe that machines have minds of their own. And the fact that we perceive them as intelligent is partly why they have such potential. Robots are moving beyond industrial, commercial and scientific applications, and are already used in hospitals and care homes. Soon it will be normal to interact with robots in our daily lives, to complete useful tasks. Robots are also being used as companions, particularly for elderly patients with cognitive impairment such as dementia. After years of scientific study, this has proven very successful at improving long-term quality of life.

However, there are ethical concerns about vulnerable people forming relationships with machines, in some cases even believing them to be animals or people. Are robot designers intending to deceive patients? As robots become more important to us, how can we trust them not to mislead us, indeed should we trust them at all?

In 2010, a group of academics produced ethical guidelines for how we should build robots, much like science fiction writer Isaac Asimov's famous laws. Asimov stated that robots could not do anything to harm a human being; that a robot should always obey a human; and that a robot should defend itself so long as this didn't interfere with the first two rules. Similarly, these academics produced the EPSRC Principles of Robotics. For me, the most interesting principle is number four: "Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent."

Hidden decisions

In my research group, we are conducting experiments with robots to investigate how well we understand them. Ultimately, we want to find out how we can best design robots to improve our mental models. We're trying to show that it's possible to create transparent robots that are useful, and when necessary emotionally engaging, despite having a transparent machine nature. We assert that if we can make robots more transparent, then we won't need to trust them, we'll always know what they are doing.

University of Bath., Author provided

We use a simple robot that moves around a room avoiding objects while searching for humans. When it finds a human it flashes lights, does a small wiggle dance, and then trundles off seeking another one. Sometimes when in a corner it goes to sleep to save battery. That's all it does. We videoed the robot, showed this to a group of 22 people and asked them what the robot was doing and why.

Some of the answers were remarkable. Based on cues from the environment, and the imaginations of the people, they came up with all sorts of ideas about what the robot was up to - views that were generally quite wrong. For instance there is a bucket in the room, and several people were sure the robot was trying to throw something into it. Others noticed an abstract picture in the room and wondered if the robot was going to complete the picture. These people were mainly graduates in professional jobs, and several had science, technology, engineering or maths degrees. Almost all used computers every day. Although we did not program the robot, nor create the room explicitly to mislead, the observers were deceived.

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We showed the same video to an almost identical second group. However this group was simultaneously shown a display demonstrating each decision in the robot's action selection system controlling its behaviour, synchronised with the robot moving around the room and tracking objects. It is a kind of dynamic heat map of the processes and decisions being made inside the robot's brain, making plain the focus of attention of the robot, and outlining the steps it takes to achieve its goals.

The results, to be published at the International Joint Conference on Artificial Intelligence in July, were remarkable. The second group came to a much better understanding of what the robot was doing, and why. We expected that result. What we didn't expect was that the second group were twice as sure that the robot was "thinking". It seems that an improved mental model of the robot is associated with an increased perception of a thinking machine, even when there is no significant change in the level of perceived intelligence. The relationship between the perception of intelligence and thinking is therefore not straightforward.

This is encouraging as it shows that we can have robots that are transparently machines and yet are still engaging, in that the participants attribute them with intelligence and thinking. We are beginning to show that designers can create something appealing without the need to hide the true capabilities of the robot.

So, the robot butler of the future may have transparency built in. Perhaps you can ask it what its doing and it will tell you by showing you or talking about what's going on in its brain. We'd like to see that mechanism built in to the low level brain code of the robot, so it has to tell it like it is. It would be nice for the user to be able to dial this up or down depending on how familiar they are with the tasks the robot is doing.

We plan to try other ways of making robots transparent using speech technology, and combinations of graphics, text and speech, and we hope to be able to produce more detailed guidelines and better tools for robot designers to help them build robots we don't need to trust.

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How to design trustworthy robot butlers that we won't want to treat like humans

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Does your car "not want" to start on cold mornings? And does your toaster "like" burning your toast? This kind of intentional language is natural to us and built into the way we interact with the world - even with machines.

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I' ve heard that in the future computerized AIs will become so much smarter than us that they will take all our jobs and resources, and humans will go extinct. Is this true?

That's the most common question I get whenever I give a talk about AI. The questioners are earnest; their worry stems in part from some experts who are asking themselves the same thing. These folks are some of the smartest people alive today, such as Stephen Hawking, Elon Musk, Max Tegmark, Sam Harris, and Bill Gates, and they believe this scenario very likely could be true. Recently at a conference convened to discuss these AI issues, a panel of nine of the most informed gurus on AI all agreed this superhuman intelligence was inevitable and not far away.

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Yet buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence. These claims might be true in the future, but there is no evidence to date to support them. The assumptions behind a superhuman intelligence arising soon are:

1. Artificial intelligence is already getting smarter than us, at an exponential rate.

2. We'll make AIs into a general purpose intelligence, like our own.

3. We can make human intelligence in silicon.

4. Intelligence can be expanded without limit.

5. Once we have exploding superintelligence it can solve most of our problems.

In contradistinction to this orthodoxy, I find the following five heresies to have more evidence to support them.

1. Intelligence is not a single dimension, so "smarter than humans" is a meaningless concept.

2. Humans do not have general purpose minds, and neither will AIs.

3. Emulation of human thinking in other media will be constrained by cost.

4. Dimensions of intelligence are not infinite.

5. Intelligences are only one factor in progress.

If the expectation of a superhuman AI takeover is built on five key assumptions that have no basis in evidence, then this idea is more akin to a religious belief - a myth. In the following paragraphs I expand my evidence for each of these five counter-assumptions, and make the case that, indeed, a superhuman AI is a kind of myth.

1.

The most common misconception about artificial intelligence begins with the common misconception about natural intelligence. This misconception is that intelligence is a single dimension. Most technical people tend to graph intelligence the way Nick Bostrom does in his book, Superintelligence  - as a literal, single-dimension, linear graph of increasing amplitude. At one end is the low intelligence of, say, a small animal; at the other end is the high intelligence, of, say, a genius-almost as if intelligence were a sound level in decibels. Of course, it is then very easy to imagine the extension so that the loudness of intelligence continues to grow, eventually to exceed our own high intelligence and become a super-loud intelligence - a roar! - way beyond us, and maybe even off the chart.

This model is topologically equivalent to a ladder, so that each rung of intelligence is a step higher than the one before. Inferior animals are situated on lower rungs below us, while higher-level intelligence AIs will inevitably overstep us onto higher rungs. Time scales of when it happens are not important; what is important is the ranking-the metric of increasing intelligence.

The problem with this model is that it is mythical, like the ladder of evolution. The pre-Darwinian view of the natural world supposed a ladder of being, with inferior animals residing on rungs below human. Even post-Darwin, a very common notion is the "ladder" of evolution, with fish evolving into reptiles, then up a step into mammals, up into primates, into humans, each one a little more evolved (and of course smarter) than the one before it. So the ladder of intelligence parallels the ladder of existence. But both of these models supply a thoroughly unscientific view.

A more accurate chart of the natural evolution of species is a disk radiating outward, like this one (above) first devised by David Hillis at the University of Texas and based on DNA. This deep genealogy mandala begins in the middle with the most primeval life forms, and then branches outward in time. Time moves outward so that the most recent species of life living on the planet today form the perimeter of the circumference of this circle. This picture emphasizes a fundamental fact of evolution that is hard to appreciate: Every species alive today is equally evolved. Humans exist on this outer ring alongside cockroaches, clams, ferns, foxes, and bacteria. Every one of these species has undergone an unbroken chain of three billion years of successful reproduction, which means that bacteria and cockroaches today are as highly evolved as humans. There is no ladder.

Likewise, there is no ladder of intelligence. Intelligence is not a single dimension. It is a complex of many types and modes of cognition, each one a continuum. Let's take the very simple task of measuring animal intelligence. If intelligence were a single dimension we should be able to arrange the intelligences of a parrot, a dolphin, a horse, a squirrel, an octopus, a blue whale, a cat, and a gorilla in the correct ascending order in a line. We currently have no scientific evidence of such a line. One reason might be that there is no difference between animal intelligences, but we don't see that either. Zoology is full of remarkable differences in how animals think. But maybe they all have the same relative "general intelligence?" It could be, but we have no measurement, no single metric for that intelligence. Instead we have many different metrics for many different types of cognition.

Instead of a single decibel line, a more accurate model for intelligence is to chart its possibility space, like the above rendering of possible forms created by an algorithm written by Richard Dawkins. Intelligence is a combinatorial continuum. Multiple nodes, each node a continuum, create complexes of high diversity in high dimensions. Some intelligences may be very complex, with many sub-nodes of thinking. Others may be simpler but more extreme, off in a corner of the space. These complexes we call intelligences might be thought of as symphonies comprising many types of instruments. They vary not only in loudness, but also in pitch, melody, color, tempo, and so on. We could think of them as ecosystem. And in that sense, the different component nodes of thinking are co-dependent, and co-created.

Human minds are societies of minds, in the words of Marvin Minsky. We run on ecosystems of thinking. We contain multiple species of cognition that do many types of thinking: deduction, induction, symbolic reasoning, emotional intelligence, spacial logic, short-term memory, and long-term memory. The entire nervous system in our gut is also a type of brain with its own mode of cognition. We don't really think with just our brain; rather, we think with our whole bodies.

These suites of cognition vary between individuals and between species. A squirrel can remember the exact location of several thousand acorns for years, a feat that blows human minds away. So in that one type of cognition, squirrels exceed humans. That superpower is bundled with some other modes that are dim compared to ours in order to produce a squirrel mind. There are many other specific feats of cognition in the animal kingdom that are superior to humans, again bundled into different systems.

Likewise in AI. Artificial minds already exceed humans in certain dimensions. Your calculator is a genius in math; Google's memory is already beyond our own in a certain dimension. We are engineering AIs to excel in specific modes. Some of these modes are things we can do, but they can do better, such as probability or math. Others are type of thinking we can't do at all - memorize every single word on six billion web pages, a feat any search engine can do. In the future, we will invent whole new modes of cognition that don't exist in us and don't exist anywhere in biology. When we invented artificial flying we were inspired by biological modes of flying, primarily flapping wings. But the flying we invented - propellers bolted to a wide fixed wing - was a new mode of flying unknown in our biological world. It is alien flying. Similarly, we will invent whole new modes of thinking that do not exist in nature. In many cases they will be new, narrow, "small," specific modes for specific jobs - perhaps a type of reasoning only useful in statistics and probability.

In other cases the new mind will be complex types of cognition that we can use to solve problems our intelligence alone cannot. Some of the hardest problems in business and science may require a two-step solution. Step one is: Invent a new mode of thought to work with our minds. Step two: Combine to solve the problem. Because we are solving problems we could not solve before, we want to call this cognition "smarter" than us, but really it is different than us. It's the differences in thinking that are the main benefits of AI. I think a useful model of AI is to think of it as alien intelligence (or artificial aliens). Its alienness will be its chief asset.

At the same time we will integrate these various modes of cognition into more complicated, complex societies of mind. Some of these complexes will be more complex than us, and because they will be able to solve problems we can't, some will want to call them superhuman. But we don't call Google a superhuman AI even though its memory is beyond us, because there are many things we can do better than it. These complexes of artificial intelligences will for sure be able to exceed us in many dimensions, but no one entity will do all we do better. It's similar to the physical powers of humans. The industrial revolution is 200 years old, and while all machines as a class can beat the physical achievements of an individual human (speed of running, weight lifting, precision cutting, etc.), there is no one machine that can beat an average human in everything he or she does.

Even as the society of minds in an AI become more complex, that complexity is hard to measure scientifically at the moment. We don't have good operational metrics of complexity that could determine whether a cucumber is more complex than a Boeing 747, or the ways their complexity might differ. That is one of the reasons why we don't have good metrics for smartness as well. It will become very difficult to ascertain whether mind A is more complex than mind B, and for the same reason to declare whether mind A is smarter than mind B. We will soon arrive at the obvious realization that "smartness" is not a single dimension, and that what we really care about are the many other ways in which intelligence operates - all the other nodes of cognition we have not yet discovered.

2.

The second misconception about human intelligence is our belief that we have a general purpose intelligence. This repeated belief influences a commonly stated goal of AI researchers to create an artificial general purpose intelligence (AGI). However, if we view intelligence as providing a large possibility space, there is no general purpose state. Human intelligence is not in some central position, with other specialized intelligence revolving around it. Rather, human intelligence is a very, very specific type of intelligence that has evolved over many millions of years to enable our species to survive on this planet. Mapped in the space of all possible intelligences, a human-type of intelligence will be stuck in the corner somewhere, just as our world is stuck at the edge of vast galaxy.

We can certainly imagine, and even invent, a Swiss-army knife type of thinking. It kind of does a bunch of things okay, but none of them very well. AIs will follow the same engineering maxim that all things made or born must follow: You cannot optimize every dimension. You can only have tradeoffs. You can't have a general multi-purpose unit outperform specialized functions. A big "do everything" mind can't do everything as well as those things done by specialized agents. Because we believe our human minds are general purpose, we tend to believe that cognition does not follow the engineer's tradeoff, that it will be possible to build an intelligence that maximizes all modes of thinking. But I see no evidence of that. We simply haven't invented enough varieties of minds to see the full space (and so far we have tended to dismiss animal minds as a singular type with variable amplitude on a single dimension.)

3.

Part of this belief in maximum general-purpose thinking comes from the concept of universal computation. Formally described as the Church-Turing hypothesis in 1950, this conjecture states that all computation that meets a certain threshold is equivalent. Therefore there is a universal core to all computation, whether it occurs in one machine with many fast parts, or slow parts, or even if it occurs in a biological brain, it is the same logical process. Which means that you should be able to emulate any computational process (thinking) in any machine that can do "universal" computation. Singularitans rely on this principle for their expectation that we will be able to engineer silicon brains to hold human minds, and that we can make artificial minds that think like humans, only much smarter. We should be skeptical of this hope because it relies on a misunderstanding of the Church-Turing hypothesis.

The starting point of the theory is: "Given infinite tape [memory] and time, all computation is equivalent." The problem is that in reality, no computer has infinite memory or time. When you are operating in the real world, real time makes a huge difference, often a life-or-death difference. Yes, all thinking is equivalent if you ignore time. Yes, you can emulate human-type thinking in any matrix you want, as long as you ignore time or the real-life constraints of storage and memory. However, if you incorporate time, then you have to restate the principal in a significant way: Two computing systems operating on vastly different platforms won't be equivalent in real time. That can be restated again as: The only way to have equivalent modes of thinking is to run them on equivalent substrates. The physical matter you run your computation on - particularly as it gets more complex - greatly influences the type of cognition that can be done well in real time.

I will extend that further to claim that the only way to get a very human-like thought process is to run the computation on very human-like wet tissue. That also means that very big, complex artificial intelligences run on dry silicon will produce big, complex, unhuman-like minds. If it would be possible to build artificial wet brains using human-like grown neurons, my prediction is that their thought will be more similar to ours. The benefits of such a wet brain are proportional to how similar we make the substrate. The costs of creating wetware is huge and the closer that tissue is to human brain tissue, the more cost-efficient it is to just make a human. After all, making a human is something we can do in nine months.

Furthermore, as mentioned above, we think with our whole bodies, not just with our minds. We have plenty of data showing how our gut's nervous system guides our "rational" decision-making processes, and can predict and learn. The more we model the entire human body system, the closer we get to replicating it. An intelligence running on a very different body (in dry silicon instead of wet carbon) would think differently.

I don't see that as a bug but rather as a feature. As I argue in point 2, thinking differently from humans is AI's chief asset. This is yet another reason why calling it "smarter than humans" is misleading and misguided.

4.

At the core of the notion of a superhuman intelligence - particularly the view that this intelligence will keep improving itself - is the essential belief that intelligence has an infinite scale. I find no evidence for this. Again, mistaking intelligence as a single dimension helps this belief, but we should understand it as a belief. There is no other physical dimension in the universe that is infinite, as far as science knows so far. Temperature is not infinite - there is finite cold and finite heat. There is finite space and time. Finite speed. Perhaps the mathematical number line is infinite, but all other physical attributes are finite. It stands to reason that reason itself is finite, and not infinite. So the question is, where is the limit of intelligence? We tend to believe that the limit is way beyond us, way "above" us, as we are "above" an ant. Setting aside the recurring problem of a single dimension, what evidence do we have that the limit is not us? Why can't we be at the maximum? Or maybe the limits are only a short distance away from us? Why do we believe that intelligence is something that can continue to expand forever?

A much better way to think about this is to see our intelligence as one of a million types of possible intelligences. So while each dimension of cognition and computation has a limit, if there are hundreds of dimensions, then there are uncountable varieties of mind - none of them infinite in any dimension. As we build or encounter these uncountable varieties of mind we might naturally think of some of them as exceeding us. In my recent book The Inevitable, I sketched out some of that variety of minds that were superior to us in some way. Here is an incomplete list:

Some folks today may want to call each of these entities a superhuman AI, but the sheer variety and alienness of these minds will steer us to new vocabularies and insights about intelligence and smartness.

Second, believers of Superhuman AI assume intelligence will increase exponentially (in some unidentified single metric), probably because they also assume it is already expanding exponentially. However, there is zero evidence so far that intelligence - no matter how you measure it - is increasing exponentially. By exponential growth I mean that artificial intelligence doubles in power on some regular interval. Where is that evidence? Nowhere I can find. If there is none now, why do we assume it will happen soon? The only thing expanding on an exponential curve are the inputs in AI, the resources devoted to producing the smartness or intelligences. But the output performance is not on a Moore's law rise. AIs are not getting twice as smart every 3 years, or even every 10 years.

I asked a lot of AI experts for evidence that intelligence performance is on an exponential gain, but all agreed we don't have metrics for intelligence, and besides, it wasn't working that way. When I asked Ray Kurzweil, the exponential wizard himself, where the evidence for exponential AI was, he wrote to me that AI does not increase explosively but rather by levels. He said: "It takes an exponential improvement both in computation and algorithmic complexity to add each additional level to the hierarchy.... So we can expect to add levels linearly because it requires exponentially more complexity to add each additional layer, and we are indeed making exponential progress in our ability to do this. We are not that many levels away from being comparable to what the neocortex can do, so my 2029 date continues to look comfortable to me."

What Ray seems to be saying is that it is not that the power of artificial intelligence is exploding exponentially, but that the effort to produce it is exploding exponentially, while the output is merely raising a level at a time. This is almost the opposite of the assumption that intelligence is exploding. This could change at some time in the future, but artificial intelligence is clearly not increasing exponentially now.

Therefore when we imagine an "intelligence explosion," we should imagine it not as a cascading boom but rather as a scattering exfoliation of new varieties. A Cambrian explosion rather than a nuclear explosion. The results of accelerating technology will most likely not be super-human, but extra-human. Outside of our experience, but not necessarily "above" it.

5.

Another unchallenged belief of a super AI takeover, with little evidence, is that a super, near-infinite intelligence can quickly solve our major unsolved problems.

Many proponents of an explosion of intelligence expect it will produce an explosion of progress. I call this mythical belief "thinkism." It's the fallacy that future levels of progress are only hindered by a lack of thinking power, or intelligence. (I might also note that the belief that thinking is the magic super ingredient to a cure-all is held by a lot of guys who like to think.)

Let's take curing cancer or prolonging longevity. These are problems that thinking alone cannot solve. No amount of thinkism will discover how the cell ages, or how telomeres fall off. No intelligence, no matter how super duper, can figure out how the human body works simply by reading all the known scientific literature in the world today and then contemplating it. No super AI can simply think about all the current and past nuclear fission experiments and then come up with working nuclear fusion in a day. A lot more than just thinking is needed to move between not knowing how things work and knowing how they work. There are tons of experiments in the real world, each of which yields tons and tons of contradictory data, requiring further experiments that will be required to form the correct working hypothesis. Thinking about the potential data will not yield the correct data.

Thinking (intelligence) is only part of science; maybe even a small part. As one example, we don't have enough proper data to come close to solving the death problem. In the case of working with living organisms, most of these experiments take calendar time. The slow metabolism of a cell cannot be sped up. They take years, or months, or at least days, to get results. If we want to know what happens to subatomic particles, we can't just think about them. We have to build very large, very complex, very tricky physical structures to find out. Even if the smartest physicists were 1,000 times smarter than they are now, without a Collider, they will know nothing new.

There is no doubt that a super AI can accelerate the process of science. We can make computer simulations of atoms or cells and we can keep speeding them up by many factors, but two issues limit the usefulness of simulations in obtaining instant progress. First, simulations and models can only be faster than their subjects because they leave something out. That is the nature of a model or simulation. Also worth noting: The testing, vetting and proving of those models also has to take place in calendar time to match the rate of their subjects. The testing of ground truth can't be sped up.

These simplified versions in a simulation are useful in winnowing down the most promising paths, so they can accelerate progress. But there is no excess in reality; everything real makes a difference to some extent; that is one definition of reality. As models and simulations are beefed up with more and more detail, they come up against the limit that reality runs faster than a 100 percent complete simulation of it. That is another definition of reality: the fastest possible version of all the details and degrees of freedom present. If you were able to model all the molecules in a cell and all the cells in a human body, this simulation would not run as fast as a human body. No matter how much you thought about it, you still need to take time to do experiments, whether in real systems or in simulated systems.

To be useful, artificial intelligences have to be embodied in the world, and that world will often set their pace of innovations. Without conducting experiments, building prototypes, having failures, and engaging in reality, an intelligence can have thoughts but not results. There won't be instant discoveries the minute, hour, day or year a so-called "smarter-than-human" AI appears. Certainly the rate of discovery will be significantly accelerated by AI advances, in part because alien-ish AI will ask questions no human would ask, but even a vastly powerful (compared to us) intelligence doesn't mean instant progress. Problems need far more than just intelligence to be solved.

Not only are cancer and longevity problems that intelligence alone can't solve, so is intelligence itself. The common trope among Singularitans is that once you make an AI "smarter than humans" then all of sudden it thinks hard and invents an AI "smarter than itself," which thinks harder and invents one yet smarter, until it explodes in power, almost becoming godlike. We have no evidence that merely thinking about intelligence is enough to create new levels of intelligence. This kind of thinkism is a belief. We have a lot of evidence that in addition to great quantities of intelligence we need experiments, data, trial and error, weird lines of questioning, and all kinds of things beyond smartness to invent new kinds of successful minds.

I'd conclude by saying that I could be wrong about these claims. We are in the early days. We might discover a universal metric for intelligence; we might discover it is infinite in all directions. Because we know so little about what intelligence is (let alone consciousness), the possibility of some kind of AI singularity is greater than zero. I think all the evidence suggests that such a scenario is highly unlikely, but it is greater than zero.

So while I disagree on its probability, I am in agreement with the wider aims of OpenAI and the smart people who worry about a superhuman AI - that we should engineer friendly AIs and figure out how to instill self-replicating values that match ours. Though I think a superhuman AI is a remote possible existential threat (and worthy of considering), I think its unlikeliness (based on the evidence we have so far) should not be the guide for our science, policies, and development. An asteroid strike on the Earth would be catastrophic. Its probability is greater than zero (and so we should support the B612 Foundation), but we shouldn't let the possibility of an asteroid strike govern our efforts in, say, climate change, or space travel, or even city planning.

Likewise, the evidence so far suggests AIs most likely won't be superhuman but will be many hundreds of extra-human new species of thinking, most different from humans, none that will be general purpose, and none that will be an instant god solving major problems in a flash. Instead there will be a galaxy of finite intelligences, working in unfamiliar dimensions, exceeding our thinking in many of them, working together with us in time to solve existing problems and create new problems.

I understand the beautiful attraction of a superhuman AI god. It's like a new Superman. But like Superman, it is a mythical figure. Somewhere in the universe a Superman might exist, but he is very unlikely. However myths can be useful, and once invented they won't go away. The idea of a Superman will never die. The idea of a superhuman AI Singularity, now that it has been birthed, will never go away either. But we should recognize that it is a religious idea at this moment and not a scientific one. If we inspect the evidence we have so far about intelligence, artificial and natural, we can only conclude that our speculations about a mythical superhuman AI god are just that: myths.

Many isolated islands in Micronesia made their first contact with the outside world during World War II. Alien gods flew over their skies in noisy birds, dropped food and goods on their islands, and never returned. Religious cults sprang up on the islands praying to the gods to return and drop more cargo. Even now, fifty years later, many still wait for the cargo to return. It is possible that superhuman AI could turn out to be another cargo cult. A century from now, people may look back to this time as the moment when believers began to expect a superhuman AI to appear at any moment and deliver them goods of unimaginable value. Decade after decade they wait for the superhuman AI to appear, certain that it must arrive soon with its cargo.

Yet non-superhuman artificial intelligence is already here, for real. We keep redefining it, increasing its difficulty, which imprisons it in the future, but in the wider sense of alien intelligences - of a continuous spectrum of various smartness, intelligences, cognition, reasonings, learning, and consciousness - AI is already pervasive on this planet and will continue to spread, deepen, diversify, and amplify. No invention before will match its power to change our world, and by century's end AI will touch and remake everything in our lives. Still the myth of a superhuman AI, poised to either gift us super-abundance or smite us into super-slavery (or both), will probably remain alive-a possibility too mythical to dismiss.
Art direction by Robert Shaw.

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The Myth of a Superhuman AI - Backchannel

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I' ve heard that in the future computerized AIs will become so much smarter than us that they will take all our jobs and resources, and humans will go extinct. Is this true? That's the most common question I get whenever I give a talk about AI.

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Autonomous AI or Human Intelligence Augmentation?

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Autonomous AI or Human Intelligence Augmentation?

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For many of us, the concept of artificial intelligence conjures up visions of a machine-dominated world, where humans are servants to the devices they created. That's a frightening image, inspired more by Hollywood and science fiction writers than technologists and the academic community. The truth is less sensational but far more meaningful.

We're actually nowhere near the self-sustaining robots Isaac Asimov imagined in I, Robot. What we have instead is intelligence amplification (IA), a field with exponentially more potential to change the world in the immediate future.

WIRED Opinion

About

The distinction between AI and IA is as simple as it is significant. AI makes machines autonomous and detached from humans; IA, in on the other hand, puts humans in control and leverages computing power to amplify our capabilities.

For a real-world example of IA, look no further than IBM's Watson, an intelligence amplification machine that is often mistaken for AI. The feedback loop created by exposing intelligence to humans through APIs enables Watson machine to learn and improve the information it provides. The machine presents that information to humans and then learns from their decisions. Like much of IA, Watson becomes smarter by amplifying our own intelligence.

While humans have used tools to bolster their productivity for centuries, the proliferation of application programming interfaces (APIs)-the mortar connecting the bricks of our digital world-in recent years has enabled greater access to valuable information in real time. The combination of intelligent computers, intelligent software, and APIs has profound implications for our everyday lives.

Doctors, for example, stand to benefit tremendously from IA in their interactions with patients. Say you have a doctor at the Mayo Clinic making a diagnosis. The patient is relying on the doctor's expertise-but the publication of new medical research far outpaces the doctor's ability to consume and analyze it. That's where IA comes in. Rather than depending on his or her finite body of knowledge, the doctor can utilize supercomputers capable of surveying vast amounts of information quickly to present decisions the doctor might not have thought of or known about.

Visions of the future have distracted us from what's possible today.

Meanwhile, present-day robots can hardly stay upright.

This isn't to say artificial intelligence doesn't have a significant role to play in the evolution of intelligent computers and they way we interact with them. Researchers at MIT, the University of Toronto, and elsewhere have advanced AI's value in performing "soft intelligence" tasks like facial identification and pattern recognition -activities that ultimately improve judgment across the entire system. However, when it comes to "hard intelligence" activities like driving a car, AI still has a lot of learning to do.

Visions of the future have distracted us from what's possible today. While Google experiments with self-driving cars that can be derailed with a simple laser pointer, automakers around the globe have already begun introducing IA-enhanced cars that can improve safety by assisting drivers with duties like highway driving on long-distance road trips. Tesla, Volvo, and Audi have or will soon introduce "autopilot" functionality on their vehicles. Though it's still unclear when autonomous vehicles will become affordable for most Americans - keeping them in a world of moonshots for now - IA-integrated cars are something we can advance, utilize, and benefit from today.

Of course, technology will always need moonshot ideas -they're what makes humans great. But focusing too heavily on fully-formed artificial intelligence misses the great strides we're making here and now with intelligence amplification that's actually changing lives.

The future of machine collaboration we've fantasized about is already here, and it's not what we've been taught to fear. Our machines really are here to serve us-all we have to do is embrace them.

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Obsessing Over AI Is the Wrong Way to Think About the Future

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For many of us, the concept of artificial intelligence conjures up visions of a machine-dominated world, where humans are servants to the devices they created. That's a frightening image, inspired more by Hollywood and science fiction writers than technologists and the academic community. The truth is less sensational but far more meaningful.

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Let us all raise a glass to AlphaGo and mark another big moment in the advance of artificial intelligence and then perhaps start to worry. AlphaGo, Google DeepMind's game of Go-playing AI just bested the best Go-playing human currently alive, the renowned Lee Sedol. This was not supposed to happen. At least, not for a while. An artificial intelligence capable of beating the best humans at the game was predicted to be 10 years away.

But as we drink to its early arrival, we should also begin trying to understand what the surprise means for the future - with regard, chiefly, to the ethics and governance implications that stretch far beyond a game.

As AlphaGo and AIs like it become more sophisticated - commonly outperforming us at tasks once thought to be uniquely human - will we feel pressured to relinquish control to the machines?

The number of possible moves in a game of Go is so massive that, in order to win against a player of Lee's calibre, AlphaGo was designed to adopt an intuitive, human-like style of gameplay. Relying exclusively on more traditional brute-force programming methods was not an option. Designers at DeepMind made AlphaGo more human-like than traditional AI by using a relatively recent development - deep learning.

Deep learning uses large data sets, "machine learning" algorithms and deep neural networks - artificial networks of "nodes" that are meant to mimic neurons - to teach the AI how to perform a particular set of tasks. Rather than programming complex Go rules and strategies into AlphaGo, DeepMind designers taught AlphaGo to play the game by feeding it data based on typical Go moves. Then, AlphaGo played against itself, tirelessly learning from its own mistakes and improving its gameplay over time. The results speak for themselves.

Possessing a more intuitive approach to problem-solving allows artificial intelligence to succeed in highly complex environments. For example, actions with high levels of unpredictablility - talking, driving, serving as a soldier - which were previously unmanageable for AI are now considered technically solvable, thanks in large part to deep learning.

AI is also increasingly able to manage complex, data intensive tasks, such as monitoring credit card systems for fraudulent behaviour, high-frequency stock trading and detecting cyber security threats. Embodied as robots, deep-learning AI is poised to begin to move and work among us - in the form of service, transportation, medical and military robots.

Deep learning represents a paradigm shift in the relationship humans have with their technological creations. It results in AI that displays genuinely surprising and unpredictable behaviour. Commenting after his first loss, Lee described being stunned by an unconventional move he claimed no human would ever have made. Demis Hassabis, one of DeepMind 's founders, echoed the sentiment: "We're very pleased that AlphaGo played some quite surprising and beautiful moves."

Alan Turing, the visionary computer scientist, predicted we would someday speak of machines that think. He never predicted this.

This also made me think back to my time as an engineer, when surprises in the lab were rarely occasions for celebration. In a more traditional design environment, the goal is to anticipate and control for as many possible states a device could find itself in. Surprises typically meant that our design had deviated from its intended behaviour and required a fix. But this just underscores the difference between traditional design paradigms and deep learning.

When it comes to deep learning, unpredictability and surprises are - or can be - a good thing. They can serve as indicators that a system is working well, perhaps better than the humans that came before it. Such is the case with AlphaGo. In the coming years, it will probably continue to learn and to improve, surprising and teaching its human competitors with new moves and strategies along the way.

Other artificial intelligence, designed to benefit humanity by surpassing our abilities in highly complex tasks - diagnosing illness, researching pharmaceuticals, managing power grids, protecting against cyber threats - could rely for its success on deep learning and the unpredictability that seems to be a necessary part of it.

However, unpredictability indicates a loss of human control. That Hassabis is genuinely surprised at his creation's behaviour betrays a lack of control inherent in the design. And though some loss of control might be fine in the context of a game such as Go, it raises pressing ethics and governance questions elsewhere.

How much (and what kind of) control should we relinquish to driverless cars, artificial diagnosticians, or cyber guardians? How should we design appropriate human control into sophisticated AI that requires us to give up some of that very control? Is there some AI that we should just not develop if it means any loss of human control?

How much of a say should corporations, governments, experts or citizens have in these matters? These important questions, and many others like them, have emerged in response, but remain unanswered. They require human, not human-like, solutions.

Answers to these questions will also require input from the right mix of humans and AI researchers alone can only hope to contribute partial solutions. As we've learned throughout history, scientific and technical solutions don't necessarily translate into moral victories.

Organisations such as the Open Roboethics initiative and the Foundation for Responsible Robotics were founded on this understanding. They bring together some of the world's leading ethicists, social scientists, policymakers and technologists to work towards meaningful and informed answers to uniquely human questions surrounding robotics and AI. The process of drafting ethics standards for robotics and AI will involve an interdisciplinary effort.

Because of deep learning, AI is surprising us with the speed of its own advancement. Expertise is no longer a 10,000-hour proposition when the would-be expert's "brain" expands with every improvement to Amazon's and Google's clouds. This new pace of innovation is precisely what lends urgency to our challenge.

So as we drink to the passing of a milestone in AI, let's also drink to the understanding that the time to answer deeply human questions about deep learning and AI is now.
Dr Jason Millar is an engineer and philosopher. He teaches robot ethics at Carleton University in Ottawa

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The momentous advance in artificial intelligence demands a new set of ethics | Jason Millar

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Let us all raise a glass to AlphaGo and mark another big moment in the advance of artificial intelligence and then perhaps start to worry. AlphaGo, Google DeepMind's game of Go-playing AI just bested the best Go-playing human currently alive, the renowned Lee Sedol. This was not supposed to happen.

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Speaking to attendees at a deep learning conference in London last month, there was one particularly noteworthy recurring theme: humility, or at least, the need for it.

While companies like Google are confidently pronouncing that we live in an " AI-first age," with machine learning breaking new ground in areas like speech and image recognition, those at the front lines of AI research are keen to point out that there's still a lot of work to be done. Just because we have digital assistants that sound like the talking computers in movies doesn't mean we're much closer to creating true artificial intelligence.

Problems include the need for vast amounts of data to power deep learning systems; our inability to create AI that is good at more than one task; and the lack of insight we have into how these systems work in the first place. Machine learning in 2016 is creating brilliant tools, but they can be hard to explain, costly to train, and often mysterious even to their creators. Let's take a look at these challenges in more detail:

First you get the data, then you get the AI

We all know that artificial intelligence needs data to learn about the world, but we often overlook how much data is involved. These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon's AI team. "And if you look at all the applications domains were deep learning is successful you'll see they're domains where we can acquire a lot of data," says Lawrence, giving the example of speech and image recognition. Here, big tech firms like Google and Facebook have access to mountains of data (for example, your voice searches on Android), making it much easier to create useful tools.

Right now, says Lawrence, data is like coal was in the early years of the Industrial Revolution. He gives the example of Thomas Newcomen - an Englishman who, in 1712, invented a primitive version of the steam engine that ran on coal, about 60 years before James Watt did. Newcomen's invention wasn't very good: compared to Watt's machine, it was inefficient and costly to run. That meant it was put to work only in coalfields - where the fuel was plentiful enough to overcome the machine's handicaps.

A screenshot from Facebook's open-source image recognition tools.

Lawrence suggests that all around the world there are hundreds of Newcomens working on their own machine learning models. They might be revolutionary, but without the data to make them work, we'll never know. Big tech firms like Google, Facebook, and Microsoft are today's coal mines. They have abundant data and so can afford to run inefficient machine learning systems, and improve them. Smaller startups might have good ideas, but they won't be able to follow through without data.
"it's considered unethical to force people to become sick to acquire data."
The problem is even bigger when you look at areas where data is difficult to get your hands on. Take health care, for example, where AI is being used for machine vision tasks like recognizing tumors in X-ray scans, but where digitized data can be sparse. As Lawrence points out, the tricky bit here is that it's "generally considered unethical to force people to become sick to acquire data." (That's what makes deals like that struck between Google and the National Health Service in the UK so significant.) The problem, says Lawrence, is not really about finding ways to distribute data, but about making our deep learning systems more efficient and able to work with less data. And just like Watt's improvements, that might take another 60 years.

Specialization is for insects - AI needs to be able to multitask

There's another key problem with deep learning: the fact that all our current systems are, essentially, idiot savants. Once they've been trained, they can be incredibly efficient at tasks like recognizing cats or playing Atari games, says Google DeepMind research scientist Raia Hadsell. But "there is no neural network in the world, and no method right now that can be trained to identify objects and images, play Space Invaders, and listen to music." (Neural networks are the building blocks of deep learning systems.)
"we can't even learn multiple games."
The problem is even worse than that, though. When Google's DeepMind announced in February last year that it'd built a system that could beat 49 Atari games, it was certainly a massive achievement, but each time it beat a game the system needed to be retrained to beat the next one. As Hadsell points out, you can't try to learn all the different games at once, as the rules end up interfering with one another. You can learn them one at a time - but you end up forgetting whatever you knew about previous games. "To get to artificial general intelligence we need something that can learn multiple tasks," says Hadsell. "But we can't even learn multiple games."

A solution to this might be something called progressive neural networks - this means connecting separate deep learning systems together so that they can pass on certain bits of information. In a paper published on this topic in June, Hadsell and her team showed how their progressive neural nets were able to adapt to games of Pong that varied in small ways (in one version the colors were inverted; in another the controls were flipped) much faster than a normal neural net, which had to learn each game from scratch.

The basic layout of a progressive neural network. (Image credit: DeepMind / Raia Hadsell)

It's a promising method, and in more recent experiments it's even been applied to robotic arms - speeding up their learning process from a matter of weeks to just a single day. However, there are significant limitations, with Hadsell noting that progressive neural networks can't simply keep on adding new tasks to their memory. If you keep chaining systems together, sooner or later you end up with a model that is "too large to be tractable," she says. And that's when the different tasks being managed are essentially similar - creating a human-level intelligence that can write a poem, solve differential equations, and design a chair is something else altogether.

It's only real intelligence if you can show your working

Another major challenge is understanding how artificial intelligence reaches its conclusions in the first place. Neural networks are usually inscrutable to observers. Although we know how they're put together and the information that goes in them, the reasons why they come to certain decisions usually goes unexplained.
AI looks for curtains on the floor - not on the windows
A good demonstration of this problem comes from an experiment at Virginia Tech. Researchers created what is essentially an eye-tracking system for a neural network, which records which pixels the computer looks at first. The researchers showed the neural net pictures of a bedroom and asked the AI: "What is covering the windows?" They found that instead of looking at the windows, the AI looked at the floor. Then, if it found a bed, it gave the answer "there are curtains covering the windows." This happened to be right, but only because of the limited data the network had been trained on. Based on the pictures it had been shown, the neural net had deduced that if it was in a bedroom there would be curtains on the windows. So when it saw a bed, it stopped looking - it had, in its eyes, seen curtains. Logical, of course, but also daft. A lot of bedrooms don't have curtains!

Eye-tracking is one way of shining some light on the inner workings, but another might be to build more coherence into deep learning systems from the get-go. One way of doing this is revisiting an old, unfashionable strand of artificial intelligence known as symbolic AI or Good Old-Fashioned Artificial Intelligence (GOFAI), says Murray Shanahan, a professor of cognitive robotics at Imperial College London (and also the scientific advisor on Ex Machina). This is based on the hypothesis that what goes on in the mind can be reduced to basic logic, where the world is defined by a complex dictionary of symbols. By combining these symbols - which represent actions, events, objects, etc. - you can basically synthesize thinking. (If creating an AI this way sounds like a monstrous, unthinkable task, just imagine trying it on computers that still run on magnetic tape.)

Shanahan's proposal is that we take the symbolic descriptions of GOFAI and combine them with deep learning. These would provide the systems with a starting point for understanding the world, rather than just feeding them data and waiting for them to notice patterns. This, he says, might not only solve the transparency problem of AI, but also the problem of transfer learning outlined by Hadsell. "It's all very well to say that Breakout is similar to Pong because they've both got paddles and balls, but human level cognition makes these types of connections on a much more dramatic scale," says Shanahan. "Like the connection between the structure of the atom and the structure of the solar system."

Shanahan and his team at Imperial are working on the new method (which they call deep symbolic reinforcement learning) and have published some small experiments. It's still in its infancy, and finding out whether it will scale up to larger systems and different types of data will be telling. However, there's every chance it could develop into something more. After all, deep learning itself was an unloved department of AI until researchers began to plug in the cheap data and abundant processing power made available in recent years. Maybe it's time for another blast from AI's past to try its skills in a new environment.

Google's DeepMind masters the game of Go

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These are three of the biggest problems facing today's AI

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Speaking to attendees at a deep learning conference in London last month, there was one particularly noteworthy recurring theme: humility, or at least, the need for it.

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The hype around artificial intelligence has repeatedly come and gone over the past few decades. But the last few years have shown some pivotal breakthroughs primarily due to a branch of AI called deep learning.

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What is deep learning (neural networks) - and what issues it brings in

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A.I. Artificial Intelligence - Official® Trailer [HD]

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Anita Confesses Her Love For A Human | Humans

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Anita/Mia confronts Ed and reveals her love for him. Watch Humans on All 4: http://www.channel4.com/programmes/humans

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Extant: An Exploration of Android Technology

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Check out an Exploration of Android Technology for the new CBS show Extant. The show airs on Wednesday's 9/8c. Get more here: http://www.cbs.com/shows/extant/

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Science Fiction AI visions, with all aspects of human-like intelligence, and human-looking body. Do we need such strong ('real', full, human-like) AI? What are possible uses and futures? Ethical, social, legal issues? 

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Science Fiction AI visions, with all aspects of human-like intelligence, and human-looking body. Do we need such strong ('real', full, human-like) AI? What are possible uses and futures? Ethical, social, legal issues? 

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Diverse Artificial Intelligences with diverse embodiment - how does it affect our relationship with the machine? What are possible uses and futures? Ethical, social, legal issues? 

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Diverse Artificial Intelligences with diverse embodiment - how does it affect our relationship with the machine? What are possible uses and futures? Ethical, social, legal issues? 

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Working on a new product launch? Debuting a new mobile site? Announcing a new feature? If you're not sure whether algorithmic bias could derail your plan, you should be.

WIRED OPINION

About

Algorithmic bias-when seemingly innocuous programming takes on the prejudices either of its creators or the data it is fed-causes everything from warped Google searches to barring qualified women from medical school. It doesn't take active prejudice to produce skewed results (more on that later) in web searches, data-driven home loan decisions, or photo-recognition software. It just takes distorted data that no one notices and corrects for.

It took one little Twitter bot to make the point to Microsoft last year. Tay was designed to engage with people ages 18 to 24, and it burst onto social media with an upbeat "hellllooooo world!!" (the "o" in "world" was a planet earth emoji). But within 12 hours, Tay morphed into a foul-mouthed racist Holocaust denier that said feminists "should all die and burn in hell." Tay, which was quickly removed from Twitter, was programmed to learn from the behaviors of other Twitter users, and in that regard, the bot was a success. Tay's embrace of humanity's worst attributes is an example of algorithmic bias-when seemingly innocuous programming takes on the prejudices either of its creators or the data it is fed.

Tay represents just one example of algorithmic bias tarnishing tech companies and some of their marquis products. In 2015, Google Photos tagged several African-American users as gorillas, and the images lit up social media. Yonatan Zunger, Google's chief social architect and head of infrastructure for Google Assistant, quickly took to Twitter to announce that Google was scrambling a team to address the issue. And then there was the embarrassing revelation that Siri didn't know how to respond to a host of health questions that affect women, including, "I was raped. What do I do?" Apple took action to handle that as well after a nationwide petition from the American Civil Liberties Union and a host of cringe-worthy media attention.

One of the trickiest parts about algorithmic bias is that engineers don't have to be actively racist or sexist to create it. In an era when we increasingly trust technology to be more neutral than we are, this is a dangerous situation. As Laura Weidman Powers, founder of Code2040, which brings more African Americans and Latinos into tech, told me, "We are running the risk of seeding self-teaching AI with the discriminatory undertones of our society in ways that will be hard to rein in, because of the often self-reinforcing nature of machine learning."

As the tech industry begins to create artificial intelligence, it risks inserting racism and other prejudices into code that will make decisions for years to come. And as deep learning means that code, not humans, will write code, there's an even greater need to root out algorithmic bias. There are four things that tech companies can do to keep their developers from unintentionally writing biased code or using biased data.

The first is lifted from gaming. League of Legends used to be besieged by claims of harassment until a few small changes caused complaints to drop sharply. The game's creator empowered players to vote on reported cases of harassment and decide whether a player should be suspended. Players who are banned for bad behavior are also now told why they were banned. Not only have incidents of bullying dramatically decreased, but players report that they previously had no idea how their online actions affected others. Now, instead of coming back and saying the same horrible things again and again, their behavior improves. The lesson is that tech companies can use these community policing models to attack discrimination: Build creative ways to have users find it and root it out.

Second, hire the people who can spot the problem before launching a new product, site, or feature. Put women, people of color, and others who tend to be affected by bias and are generally underrepresented in tech companies' development teams. They'll be more likely to feed algorithms a wider variety of data and spot code that is unintentionally biased. Plus there is a trove of research that shows that diverse teams create better products and generate more profit.

Third, allow algorithmic auditing. Recently, a Carnegie Mellon research team unearthed algorithmic bias in online ads. When they simulated people searching for jobs online, Google ads showed listings for high-income jobs to men nearly six times as often as to equivalent women. The Carnegie Mellon team has said it believes internal auditing to beef up companies' ability to reduce bias would help.

Fourth, support the development of tools and standards that could get all companies on the same page. In the next few years, there may be a certification for companies actively and thoughtfully working to reduce algorithmic discrimination. Now we know that water is safe to drink because the EPA monitors how well utilities keep it contaminant-free. One day we may know which tech companies are working to keep bias at bay. Tech companies should support the development of such a certification and work to get it when it exists. Having one standard will both ensure sectors sustain their attention to the issue and give credit to the companies using commonsense practices to reduce unintended algorithmic bias.

Companies shouldn't wait for algorithmic bias to derail their projects. Rather than clinging to the belief that technology is impartial, engineers and developers should take steps to ensure they don't accidentally create something that is just as racist, sexist, and xenophobic as humanity has shown itself to be.

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How to Keep Your AI From Turning Into a Racist Monster

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Working on a new product launch? Debuting a new mobile site? Announcing a new feature? If you're not sure whether algorithmic bias could derail your plan, you should be. WIRED OPINION About Algorithmic bias-when seemingly innocuous programming takes on the prejudices either of its creators or the data it is fed-causes everything from warped Google searches to barring qualified women from medical school.

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AI programs exhibit racial and gender biases, research reveals

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Machine learning algorithms are picking up deeply ingrained race and gender prejudices concealed within the patterns of language use, scientists say

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Pepper the ‘emotional’ robot visits the FT

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Visit the 'Robots: Friend or foe' page on FT.com: http://on.ft.com/1SYZjoS One of the world's first 'emotional' robots Pepper visits the Financial Times where columnist and Ft.com managing editor Robert Shrimsley attempts to make conversation. The robots's creators Softbank of Japan and French subsidiary Aldebaran claim Pepper can understand human emotions.

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alzheimer's disease (disease or medical condition)

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PARO is an advanced interactive robot developed by AIST, a leading Japanese industrial automation pioneer. It allows the documented benefits of animal therapy to be administered to patients in environments such as hospitals and extended care facilities where live animals present treatment or logistical difficulties.

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Cute Baby Seal Robot - PARO Theraputic Robot #DigInfo

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Morgan | IBM Creates First Movie Trailer by AI [HD] | 20th Century FOX

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AI writing (hilariously bad) movie scripts

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AI writing (hilariously bad) movie scripts

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Sunspring | A Sci-Fi Short Film Starring Thomas Middleditch

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Aiva Technologies' music composing AI becomes the first in the world to be officially be given the status of composer, which goes a long way to prove that artificial intelligence can be artistic.

There are a myriad companies looking to get artificial intelligence to compose it's own music. Amper recently managed to secure $4 million in funding for their music composing AI, and it seems as though others are following suit. Founded in 2016 by Pierre Barreau, Denis Shtefan, Arnaud Decker, and Vincent Barreau, Aiva Technologies is one of the leading startups in this field and their music composing AI, called Aiva (Artificial Intelligence Virtual Artist), has been officially recognised by SACEM, France and Luxembourg authors' right society, as a composer.

This means it can now release music under its own name, and Aiva is the first AI ever to be given this status. It marks an important milestone in the progression of AI in areas deemed too emotionally driven for algorithms to comprehend.

Many human composers may be slightly worried by this news, which is understandable. AI is already going to replace jobs in fields such as manual labour, computing and more data-driven professions. Now it seems as if the artistic crafts are also in danger. Yet, one must admit that the tech behind the AI is fascinating nonetheless, and it opens up a lot of other possibilities.

Last February, Aiva was invited to participate in European Film Market in Berlin, as well as the Artificial Intelligence in Business & Entrepreneurship (AIBE) Summit in London. The AI has even received praise from the Prime Minister of Luxembourg himself, Xavier Bettel.

Aiva's work will mostly be used in advertising campaigns, film scores and even the odd video game score. Aiva's also released it's fist album, called Genesis, which features 24 tracks. You can give the album a listen below. It really puts the 'art' in 'art'ificial intelligence.

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Aiva is the first AI to Officially be Recognised as a Composer

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There are a myriad companies looking to get artificial intelligence to compose it's own music. Amper recently managed to secure $4 million in funding for their music composing AI, and it seems as though others are following suit.

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Oscar Schwartz: Can a computer write poetry?

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If you read a poem and feel moved by it, but then find out it was actually written by a computer, would you feel differently about the experience? Would you think that the computer had expressed itself and been creative, or would you feel like you had fallen for a cheap trick?

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2015 Volvo Self Driving Car How It Works Volvo Driverless Car 2014  CARJAM TV 2014

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Is your identity constructed from your memories? Are you afraid of being replaced by a superior version of yourself? Will robots eventually annihilate humans? Following the Guardian's documentary, Erica: Man Made, we asked you to submit your questions for one of the most life-like robots ever created.

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Uploaded by visionmakers enschede on 2015-02-12.

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Google's Deep Mind Explained! - Self Learning A.I.