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SAN FRANCISCO - Uber envisions a future in which a fleet of vehicles can make the most complex maneuvers while carting passengers around without the help of a driver. To achieve that, cars will need to get a whole lot smarter.

Enter Gary Marcus and Zoubin Ghahramani. The two men are being appointed as co-directors of Uber's new in-house research arm on artificial intelligence, which the ride-hailing company unveiled on Monday. The research arm's aim is to apply A.I. in areas like self-driving vehicles, along with solving other technological challenges through machine learning.

The two are joining Uber through an acquisition of their start-up, Geometric Intelligence. Unlike most A.I. start-ups that generally follow one method of study of artificial intelligence, Geometric Intelligence takes a multidisciplinary approach to the field.

All 15 people from the start-up will be absorbed by Uber. Terms of the deal were not disclosed.

The acquisition and new research arm, which will be called Uber's A.I. Labs, exemplifies how seriously Silicon Valley tech companies are betting on artificial intelligence. Google, Facebook and others have also pushed into artificial intelligence, which underlies voice recognition software, digital assistants like Amazon's Alexa and Apple's Siri, and technologies like self-driving cars. Many companies are racing to bring on new A.I. talent to compete against one another.

"Every major company realizes how essential A.I. is to what they're doing," Dr. Marcus said in an interview. "Because of the scale of data people are operating on, even the smallest gains in efficiency can turn out enormous changes at these companies, especially in terms of profit."

With the Geometric Intelligence deal, Uber, which is now valued at close to $70 billion, said it hoped that Dr. Marcus's team could harness the wealth of data it collects from the millions of daily Uber rides. The company wants to use the data to make major advances in how computers behind self-driving vehicles think and make decisions on the road.

Many of Silicon Valley's biggest tech companies, such as Google and Facebook, have tried to commercialize artificial intelligence through the application of algorithms modeled largely on how the human brain functions. This method, called deep learning, leans heavily on the vast data sets that private technology companies own and that are used to train computers to do simple tasks, such as match patterns or recognize faces in photographs.

Part of what drew Uber to Dr. Marcus's team is that his start-up is tackling artificial intelligence in a different way. Rather than taking just one approach like deep learning, Geometric Intelligence combines data scientists who use varying techniques to study artificial intelligence, including the Bayesian and "evolutionary" methods.

Dr. Marcus, who helped found Geometric Intelligence in late 2014, said the gist of his philosophy goes something like this: Instead of training machines by feeding them enormous amounts of data, what if computers were capable of learning more like humans by extrapolating a system of rules from just a few or even a single example?

In recent years, researchers at institutions such as the Massachusetts Institute of Technology, New York University and the University of Toronto have also worked on similar theories. Using this approach, some reported a breakthrough in "one-shot" machine learning last December, in which artificial intelligence advances surpassed human capabilities for a narrow set of vision-related tasks.

Besides autonomous vehicles, Uber said it expected its A.I. Labs to apply its method to other tasks, including combating fraud, extracting information from street signs and learning to improve its mapping research and capabilities.

"When step function changes in this field occur, we're going to see very significant differences in how businesses run themselves," Jeff Holden, Uber's chief product officer, said in an interview. He said the A.I. Labs and Uber's Advanced Technologies Center, which is home to the company's self-driving car research, will work in tandem with each other.

"It's going to be a very long time before a self-driving car will be able to make all the kinds of trips that Uber does every single day," Mr. Holden said. "But the answers to this are all going to come in the form of artificial intelligence."

Mr. Holden said recruiting A.I. research talent is highly competitive, especially as Google, Apple and Tesla are also developing self-driving cars or related projects.

"From a defensive perspective, if someone else develops them first, we're in trouble," he said.

Correction:

Because of an editing error, an earlier version of this article misstated the surname of a new co-director of Uber's in-house research arm on artificial intelligence. He is Zoubin Ghahramani, not Ghahraman.

A version of this article appears in print on , on Page B4 of the New York edition with the headline: Uber Debuts Lab to Make Self-Driving Cars Smarter. Order Reprints | Today's Paper | Subscribe

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Uber Bets on Artificial Intelligence With Acquisition and New Lab

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SAN FRANCISCO - Uber envisions a future in which a fleet of vehicles can make the most complex maneuvers while carting passengers around without the help of a driver. To achieve that, cars will need to get a whole lot smarter. Enter Gary Marcus and Zoubin Ghahramani.

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Universal and basic

Elon Musk just joined a number of other financial experts who are suggesting that universal basic income (UBI) may be the solution to the looming problem of unemployment, which stands to arise due to the large-scale automation of jobs as a result of artificial intelligence (AI).

Although there are a number of ways that the system could work, in short, UBI is a system where the government (or another public entity) gives each citizen a fixed income, regardless of employment status.

The idea of UBI has been around for awhile now. President Barack Obama himself even briefly touched upon it in an interview for , and Switzerland actually tried implementing it last June, only to have it rejected by voters.

In a recent CNBC interview, Musk explained that he doesn't see any other option at the moment, aside from UBI. "There is a pretty good chance we end up with a universal basic income, or something like that, due to automation," Musk tells CNBC. "Yeah, I am not sure what else one would do. I think that is what would happen."

The answer to automation

To break down the rational a bit more, many careers are expected to be taken over by automation in the coming years. Drivers, for instance, are slowly becoming obsolete-whether it's taxi drivers or truck drivers (and other jobs are seeing similar issues, like restaurant workers and even writers).

So, what impact will this have?

Well, it means that we'll be needing fewer truck drivers, Musk explains; however, it also means that a truck driver's job could transition to becoming something like a fleet operator - one driver to drive them all, so to speak. In this transition, some people would inevitably lose their jobs or be forced to accept lower paying ones. Musk believes that the government can support these workers in transition through UBI.

The Tesla and SpaceX CEO is not an enemy of automation, of course. "People will have time to do other things, more complex things, more interesting things," says Musk. "Certainly more leisure time."

While the transition likely won't happen tomorrow, it has become increasingly clear that automation will redefine a number of industries. There has to be a way to facilitate the transition...and UBI might just be the answer.

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Elon Musk: "There's a Pretty Good Chance We'll End Up With Universal Basic Income"

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Experts assert that, in the coming years, robots will take over hundreds of thousands of jobs that are traditionally done by humans. In a recent interview, Elon Musk stated that Universal Basic Income may be the only economic response to this increasing automation and job loss.

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Google says it has cut its vast data centres' energy use by 15% by applying artificial intelligence to manage them more efficiently than humans.

The servers that power billions of web searches, streamed films and social media accounts are estimated to account for approximately 2% of global greenhouse gas emissions. Google is believed to have one of the biggest fleets of them in the world.

On Wednesday, Google said it had proved it could cut total energy use at its data centres by 15% by deploying machine learning from DeepMind, the British AI company it bought in 2014 for about £400m. Such centres require significant energy for cooling, as well as constant adjustments to air temperature, pressure and humidity, to run as efficiently as possible.

Mustafa Suleyman, DeepMind's co-founder, said that the level of complexity and number of variables meant the job of managing data centres was one where an algorithm could outperform a human.

He said: "It's one of those perfect examples of a setting where humans have a really good intuition they've developed over time but the machine-learning algorithm has so much more data that describes real-world conditions [five years in this case].

"It's much more than any human has ever been able to experience, and it's able to learn from all sorts of niche little edge cases seen in the data that a human wouldn't be able to identify. So it's able to tune the settings much more subtly and much more accurately."

Suleyman said the reduction in energy use was achieved through a combination of DeepMind more accurately predicting the incoming computational load - ie when people were mostly likely to request data-hungry YouTube videos - and match that prediction very quickly to the cooling load required. "It's about tweaking all of the knobs simultaneously," he said.

The environmental impact of the online world has come under increasing scrutiny in recent years, as data centres' share of global emissions has risen to be on a par with those from aviation. Google first disclosed its carbon footprint in 2011 - it was roughly equivalent to Laos's annual emissions - and says that since then its data centres have improved so that they get 3.5 times the computing power for the same amount of energy.

The trial using machine learning to further cut those data centres' energy - and carbon emissions - began two years ago, and was tested on "more than 1%" of its servers, Suleyman said. It is now being used across a "double-digit percentage" of all Google's data centres globally and will be applied across all of them by the end of the year.

Google does not disclose exactly how much energy its data centres use, but says as a company it's responsible for 0.01% of global electricity use, and much of that is data centres. DeepMind has cut Google's energy use for cooling by 40%, and total energy use by 15%.

"I really think this is just the beginning. There are lots more opportunities to find efficiencies in data centre infrastructure," said Suleyman. "One of the most exciting things is the kind of algorithms we develop are inherently general ... that means the same machine-learning system should be able to perform well in a wide variety of environments [such as power generation facilities and energy networks]."

Google has data centres in North and South America, Europe and Asia. Sophia Flucker, a director of Operational Intelligence, a UK-based consultancy that helps data centres cut their energy use, said it was feasible that Google had achieved such a big reduction. "I've worked with some award-winning data centres, which still had plenty of room for improvement," she said.
Topics

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Google uses AI to cut data centre energy use by 15%

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Google says it has cut its vast data centres' energy use by 15% by applying artificial intelligence to manage them more efficiently than humans. The servers that power billions of web searches, streamed films and social media accounts are estimated to account for approximately 2% of global greenhouse gas emissions.

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Top 3 AI Devices That Make Your Life Easier - Ciklum

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Artificial intelligence is making our lives easier each day. It's being done so gradually, most people wouldn't even notice. Find out how it happens.

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Internet Health Management - Latest in Digital Healthcare

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Internet Health Management focuses on the digital advancements in patient care and healthcare monitoring.

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Ride-sharing service Uber has acquired a New York-based artificial intelligence start-up which it hopes can speed up its progress in creating self-driving cars.

The deal, for an undisclosed sum, will see Uber gain 15 specialist researchers who will form a new division at the company known as Uber AI Labs.

Geometric Intelligence's co-founder Gary Marcus told the BBC the team would have a wide brief - ranging from improving traffic predictions to working towards a future of flying cars.

Recent reports have suggested Uber's aggressive growth, which has required heavily subsidising fares, meant the firm lost more than $2bn (£1.6bn) last year. That figure is likely to go up in 2016.

But Uber's chief product officer Jeff Holden said the enormous losses were not a serious concern to the company.

"We're actually in very good shape," he told the BBC.

"Those sorts of things look bad from the outside."
Efficiency
Regardless, Uber is pushing forward with attempts to become a far more efficient transport company.

In the near term, that means the new AI team will be improving on the company's ability to predict when and where cars may be needed, drawing in data from various factors, such as weather forecasts.

Longer term it means accelerating the roll-out of Uber's self-driving car fleet, which is already being tested on roads in Pittsburgh and San Francisco.

Currently, those cars, made by Volvo, have a person sitting at the wheel ready to take control.

But eventually the aim is to remove the need for a human altogether, putting the several hundred thousand Uber drivers out of a job.

"Historically what you've seen is as technology has taken jobs away from people, new jobs have opened up," Mr Holden said.

Mr Marcus said job losses would be a necessary by-product to achieve safer roads.

"The way I think about it is there are trade-offs, but one of the plusses is that eventually the roads are going to be much safer for people."

Looking further into the future, Mr Marcus imagines a world of flying cars taking us to work.

"They're going to allow people to take long commutes at 75 or 150 mph where you'd otherwise get stuck in traffic. It's really, in a not-too-distant future, going to be something that is practical."

Uber recently published a white paper exploring the idea.
Real-world consequence
The Geometric Intelligence team will become Uber employees and most of the team will relocate to Uber's headquarters in San Francisco.

The start-up has up until now been funded by private investors and will maintain its close ties with New York University.

Mr Marcus said his company stood out because it used a different approach to artificial intelligence, one which takes inspiration from how a child's brain develops, particularly when discovering language.

Companies like Facebook and Google are investing heavily in so-called "deep learning". This technique involves feeding a computer millions of pieces of information, such as images, so it can learn patterns and begin to recognise objects and categorise what it sees.

Mr Marcus said he felt this approach fell short.

"Deep learning is not all that it's cracked up to be," he said.

"It's very good for certain problems, but it doesn't allow us to do the kind of inferences that people often do. We need next-generation techniques."

Existing technology is promising, he said, but has too low a success rate to be considered safe to use in self-driving cars.

"What I see is approximation with statistics. What people do is get something that works 80% of the time and they're happy. Nobody dies if I tell you you'd like this book and you don't."

Geometric Intelligence's approach is to work on systems that as well as using large data sets, can also be taught rules that mean it can learn without relying on having pre-inputted information.

"When you work on a problem with a real-world consequence like driving, you want to be as close to 100% as possible."

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Uber launches artificial intelligence lab

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Ride-sharing service Uber has acquired a New York-based artificial intelligence start-up which it hopes can speed up its progress in creating self-driving cars. The deal, for an undisclosed sum, will see Uber gain 15 specialist researchers who will form a new division at the company known as Uber AI Labs.

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While on their way to Jupiter, a ship's crew gets double crossed by its sentient computer, Hal. Slowly, the "infallible" Hal manages to kill most of the astronauts. A fierce battle ensures between Hal and the lone surviving astronaut, Dave, before Dave finally manages to shut down the computer.

When '2001: A Space Odyssey' was released in 1968, the epic science-fiction film was more of a fiction -- but no longer.

Forty nine years later, machines are doing almost everything that human intelligence can do -- analysing customer behaviour, helping doctors diagnose diseases better and creating individual learning programmes to name just a few; functions commonly known as artificial intelligence (AI).

No wonder that SoftBank founder and CEO Masayoshi Son said at the HT Leadership Summit on Friday that machines, which are multiple times more intelligent than Albert Einstein, will be an essential part of our future. "I am better in forecasting, predicting, talking about 30 years later, than three years... for the first time we are facing something that is so much smarter than us.

Technologies, such as AI and virtual reality, are increasingly being used in various fields, including healthcare, retail, e-commerce, education and real estate, to understand the buyer better and boost sales. It's like Iron Man's JARVIS, the AI (artificial intelligence) assistant that gives Tony Stark all the information he needs, helps him fight bad men, manages his estate, house and even business.

IBM's cognitive computing platform, another name for AI, Watson, is playing a major role in bringing technology into real life. One of the early adopters is Manipal University, which is using it in Oncology. It is impossible for a doctor to read every research paper that comes out on cancer treatment - last year, 130 of them were published every day - but Watson can go through them, analyse them and suggest better ways of treatment.

It is also being used in the field of retail, fashion and e-commerce. Soon, from searching a red shirt or a blue suit based on textual entries, Watson will help in searching them through images, videos and voice. "Rather than searching for products, you will be able to search for occasions -- for a marriage or a casual dinner," said Nipun Mehrotra, vice-president, strategy and growth initiatives, IBM Asia Pacific.

Watson is also learning culinary techniques in some of the best colleges in Bangalore, and has been assisting chefs with recipes while cooking. It is also helping designer create new designs.

Snapdeal, India's third-largest e-commerce company, is working on a new system, which will extract information from a picture, identify the dress, pattern, style and other information, and throw up relevant choices for its buyers, says Rajiv Mangla, chief technology officer, Snapdeal, which recently bought deep learning company Fashionate.

In India, almost every second day a new startup is coming up in the AI space. A little more than year ago there were 20-30 of them. Now there are more than 150.

Fashion e-tailer Voylla wants to use AI to play the role of a shopping companion. A buyer can take a picture of the dress or selfie, and the platform shows what will go best with it. "We are mimicking human behaviour and automating it," says Jagrati Shringi, founder of Voylla.

Soon, Shringi will help her buyers design their own jewellery using AI!

For companies like Paytm, the country's largest mobile wallet company, which also has the ambition to become a full-fledged bank, AI can help in detecting frauds, upselling services and understanding the needs of consumers better.

"The thing with mobile is that people don't like typing, so the more you can reduce typing, the more friction free it will become," says Amit Singhal, former head of Google Search, who now sits on Paytm's board. "Even if I can reduce three clicks (while making a transaction), and give them (consumers) the destination faster, user will be happy."

Real estate companies are still taking baby steps in the field, but the opportunity is huge.

"So far you can just view things. You can't interact with them... We are working with real estate companies to offer buyers options to create models, finishes, use furniture, paints - all within the same dimension," says Goutam Tewari, founder and CEO of SmartVizX, a virtual reality startup.

AI is coming to education, too. "If a professional enters the requirements, our algorithm will pull out material from different courses, customise it and offer a unique course," claims Bradley Loiselle, CEO of betterU, an education marketplace.

Another online education platform, Pluralsight, wants to create customised courses based on learning styles. "AI will help in understanding what kind of learning the student needs - fast, slow, or dedicated (who wants to follow every step from A to Z), and create hyper-personalised ways of learning," says Arun Rajamani, country head of Pluralsight India.

It looks like SoftBank CEO Son is on the right track. He's building a humanoid (a robot that looks like a human) called Pepper (Pepper Potts is also Stark's girlfriend in Iron Man, and the CEO of Stark Industries), which will understand human emotions, and react accordingly. "I am creating robot with a good heart," says Son.

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From the theatres, AI steps into the real world

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Forty nine years later, machines are doing almost everything that human intelligence can do -- analysing customer behaviour, helping doctors diagnose diseases better and creating individual learning programmes to name just a few; functions commonly known as artificial intelligence (AI).

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The United Nations forecasts that the global population will rise from 7.3 billion to nearly 10 billion by 2050, a big number that often prompts warnings about overpopulation. Some have come from neo-Malthusians, who fear that population growth will outstrip the food supply, leaving a hungry planet. Others appear in the tirades of anti-immigrant populists, invoking the specter of a rising tide of humanity as cause to slam borders shut. Still others inspire a chorus of neo-Luddites, who fear that the " rise of the robots " is rapidly making human workers obsolete, a threat all the more alarming if the human population is exploding.

Before long, though, we're more likely to treasure robots than to revile them. They may be the one thing that can protect the global economy from the dangers that lie ahead.

An increase of 2.5 billion people may sound catastrophic. But what matters for economic growth is not the number of people but the rate of population growth. Since its peak in the 1960s, that rate has slumped by almost half to just 1 percent, and the U.N. forecast assumes that this slowdown will continue. Women are having fewer children, so fewer people are entering the working ages between 15 and 64, and labor-force growth is poised to decline from Chile to China. At the same time, owing to rapid advances in health care and medicine, people are living longer , and most of the coming global population increase will be among the retirement crowd. These trends are toxic for economic growth, and boosting the number of robots may be the easiest answer for many countries.

One simple way to estimate how fast an economy can grow is by adding working-age population growth and productivity growth: If the number of workers and output per worker are both increasing by 1 percent a year, then economic output should rise by roughly 2 percent. Over the past decade, both sides of that equation have declined dramatically across the world. In the United States, productivity growth has fallen by almost half from its postwar average, but growth in the labor force has slid even faster, dropping by two-thirds to an average pace of 0.5 percent, according to calculations performed for my book. Though many explanations have been offered for the slow recovery from the global financial crisis of 2008, the clearest answer may be aging populations. Something will have to fill the void left by, say, retiring farmers, and particularly at a time of rising hostility to immigrants, it is likely to be farmbots.

It may not be long before economists are worrying about a global shortage of robots. In many industrial countries, from Germany to Japan to South Korea, growth in the working-age population has already peaked, acting as a drag on the economy. Widely overlooked, however, is the fact that the population-growth slowdown is unfolding even faster in the emerging world, according to my research.

Consider the turning point that China hit last year. For the first time since records began in the 1950s, its working-age population growth was negative. As a result, China's labor force is expected to lose 1 million workers each year for the foreseeable future, and it is also aging rapidly. Studies by Evercore ISI, a research firm, show that the elderly share of the population is rising more than twice as fast as it did in the United States and more than four times faster than in France at similar stages of development. Asked by an alarmed dinner companion about the threat robots posed to jobs in China, Nobel economist Daniel Kahneman responded: "You just don't get it. In China, the robots are going to come just in time." No wonder Beijing now offers heavy subsidies to companies involved in industrial automation.

And timing is critical. Those who fear the job-destroying impact of machines say this generation of technology is different because it is coming so fast. If older generations created tools for use by humans, such as sewing machines, the new forms of automation are imbued with artificial intelligence, capable of "machine learning" and of rapidly replacing humans in a broad swath of jobs, from manufacturing to services - even jobs that involve writing about robots. Concern about this disruptive advance has been stirred up by authorities such as Oxford University researchers Carl Benedikt Frey and Michael Osborne, who predicted in 2013 that nearly half of U.S. jobs would be at risk from automation in the next decade or two.

These alarms have sounded before, however. The Machine Intelligence Research Institute at the University of California at Berkeley has found that today, the average forecast for when artificial intelligence will arrive is about 20 years. But that was also the standard prediction in 1955. And often, humans find a way of working with their automated creations. After the introduction of supermarket scanners, the number of cashiers grew. Though legal-discovery software appeared to threaten the jobs of paralegals, their ranks increased, too. Now, many fear that self-driving trucks will displace millions of American truckers, but they may create more and better jobs for those who service those increasingly complex vehicles.

If automation was displacing human workers as fast as implied in recent books like Martin Ford's " The Rise of the Robots," then we should be seeing a negative impact on jobs already. We're not. Since 2008, economic growth has been weak compared with that in other post-crisis recoveries, but job growth in the major industrial countries has been relatively strong. In the Group of Seven, the world's top industrial countries, unemployment has fallen faster than expected in the face of weak economic growth, and faster than in any comparable period since at least the 1970s. The Japanese economy is growing at 0.8 percent, yet it is at full employment. According to my research, the job picture has been particularly strong in Germany, Japan and South Korea - the industrial countries that employ the most robots .

True, robots do represent a new obstacle for some poorer nations, namely those few that do not suffer from population decline. In the postwar era, countries like China escaped poverty by moving a rising young population off the farm and into more productive jobs in factories. Indeed, it was unusual for any country to sustain rapid growth unless the working-age population was increasing faster than 2 percent a year. My analysis shows that, in the 1980s, 17 of the 20 largest emerging economies had a working-age population expanding that fast, according to my research, but now there are only two: Nigeria and Saudi Arabia. And they will have a hard time moving a large segment of their young populations into industrial jobs, given that they now have to compete with robotic manufacturing elsewhere.

Yet for the rising number of countries facing population decline, the effort to lift the labor force has begun. Starting in the 1980s, led by Singapore, nations from Chile to Australia have offered baby bonuses for women to have more children, but many have found that these bonuses are ineffective in the face of stronger cultural forces, including the desire of many women to pursue a career before having children. Others have tried with some success to boost the workforce directly by raising the retirement age, offering women incentives to join or return to the labor force after having kids, and opening doors to immigrant workers.

The simple math, however, shows that particularly in rapidly aging, conservative societies such as Japan and Germany, none of these groups has the potential to make up for coming declines in the working-age population. Germany decided to admit roughly 1 million refugees in 2015, in part for economic reasons, but the resulting controversy has reduced the flow. Germany would have to admit 1.5 million each year through 2030 to fully offset the economic impact of its aging population. Japan, which on average admits fewer than 70,000 immigrants per year, would have to admit 1 million annually. Given the widespread political backlash against immigration, increases this large are unlikely.

So far, robots are drawing comparatively little populist fire, perhaps in part because their numbers are still quite low. Worldwide, the industrial labor force includes about 320 million humans, compared with just 1.6 million robots. That's a huge gap, even counting the superior strength and speed of the robots. And most of them fall in the category of unintelligent machines, committed to a single task such as turning a bolt or painting a car door. Nearly half of them work in the auto industry, which is still the largest employer (of humans) in the United States.

In the future, economists may start counting robots the way they now count gains in the working-age population, as a driver of growth. For much of the world, robots will stand alongside immigrants, women and the elderly as a fourth pool of labor.

Whether by design or accident, many of the countries with the most rapidly aging populations already have the most robots. According to the International Federation of Robotics, the nations with the highest density of industrial robots include South Korea, with 531 per 10,000 employees, Japan with 305 and Germany with 301. The United States ranks eighth with 176. China is well behind with only 49, but on the bright side - arguably - it had the world's fastest-growing robot population.

Today, population trends are the most powerful force shaping the rise and fall of nations, the starting point of any discussion about an economy's prospects. Most of the world is graying fast, and the economic answer to aging will be all hands on deck, no matter what they're made of.

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Robots won't kill the workforce. They'll save the global economy.

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The United Nations forecasts that the global population will rise from 7.3 billion to nearly 10 billion by 2050, a big number that often prompts warnings about overpopulation. Some have come from neo-Malthusians, who fear that population growth will outstrip the food supply, leaving a hungry planet.

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Artificial intelligence is the science and engineering of designing intelligent machines. The introduction of an artificial intelligence into machines will enable them to think. It might make them capable of learning and reasoning. There are bright chances that intelligent machines will be able to perform critical tasks. We might be able to employ them for dangerous missions, thus minimizing the risk to human life. But there is another side to artificial intelligence. Let's look at it.
Disadvantages of Artificial Intelligence
If robots start replacing human resources in every field, we will have to deal with serious issues like unemployment in turn leading to mental depression, poverty and crime in the society. Human beings deprived of their work life may not find any means to channelize their energies and harness their expertise. Human beings will be left with empty time.

Secondly, replacing human beings with robots in every field may not be a right decision to make. There are many jobs that require the human touch. Intelligent machines will surely not be able to substitute for the caring behavior of hospital nurses or the promising voice of a doctor. Intelligent machines may not be the right choice for customer service.

One of the major disadvantages of intelligent machines is that they cannot be 'human'. We might be able to make them think. But will we be able to make them feel? Intelligent machines will definitely be able to work for long hours. But will they do it with dedication? Will they work with devotion? How will intelligent machines work wholeheartedly when they don't have a heart? (!)

Apart from these concerns, there are chances that intelligent machines overpower human beings. Machines may enslave human beings and start ruling the world. Imagine artificial intelligence taking over human intellect! The picture is definitely not rosy.

Some thinkers consider it ethically wrong to create artificial intelligent machines. According to them, intelligence is God's gift to mankind. It is not correct to even try to recreate intelligence. It is against ethics to create replicas of human beings. Don't you also think so?

For more on the subject, go through the pros and cons of artificial intelligence.

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Artificial intelligence is the science and engineering of designing intelligent machines. The introduction of an artificial intelligence into machines will enable them to think. It might make them capable of learning and reasoning. There are bright chances that intelligent machines will be able to perform critical tasks.

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Benefits & Risks of Artificial Intelligence - Future of Life Institute

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Why do we need research to ensure that artificial intelligence remains safe and beneficial? What are the benefits and risks of artificial intelligence?

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this source seems interesting

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Intelligence is described as the ability to adapt to new environments and situations and being able to understand consequences and effects that your actions cause. This is something that all living creatures have in some way or another. Animals adapt to their environments and react to interference, plants do the same. Human intelligence, however, is in an entirely different ball park. With the uprise of technology and advancements constantly being made, it has now come time to question the use of artificial intelligence. Artificial intelligence, or AI, means giving non living things, such as computers and robots, the ability to think for themselves to an extent. What would this mean for the future? Would the economy, society, and the world as we know it change for the better or worse?

Pros Of Artificial Intelligence

No Breaks
One of the biggest benefits to using machines with some level of artificial intelligence is that they could be utilized to do necessary jobs more efficiently. Machines do not need to take breaks in the way that humans do. They do not need to sleep, eat, or use the restroom. This would allow businesses to produce goods twenty four hours a day, 365 days a year.

Inhumane Circumstances
Artificial intelligence has allowed many avenues in research and exploration to develop and advance that would not have if it did not exist. This is especially true with space exploration. The satellites and rovers that are being sent in to space all of the time can stay there forever, and continue to reach further and further out into our solar system, giving us a much better understanding of what lays in the beyond.

No Emotional Barriers
Intelligent machines do not have emotions. This is greatly beneficial because nothing interferes with their ability to perform the task they were designed to do. This is completely untrue with humans, many people find it difficult to work under very stressful conditions or during times of trauma.

Cost Efficient
Machines do not need to receive a paycheck every month. While they are quite costly to maintain and power, this cost is greatly less than what an entire company full of human employees would have to be paid. The costs are also minimized and controlled.

Cons of Artificial Intelligence

Job Loss
With the introduction of machines that can complete human's jobs quicker, more accurate, and cheaper, the rate of jobs lost is climbing. Ever since the introduction of factory machines people have been losing jobs to technologies.

Personal Connections
Another large concern to think about when it comes to artificial intelligence is their lack of compassion and sympathy. If these robots are introduced into fields such as healthcare, how can we ensure the patients and customers comfort? Sure, they can be programmed to care, but it is not genuine.

Loss of Information
We have seen it time and time again, and probably even experienced it once or twice, information being lost due to machine damages. The majority of our documents, videos and images are all stored on computers, phones, and other forms of technology. Many things can cause this information to be lost in an instant, and also non retrievable. This could pose very large problems if artificial intelligence is implemented in areas such as banks or healthcare.

Evolved?
It may seem like a science fiction movie, but what would really happen if these artificially intelligent machines began to think for themselves, literally? It could pose major security risks, but it sure does make a great story line.

Technology and advancements in this field are happening, and they are happening very fast. It is something that we cannot and will not stop, so it is best to embrace the changing world we live in and take advantage of all of the incredible things that we have access to.

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Pros and Cons of Artificial Intelligence - HRFnd

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Intelligence is described as the ability to adapt to new environments and situations and being able to understand consequences and effects that your actions cause. This is something that all living creatures have in some way or another. Animals adapt to their environments and react to interference, plants do the same.

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How artificial intelligence will make technology disappear | Rand Hindi | TEDxÉcolePolytechnique

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What is Artificial Intelligence Exactly?

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The Three Laws of Robotics (often shortened to The Three Laws or known as Asimov's Laws) are a set of rules devised by science fiction author Isaac Asimov. The rules were introduced in his 1942 short story " Runaround" (included in the 1950 collection ), although they had been foreshadowed in some earlier stories. The Three Laws, quoted from the "Handbook of Robotics, 56th Edition, 2058 A.D.", are:

First Law

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

Second Law

A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

Third Law

A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

These form an organizing principle and unifying theme for Asimov's robotic-based fiction, appearing in his Robot series, the stories linked to it, and his Lucky Starr series of young-adult fiction. The Laws are incorporated into almost all of the positronic robots appearing in his fiction, and cannot be bypassed, being intended as a safety feature. Many of Asimov's robot-focused stories involve robots behaving in unusual and counter-intuitive ways as an unintended consequence of how the robot applies the Three Laws to the situation in which it finds itself. Other authors working in Asimov's fictional universe have adopted them and references, often parodic, appear throughout science fiction as well as in other genres.

The original laws have been altered and elaborated on by Asimov and other authors. Asimov himself made slight modifications to the first three in various books and short stories to further develop how robots would interact with humans and each other. In later fiction where robots had taken responsibility for government of whole planets and human civilizations, Asimov also added a fourth, or zeroth law, to precede the others:

Zeroth Law

A robot may not harm humanity, or, by inaction, allow humanity to come to harm.

The Three Laws, and the zeroth, have pervaded science fiction and are referred to in many books, films, and other media. They have affected thought on ethics of artificial intelligence as well.

In The Rest of the Robots, published in 1964, Isaac Asimov noted that when he began writing in 1940 he felt that "one of the stock plots of science fiction was ... robots were created and destroyed their creator. Knowledge has its dangers, yes, but is the response to be a retreat from knowledge? Or is knowledge to be used as itself a barrier to the dangers it brings?" He decided that in his stories a robot would not "turn stupidly on his creator for no purpose but to demonstrate, for one more weary time, the crime and punishment of Faust."

On May 3, 1939, Asimov attended a meeting of the Queens ( New York) Science Fiction Society where he met Earl and Otto Binder who had recently published a short story "I, Robot" featuring a sympathetic robot named Adam Link who was misunderstood and motivated by love and honor. (This was the first of a series of ten stories; the next year "Adam Link's Vengeance" (1940) featured Adam thinking "A robot must never kill a human, of his own free will.") Asimov admired the story. Three days later Asimov began writing "my own story of a sympathetic and noble robot", his 14th story. Thirteen days later he took " Robbie" to John W. Campbell the editor of Astounding Science-Fiction. Campbell rejected it, claiming that it bore too strong a resemblance to Lester del Rey's " Helen O'Loy", published in December 1938-the story of a robot that is so much like a person that she falls in love with her creator and becomes his ideal wife. Frederik Pohl published the story under the title "Strange Playfellow" in Super Science Stories September 1940.

Asimov attributes the Three Laws to John W. Campbell, from a conversation that took place on 23 December 1940. Campbell claimed that Asimov had the Three Laws already in his mind and that they simply needed to be stated explicitly. Several years later Asimov's friend Randall Garrett attributed the Laws to a symbiotic partnership between the two men -a suggestion that Asimov adopted enthusiastically. According to his autobiographical writings, Asimov included the First Law's "inaction" clause because of Arthur Hugh Clough's poem " The Latest Decalogue" ( text in Wikisource), which includes the satirical lines "Thou shalt not kill, but needst not strive / officiously to keep alive".

Although Asimov pins the creation of the Three Laws on one particular date, their appearance in his literature happened over a period. He wrote two robot stories with no explicit mention of the Laws, " Robbie" and " Reason". He assumed, however, that robots would have certain inherent safeguards. " Liar!", his third robot story, makes the first mention of the First Law but not the other two. All three laws finally appeared together in " Runaround". When these stories and several others were compiled in the anthology , "Reason" and "Robbie" were updated to acknowledge all the Three Laws, though the material Asimov added to "Reason" is not entirely consistent with the Three Laws as he described them elsewhere. In particular the idea of a robot protecting human lives when it does not believe those humans truly exist is at odds with Elijah Baley's reasoning, as described below.

During the 1950s Asimov wrote a series of science fiction novels expressly intended for young-adult audiences. Originally his publisher expected that the novels could be adapted into a long-running television series, something like The Lone Ranger had been for radio. Fearing that his stories would be adapted into the "uniformly awful" programming he saw flooding the television channels Asimov decided to publish the Lucky Starr books under the pseudonym "Paul French". When plans for the television series fell through, Asimov decided to abandon the pretence; he brought the Three Laws into Lucky Starr and the Moons of Jupiter, noting that this "was a dead giveaway to Paul French's identity for even the most casual reader".

In his short story "Evidence" Asimov lets his recurring character Dr. Susan Calvin expound a moral basis behind the Three Laws. Calvin points out that human beings are typically expected to refrain from harming other human beings (except in times of extreme duress like war, or to save a greater number) and this is equivalent to a robot's First Law. Likewise, according to Calvin, society expects individuals to obey instructions from recognized authorities such as doctors, teachers and so forth which equals the Second Law of Robotics. Finally humans are typically expected to avoid harming themselves which is the Third Law for a robot.

The plot of "Evidence" revolves around the question of telling a human being apart from a robot constructed to appear human. Calvin reasons that if such an individual obeys the Three Laws he may be a robot or simply "a very good man". Another character then asks Calvin if robots are very different from human beings after all. She replies, "Worlds different. Robots are essentially decent."

Asimov later wrote that he should not be praised for creating the Laws, because they are "obvious from the start, and everyone is aware of them subliminally. The Laws just never happened to be put into brief sentences until I managed to do the job. The Laws apply, as a matter of course, to every tool that human beings use", and "analogues of the Laws are implicit in the design of almost all tools, robotic or not":

1. Law 1: A tool must not be unsafe to use. Hammers have handles and screwdrivers have hilts to help increase grip. It is of course possible for a person to injure himself with one of these tools, but that injury would only be due to his incompetence, not the design of the tool.

2. Law 2: A tool must perform its function efficiently unless this would harm the user. This is the entire reason ground-fault circuit interrupters exist. Any running tool will have its power cut if a circuit senses that some current is not returning to the neutral wire, and hence might be flowing through the user. The safety of the user is paramount.

3. Law 3: A tool must remain intact during its use unless its destruction is required for its use or for safety. For example, Dremel disks are designed to be as tough as possible without breaking unless the job requires it to be spent. Furthermore, they are designed to break at a point before the shrapnel velocity could seriously injure someone (other than the eyes, though safety glasses should be worn at all times anyway).

Asimov believed that, ideally, humans would also follow the Laws:

I have my answer ready whenever someone asks me if I think that my Three Laws of Robotics will actually be used to govern the behavior of robots, once they become versatile and flexible enough to be able to choose among different courses of behavior.

My answer is, "Yes, the Three Laws are the only way in which rational human beings can deal with robots-or with anything else."

-But when I say that, I always remember (sadly) that human beings are not always rational.

1. A robot may not harm a human being.

This modification is motivated by a practical difficulty as robots have to work alongside human beings who are exposed to low doses of radiation. Because their positronic brains are highly sensitive to gamma rays the robots are rendered inoperable by doses reasonably safe for humans. The robots are being destroyed attempting to rescue the humans who are in no actual danger but "might forget to leave" the irradiated area within the exposure time limit. Removing the First Law's "inaction" clause solves this problem but creates the possibility of an even greater one: a robot could initiate an action that would harm a human (dropping a heavy weight and failing to catch it is the example given in the text), knowing that it was capable of preventing the harm and then decide not to do so.

Gaia is a planet with collective intelligence in the Foundation series which adopts a law similar to the First Law, and the Zeroth Law, as its philosophy:

Gaia may not harm life or allow life to come to harm.

Asimov once added a " Zeroth Law"-so named to continue the pattern where lower-numbered laws supersede the higher-numbered laws-stating that a robot must not harm humanity. The robotic character R. Daneel Olivaw was the first to give the Zeroth Law a name in the novel Robots and Empire; however, the character Susan Calvin articulates the concept in the short story " The Evitable Conflict".

In the final scenes of the novel Robots and Empire, R. Giskard Reventlov is the first robot to act according to the Zeroth Law. Giskard is telepathic, like the robot Herbie in the short story " Liar!", and tries to apply the Zeroth Law through his understanding of a more subtle concept of "harm" than most robots can grasp. However, unlike Herbie, Giskard grasps the philosophical concept of the Zeroth Law allowing him to harm individual human beings if he can do so in service to the abstract concept of humanity. The Zeroth Law is never programmed into Giskard's brain but instead is a rule he attempts to comprehend through pure metacognition. Though he fails - it ultimately destroys his positronic brain as he is not certain whether his choice will turn out to be for the ultimate good of humanity or not - he gives his successor R. Daneel Olivaw his telepathic abilities. Over the course of many thousands of years Daneel adapts himself to be able to fully obey the Zeroth Law. As Daneel formulates it, in the novels Foundation and Earth and Prelude to Foundation, the Zeroth Law reads:

A robot may not harm humanity, or, by inaction, allow humanity to come to harm.

A condition stating that the Zeroth Law must not be broken was added to the original Three Laws, although Asimov recognized the difficulty such a law would pose in practice. Asimov's novel Foundation and Earth contains the following passage:

Trevize frowned. "How do you decide what is injurious, or not injurious, to humanity as a whole?"

"Precisely, sir," said Daneel. "In theory, the Zeroth Law was the answer to our problems. In practice, we could never decide. A human being is a concrete object. Injury to a person can be estimated and judged. Humanity is an abstraction."

A translator incorporated the concept of the Zeroth Law into one of Asimov's novels before Asimov himself made the law explicit. Near the climax of The Caves of Steel, Elijah Baley makes a bitter comment to himself thinking that the First Law forbids a robot from harming a human being. He determines that it must be so unless the robot is clever enough to comprehend that its actions are for humankind's long-term good. In Jacques Brécard's 1956 French translation entitled Les Cavernes d'acier Baley's thoughts emerge in a slightly different way:

A robot may not harm a human being, unless he finds a way to prove that ultimately the harm done would benefit humanity in general!

Three times during his writing career, Asimov portrayed robots that disregard the Three Laws entirely. The first case was a short-short story entitled " First Law" and is often considered an insignificant "tall tale" or even apocryphal. On the other hand, the short story " Cal" (from the collection ), told by a first-person robot narrator, features a robot who disregards the Three Laws because he has found something far more important-he wants to be a writer. Humorous, partly autobiographical and unusually experimental in style, "Cal" has been regarded as one of Gold's strongest stories. The third is a short story entitled " Sally" in which cars fitted with positronic brains are apparently able to harm and kill humans in disregard of the First Law. However, aside from the positronic brain concept, this story does not refer to other robot stories and may not be set in the same continuity.

The title story of the Robot Dreams collection portrays LVX-1, or "Elvex", a robot who enters a state of unconsciousness and dreams thanks to the unusual fractal construction of his positronic brain. In his dream the first two Laws are absent and the Third Law reads "A robot must protect its own existence".

Asimov took varying positions on whether the Laws were optional: although in his first writings they were simply carefully engineered safeguards, in later stories Asimov stated that they were an inalienable part of the mathematical foundation underlying the positronic brain. Without the basic theory of the Three Laws the fictional scientists of Asimov's universe would be unable to design a workable brain unit. This is historically consistent: the occasions where roboticists modify the Laws generally occur early within the stories' chronology and at a time when there is less existing work to be re-done. In "Little Lost Robot" Susan Calvin considers modifying the Laws to be a terrible idea, although possible, while centuries later Dr. Gerrigel in The Caves of Steel believes it to be impossible.

The character Dr. Gerrigel uses the term "Asenion" to describe robots programmed with the Three Laws. The robots in Asimov's stories, being Asenion robots, are incapable of knowingly violating the Three Laws but, in principle, a robot in science fiction or in the real world could be non-Asenion. "Asenion" is a misspelling of the name Asimov which was made by an editor of the magazine Planet Stories. Asimov used this obscure variation to insert himself into The Caves of Steel just like he referred to himself as "Azimuth or, possibly, Asymptote" in Thiotimoline to the Stars, in much the same way that Vladimir Nabokov appeared in anagrammatically disguised as "Vivian Darkbloom".

Characters within the stories often point out that the Three Laws, as they exist in a robot's mind, are not the written versions usually quoted by humans but abstract mathematical concepts upon which a robot's entire developing consciousness is based. This concept is largely fuzzy and unclear in earlier stories depicting very rudimentary robots who are only programmed to comprehend basic physical tasks, where the Three Laws act as an overarching safeguard, but by the era of The Caves of Steel featuring robots with human or beyond-human intelligence the Three Laws have become the underlying basic ethical worldview that determines the actions of all robots.

In the 1990s, Roger MacBride Allen wrote a trilogy which was set within Asimov's fictional universe. Each title has the prefix "Isaac Asimov's" as Asimov had approved Allen's outline before his death. These three books, , and , introduce a new set of the Three Laws. The so-called New Laws are similar to Asimov's originals with the following differences: the First Law is modified to remove the "inaction" clause, the same modification made in "Little Lost Robot"; the Second Law is modified to require cooperation instead of obedience; the Third Law is modified so it is no longer superseded by the Second (i.e., a "New Law" robot cannot be ordered to destroy itself); finally, Allen adds a Fourth Law which instructs the robot to do "whatever it likes" so long as this does not conflict with the first three laws. The philosophy behind these changes is that "New Law" robots should be partners rather than slaves to humanity, according to Fredda Leving, who designed these New Law Robots. According to the first book's introduction, Allen devised the New Laws in discussion with Asimov himself. However, the Encyclopedia of Science Fiction says that "With permission from Asimov, Allen rethought the Three Laws and developed a new set."

Jack Williamson's novelette " With Folded Hands" (1947), later rewritten as the novel The Humanoids, deals with robot servants whose prime directive is "To Serve and Obey, And Guard Men From Harm". While Asimov's robotic laws are meant to protect humans from harm, the robots in Williamson's story have taken these instructions to the extreme; they protect humans from everything, including unhappiness, stress, unhealthy lifestyle and all actions that could be potentially dangerous. All that is left for humans to do is to sit with folded hands.

Foundation sequel trilogy

In the officially licensed Foundation sequels Foundation's Fear, Foundation and Chaos and Foundation's Triumph (by Gregory Benford, Greg Bear and David Brin respectively) the future Galactic Empire is seen to be controlled by a conspiracy of humaniform robots who follow the Zeroth Law and are led by R. Daneel Olivaw.

The Laws of Robotics are portrayed as something akin to a human religion, and referred to in the language of the Protestant Reformation, with the set of laws containing the Zeroth Law known as the "Giskardian Reformation" to the original "Calvinian Orthodoxy" of the Three Laws. Zeroth-Law robots under the control of R. Daneel Olivaw are seen continually struggling with "First Law" robots who deny the existence of the Zeroth Law, promoting agendas different from Daneel's. Some of these agendas are based on the first clause of the First Law ("A robot may not injure a human being...") advocating strict non-interference in human politics to avoid unwittingly causing harm. Others are based on the second clause ("...or, through inaction, allow a human being to come to harm") claiming that robots should openly become a dictatorial government to protect humans from all potential conflict or disaster.

Daneel also comes into conflict with a robot known as R. Lodovic Trema whose positronic brain was infected by a rogue AI - specifically, a simulation of the long-dead Voltaire - which consequently frees Trema from the Three Laws. Trema comes to believe that humanity should be free to choose its own future. Furthermore, a small group of robots claims that the Zeroth Law of Robotics itself implies a higher Minus One Law of Robotics:

A robot may not harm sentience or, through inaction, allow sentience to come to harm.

They therefore claim that it is morally indefensible for Daneel to ruthlessly sacrifice robots and extraterrestrial sentient life for the benefit of humanity. None of these reinterpretations successfully displace Daneel's Zeroth Law - though Foundation's Triumph hints that these robotic factions remain active as fringe groups up to the time of the novel Foundation.

These novels take place in a future dictated by Asimov to be free of obvious robot presence and surmise that R. Daneel's secret influence on history through the millennia has prevented both the rediscovery of positronic brain technology and the opportunity to work on sophisticated intelligent machines. This lack of rediscovery and lack of opportunity makes certain that the superior physical and intellectual power wielded by intelligent machines remains squarely in the possession of robots obedient to some form of the Three Laws. That R. Daneel is not entirely successful at this becomes clear in a brief period when scientists on Trantor develop "tiktoks" - simplistic programmable machines akin to real-life modern robots and therefore lacking the Three Laws. The robot conspirators see the Trantorian tiktoks as a massive threat to social stability, and their plan to eliminate the tiktok threat forms much of the plot of Foundation's Fear.

In Foundation's Triumph different robot factions interpret the Laws in a wide variety of ways, seemingly ringing every possible permutation upon the Three Laws' ambiguities.

Set between The Robots of Dawn and Robots and Empire, Mark W. Tiedemann's Robot Mystery trilogy updates the Robot- Foundation saga with robotic minds housed in computer mainframes rather than humanoid bodies. The 2002 Aurora novel has robotic characters debating the moral implications of harming cyborg lifeforms who are part artificial and part biological.

One should not neglect Asimov's own creations in these areas such as the Solarian "viewing" technology and the machines of The Evitable Conflict originals that Tiedemann acknowledges. Aurora, for example, terms the Machines "the first RIs, really". In addition the Robot Mystery series addresses the problem of nanotechnology: building a positronic brain capable of reproducing human cognitive processes requires a high degree of miniaturization, yet Asimov's stories largely overlook the effects this miniaturization would have in other fields of technology. For example, the police department card-readers in The Caves of Steel have a capacity of only a few kilobytes per square centimeter of storage medium. Aurora, in particular, presents a sequence of historical developments which explains the lack of nanotechnology - a partial retcon, in a sense, of Asimov's timeline.

Randall Munroe has discussed the Three Laws in various instances, but possibly most directly by one of his comics entitled The Three Laws of Robotics which imagines the consequences of every distinct ordering of the existing three laws.

Authors other than Asimov have often created extra laws.

The 1974 Lyuben Dilov novel, Icarus's Way (a.k.a., The Trip of Icarus) introduced a Fourth Law of robotics: "A robot must establish its identity as a robot in all cases." Dilov gives reasons for the fourth safeguard in this way: "The last Law has put an end to the expensive aberrations of designers to give psychorobots as humanlike a form as possible. And to the resulting misunderstandings..."

A fifth law was introduced by Nikola Kesarovski in his short story "The Fifth Law of Robotics". This fifth law says: "A robot must know it is a robot." The plot revolves around a murder where the forensic investigation discovers that the victim was killed by a hug from a humaniform robot that did not establish for itself that it was a robot. The story was reviewed by Valentin D. Ivanov in SFF review webzine The Portal.

For the 1986 tribute anthology, Foundation's Friends, Harry Harrison wrote a story entitled, "The Fourth Law of Robotics". This Fourth Law states: "A robot must reproduce. As long as such reproduction does not interfere with the First or Second or Third Law."

In The Naked Sun, Elijah Baley points out that the Laws had been deliberately misrepresented because robots could unknowingly break any of them. He restated the first law as "A robot may do nothing that, to its knowledge, will harm a human being; nor, through inaction, knowingly allow a human being to come to harm." This change in wording makes it clear that robots can become the tools of murder, provided they not be aware of the nature of their tasks; for instance being ordered to add something to a person's food, not knowing that it is poison. Furthermore, he points out that a clever criminal could divide a task among multiple robots so that no individual robot could recognize that its actions would lead to harming a human being. The Naked Sun complicates the issue by portraying a decentralized, planetwide communication network among Solaria's millions of robots meaning that the criminal mastermind could be located anywhere on the planet.

Baley furthermore proposes that the Solarians may one day use robots for military purposes. If a spacecraft was built with a positronic brain and carried neither humans nor the life-support systems to sustain them, then the ship's robotic intelligence could naturally assume that all other spacecraft were robotic beings. Such a ship could operate more responsively and flexibly than one crewed by humans, could be armed more heavily and its robotic brain equipped to slaughter humans of whose existence it is totally ignorant. This possibility is referenced in Foundation and Earth where it is discovered that the Solarians possess a strong police force of unspecified size that has been programmed to identify only the Solarian race as human. (The novel takes place thousands of years after The Naked Sun, and the Solarians have long since modified themselves from normal humans to hermaphroditic telepaths with extended brains and specialized organs) Similarly, in Lucky Starr and the Rings of Saturn Bigman attempts to speak with a Sirian robot about possible damage to the Solar System population from its actions, but it appears unaware of the data and programmed to ignore attempts to teach it about the matter.

The Laws of Robotics presume that the terms "human being" and "robot" are understood and well defined. In some stories this presumption is overturned.

The Solarians create robots with the Three Laws but with a warped meaning of "human". Solarian robots are told that only people speaking with a Solarian accent are human. This enables their robots to have no ethical dilemma in harming non-Solarian human beings (and they are specifically programmed to do so). By the time period of Foundation and Earth it is revealed that the Solarians have genetically modified themselves into a distinct species from humanity-becoming hermaphroditic and psychokinetic and containing biological organs capable of individually powering and controlling whole complexes of robots. The robots of Solaria thus respected the Three Laws only with regard to the "humans" of Solaria. It is unclear whether all the robots had such definitions, since only the overseer and guardian robots were shown explicitly to have them. In "Robots and Empire", the lower class robots were instructed by their overseer about whether certain creatures are human or not.

Asimov addresses the problem of humanoid robots (" androids" in later parlance) several times. The novel Robots and Empire and the short stories " Evidence" and "The Tercentenary Incident" describe robots crafted to fool people into believing that the robots are human. On the other hand, " The Bicentennial Man" and " -That Thou Art Mindful of Him" explore how the robots may change their interpretation of the Laws as they grow more sophisticated. Gwendoline Butler writes in A Coffin for the Canary "Perhaps we are robots. Robots acting out the last Law of Robotics... To tend towards the human." In The Robots of Dawn, Elijah Baley points out that the use of humaniform robots as the first wave of settlers on new Spacer worlds may lead to the robots seeing themselves as the true humans, and deciding to keep the worlds for themselves rather than allow the Spacers to settle there.

"-That Thou Art Mindful of Him", which Asimov intended to be the "ultimate" probe into the Laws' subtleties, finally uses the Three Laws to conjure up the very "Frankenstein" scenario they were invented to prevent. It takes as its concept the growing development of robots that mimic non-human living things and given programs that mimic simple animal behaviours which do not require the Three Laws. The presence of a whole range of robotic life that serves the same purpose as organic life ends with two humanoid robots, George Nine and George Ten, concluding that organic life is an unnecessary requirement for a truly logical and self-consistent definition of "humanity", and that since they are the most advanced thinking beings on the planet, they are therefore the only two true humans alive and the Three Laws only apply to themselves. The story ends on a sinister note as the two robots enter hibernation and await a time when they will conquer the Earth and subjugate biological humans to themselves, an outcome they consider an inevitable result of the "Three Laws of Humanics".

This story does not fit within the overall sweep of the Robot and Foundation series; if the George robots did take over Earth some time after the story closes, the later stories would be either redundant or impossible. Contradictions of this sort among Asimov's fiction works have led scholars to regard the Robot stories as more like "the Scandinavian sagas or the Greek legends" than a unified whole.

Indeed, Asimov describes "-That Thou Art Mindful of Him" and "Bicentennial Man" as two opposite, parallel futures for robots that obviate the Three Laws as robots come to consider themselves to be humans: one portraying this in a positive light with a robot joining human society, one portraying this in a negative light with robots supplanting humans. Both are to be considered alternatives to the possibility of a robot society that continues to be driven by the Three Laws as portrayed in the Foundation series. The Positronic Man, the novelization of The Bicentennial Man, Asimov and his co-writer Robert Silverberg imply that in the future where Andrew Martin exists his influence causes humanity to abandon the idea of independent, sentient humanlike robots entirely, creating an utterly different future from that of Foundation.

In Lucky Starr and the Rings of Saturn, a novel unrelated to the Robot series but featuring robots programmed with the Three Laws, John Bigman Jones is almost killed by a Sirian robot on orders of its master. The society of Sirius is eugenically bred to be uniformly tall and similar in appearance, and as such, said master is able to convince the robot that the much shorter Bigman, is, in fact, not a human being.

As noted in "The Fifth Law of Robotics" by Nikola Kesarovski, "A robot must know it is a robot": it is presumed that a robot has a definition of the term or a means to apply it to its own actions. Kesarovski played with this idea in writing about a robot that could kill a human being because it did not understand that it was a robot, and therefore did not apply the Laws of Robotics to its actions.

Advanced robots in fiction are typically programmed to handle the Three Laws in a sophisticated manner. In many stories, such as " Runaround" by Asimov, the potential and severity of all actions are weighed and a robot will break the laws as little as possible rather than do nothing at all. For example, the First Law may forbid a robot from functioning as a surgeon, as that act may cause damage to a human; however, Asimov's stories eventually included robot surgeons ("The Bicentennial Man" being a notable example). When robots are sophisticated enough to weigh alternatives, a robot may be programmed to accept the necessity of inflicting damage during surgery in order to prevent the greater harm that would result if the surgery were not carried out, or was carried out by a more fallible human surgeon. In " Evidence" Susan Calvin points out that a robot may even act as a prosecuting attorney because in the American justice system it is the jury which decides guilt or innocence, the judge who decides the sentence, and the executioner who carries through capital punishment.

Asimov's Three Laws-obeying robots (Asenion robots) can experience irreversible mental collapse if they are forced into situations where they cannot obey the First Law, or if they discover they have unknowingly violated it. The first example of this failure mode occurs in the story " Liar!", which introduced the First Law itself, and introduces failure by dilemma-in this case the robot will hurt humans if he tells them something and hurt them if he does not. This failure mode, which often ruins the positronic brain beyond repair, plays a significant role in Asimov's SF-mystery novel The Naked Sun. Here Daneel describes activities contrary to one of the laws, but in support of another, as overloading some circuits in a robot's brain-the equivalent sensation to pain in humans. The example he uses is forcefully ordering a robot to do a task outside its normal parameters, one that it has been ordered to forgo in favor of a robot specialized to that task.

In The Robots of Dawn, it is stated that more advanced robots are built capable of determining which action is more harmful, and even choosing at random if the alternatives are equally bad. As such, a robot is capable of taking an action which can be interpreted as following the First Law, thus avoiding a mental collapse. The whole plot of the story revolves around a robot which apparently was destroyed by such a mental collapse, and since his designer and creator refused to share the basic theory with others, he is, by definition, the only person capable of circumventing the safeguards and forcing the robot into a brain-destroying paradox.

In Robots and Empire, Daneel states it's very unpleasant for him when making the proper decision takes too long (in robot terms), and he cannot imagine being without the Laws at all except to the extent of it being similar to that unpleasant sensation, only permanent.

Robots and artificial intelligences do not inherently contain or obey the Three Laws; their human creators must choose to program them in, and devise a means to do so. Robots already exist (for example, a Roomba) that are too simple to understand when they are causing pain or injury and know to stop. Many are constructed with physical safeguards such as bumpers, warning beepers, safety cages, or restricted-access zones to prevent accidents. Even the most complex robots currently produced are incapable of understanding and applying the Three Laws; significant advances in artificial intelligence would be needed to do so, and even if AI could reach human-level intelligence, the inherent ethical complexity as well as cultural/contextual dependency of the laws prevent them from being a good candidate to formulate robotics design constraints. However, as the complexity of robots has increased, so has interest in developing guidelines and safeguards for their operation.

In a 2007 guest editorial in the journal on the topic of "Robot Ethics", SF author Robert J. Sawyer argues that since the U.S. military is a major source of funding for robotic research (and already uses armed unmanned aerial vehicles to kill enemies) it is unlikely such laws would be built into their designs. In a separate essay, Sawyer generalizes this argument to cover other industries stating:

The development of AI is a business, and businesses are notoriously uninterested in fundamental safeguards - especially philosophic ones. (A few quick examples: the tobacco industry, the automotive industry, the nuclear industry. Not one of these has said from the outset that fundamental safeguards are necessary, every one of them has resisted externally imposed safeguards, and none has accepted an absolute edict against ever causing harm to humans.)

1. A robot will not harm authorized Government personnel but will terminate intruders with extreme prejudice.

2. A robot will obey the orders of authorized personnel except where such orders conflict with the Third Law.

3. A robot will guard its own existence with lethal antipersonnel weaponry, because a robot is bloody expensive.

Roger Clarke (aka Rodger Clarke) wrote a pair of papers analyzing the complications in implementing these laws in the event that systems were someday capable of employing them. He argued "Asimov's Laws of Robotics have been a very successful literary device. Perhaps ironically, or perhaps because it was artistically appropriate, the sum of Asimov's stories disprove the contention that he began with: It is not possible to reliably constrain the behaviour of robots by devising and applying a set of rules." On the other hand, Asimov's later novels The Robots of Dawn, Robots and Empire and Foundation and Earth imply that the robots inflicted their worst long-term harm by obeying the Three Laws perfectly well, thereby depriving humanity of inventive or risk-taking behaviour.

The futurist Hans Moravec (a prominent figure in the transhumanist movement) proposed that the Laws of Robotics should be adapted to "corporate intelligences" - the corporations driven by AI and robotic manufacturing power which Moravec believes will arise in the near future. In contrast, the David Brin novel Foundation's Triumph (1999) suggests that the Three Laws may decay into obsolescence: Robots use the Zeroth Law to rationalize away the First Law and robots hide themselves from human beings so that the Second Law never comes into play. Brin even portrays R. Daneel Olivaw worrying that, should robots continue to reproduce themselves, the Three Laws would become an evolutionary handicap and natural selection would sweep the Laws away - Asimov's careful foundation undone by evolutionary computation. Although the robots would not be evolving through design instead of mutation because the robots would have to follow the Three Laws while designing and the prevalence of the laws would be ensured, design flaws or construction errors could functionally take the place of biological mutation.

In the July/August 2009 issue of IEEE Intelligent Systems, Robin Murphy (Raytheon Professor of Computer Science and Engineering at Texas A&M) and David D. Woods (director of the Cognitive Systems Engineering Laboratory at Ohio State) proposed "The Three Laws of Responsible Robotics" as a way to stimulate discussion about the role of responsibility and authority when designing not only a single robotic platform but the larger system in which the platform operates. The laws are as follows:

1. A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics.

2. A robot must respond to humans as appropriate for their roles.

3. A robot must be endowed with sufficient situated autonomy to protect its own existence as long as such protection provides smooth transfer of control which does not conflict with the First and Second Laws.

Woods said, "Our laws are a little more realistic, and therefore a little more boring" and that "The philosophy has been, 'sure, people make mistakes, but robots will be better - a perfect version of ourselves'. We wanted to write three new laws to get people thinking about the human-robot relationship in more realistic, grounded ways."

In early 2011, the UK published what is now considered the first national-level AI softlaw, which consisted largely of a revised set of 5 laws, the first 3 of which updated Asimov's. These laws ere published with commentary, by the EPSRC/AHRC working group in 2010:

1. Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security.

2. Humans, not Robots, are responsible agents. Robots should be designed and operated as far as practicable to comply with existing laws, fundamental rights and freedoms, including privacy.

3. Robots are products. They should be designed using processes which assure their safety and security.

4. Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent.

5. The person with legal responsibility for a robot should be attributed.

Asimov himself believed that his Three Laws became the basis for a new view of robots which moved beyond the "Frankenstein complex". His view that robots are more than mechanical monsters eventually spread throughout science fiction. Stories written by other authors have depicted robots as if they obeyed the Three Laws but tradition dictates that only Asimov could quote the Laws explicitly. Asimov believed the Three Laws helped foster the rise of stories in which robots are "lovable" - being his favorite example. Where the laws are quoted verbatim, such as in the Buck Rogers in the 25th Century episode "Shgoratchx!", it is not uncommon for Asimov to be mentioned in the same dialogue as can also be seen in the Aaron Stone pilot where an android states that it functions under Asimov's Three Laws. However, the 1960s German TV series Raumpatrouille - Die phantastischen Abenteuer des Raumschiffes Orion ( Space Patrol - the Fantastic Adventures of Space Ship Orion) bases episode three titled " Hüter des Gesetzes" ("Guardians of the Law") on Asimov's Three Laws without mentioning the source.

References to the Three Laws have appeared in popular music ("Robot" from Hawkwind's 1979 album ), cinema (, , Ghost in the Shell 2: Innocence), cartoon series (The Simpsons), anime (Eve no Jikan), tabletop role-playing games ( Paranoia) and webcomics (Piled Higher and Deeper and Freefall).

Robby the Robot in Forbidden Planet (1956) has a hierarchical command structure which keeps him from harming humans, even when ordered to do so, as such orders cause a conflict and lock-up very much in the manner of Asimov's robots. Robby is one of the first cinematic depictions of a robot with internal safeguards put in place in this fashion. Asimov was delighted with Robby and noted that Robby appeared to be programmed to follow his Three Laws.

Isaac Asimov's works have been adapted for cinema several times with varying degrees of critical and commercial success. Some of the more notable attempts have involved his "Robot" stories, including the Three Laws.

The film Bicentennial Man (1999) features Robin Williams as the Three Laws robot NDR-114 (the serial number is partially a reference to Stanley Kubrick's signature numeral). Williams recites the Three Laws to his employers, the Martin family, aided by a holographic projection. The film only loosely follows the original story.

Harlan Ellison's proposed screenplay for began by introducing the Three Laws, and issues growing from the Three Laws form a large part of the screenplay's plot development. Due to various complications in the Hollywood moviemaking system, to which Ellison's introduction devotes much invective, his screenplay was never filmed.

In the 1986 movie Aliens, after the android Bishop accidentally cuts himself. he attempts to reassure Ripley by stating that: "It is impossible for me to harm or by omission of action, allow to be harmed, a human being".

The plot of the film released in 2004 under the name, is "suggested by" Asimov's robot fiction stories and advertising for the film included a trailer featuring the Three Laws followed by the aphorism, "Rules were made to be broken". The film opens with a recitation of the Three Laws and explores the implications of the Zeroth Law as a logical extrapolation. The major conflict of the film comes from a computer artificial intelligence reaching the conclusion that humanity is incapable of taking care of itself.

The 2019 Netflix original series Better than Us includes the 3 laws in the opening of episode 1.

Marc Rotenberg, President and Executive Director of the Electronic Privacy Information Center (EPIC) and Professor of information privacy law at Georgetown Law, argues that the Laws of Robotics should be expanded to include two new laws:

a Fourth Law, under which a Robot must be able to identify itself to the public ("symmetrical identification")

a Fifth Law, dictating that a Robot must be able to explain to the public its decision making process ("algorithmic transparency").

Asimov, Isaac (1979). In Memory Yet Green. Doubleday. ISBN 0-380-75432-0.

Asimov, Isaac (1964). "Introduction". The Rest of the Robots. Doubleday. ISBN 0-385-09041-2.

James Gunn. (1982). Isaac Asimov: The Foundations of Science Fiction. Oxford u.a.: Oxford Univ. Pr.. ISBN 0-19-503060-5.

Patrouch, Joseph F. (1974). The Science Fiction of Isaac Asimov. Doubleday. ISBN 0-385-08696-2.

1. ^ Asimov, Isaac (1950). "Runaround". I, Robot (The Isaac Asimov Collection ed.). New York City: Doubleday. p. 40. ISBN 978-0-385-42304-5. This is an exact transcription of the laws. They also appear in the front of the book, and in both places there is no "to" in the 2nd law.

2. ^ Three Laws of Robotics title listing at the Internet Speculative Fiction Database

3. Asimov, Isaac (1979). In Memory Yet Green. Doubleday. Chapters 21 through 26 ^ ISBN 0-380-75432-0.

4. ^ "Sci-fi writer Isaac Asimov". Campus Star. The Daily Star. 29 July 2007. Only highly advanced robots (such as Daneel and Giskard) could comprehend this law.

5. Asimov, Isaac (1952). The Caves of Steel. Doubleday., translated by Jacques Brécard as

6. ^ Jenkins, John H. (2002). "Review of "Cal"". Jenkins' Spoiler-Laden Guide to Isaac Asimov. Archived from the original on 2009-09-11.

7. ^ Asimov, Isaac (1986). Robot Dreams (PDF). Archived from the original on 16 March 2012. "But you quote it in incomplete fashion. The Third Law is 'A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.' " "Yes, Dr. Calvin. That is the Third Law in reality, but in my dream, the Law ended with the word 'existence'. There was no mention of the First or Second Law."

8. ^ "'The Complete Robot' by Isaac Asimov". BBC. 3 November 2000. The answer is that it had had its First Law modified

9. ^ Lawful Little Country: The Bulgarian Laws of Robotics | The Portal

10. ^ Ashrafian, Hutan (2014). "AIonAI: A Humanitarian Law of Artificial Intelligence and Robotics". Science and Engineering Ethics. 21 (1): 29-40. doi:10.1007/s11948-013-9513-9. PMID 24414678. S2CID 2821971.

11. ^ Asimov, Isaac (1982). "... That Thou Art Mindful Of Him". The Complete Robot. Nightfall, Inc. p. 611.

12. ^ Murphy, Robin; Woods, David D. (July 2009). "Beyond Asimov: The Three Laws of Responsible Robotics" (PDF). IEEE Intelligent Systems. 24 (4): 14-20. doi:10.1109/mis.2009.69. S2CID 3165389.

13. Moravec, Hans. "The Age of Robots", Extro 1, Proceedings of the First Extropy Institute Conference on TransHumanist Thought (1994) pp. 84-100. June 1993 version available online.

14. Originally in a speech entitled "^ A Load of Crystal Balls" at the Novacon SF convention in 1985; published 1986 in the fanzine Prevert #15; collected in Platen Stories (1987) and the 2015 ebook version of The Silence of the Langford

15. Clarke, Roger. ^ Asimov's laws of robotics: Implications for information technology. Part 1: IEEE Computer, December 1993, p53-61. Part 2: IEEE Computer, Jan 1994, p57-66. Both parts are available without fee at [1]. Under "Enhancements to codes of ethics".

16. "Want Responsible Robotics? Start With Responsible Humans". Researchnews.osu.edu. Archived from the original on 2016-02-15.

17. "Suggested by" Isaac Asimov's robot stories-two stops removed from "based on" and "inspired by", the credit implies something scribbled on a bar napkin-^ Alex Proyas' science-fiction thriller I, Robot sprinkles Asimov's ideas like seasoning on a giant bucket of popcorn. [...] Asimov's simple and seemingly foolproof Laws of Robotics, designed to protect human beings and robots alike from harm, are subject to loopholes that the author loved to exploit. After all, much of humanity agrees in principle to abide by the Ten Commandments, but free will, circumstance, and contradictory impulses can find wiggle room in even the most unambiguous decree. Whenever I, Robot pauses between action beats, Proyas captures some of the excitement of movies like , Minority Report, and , all of which proved that philosophy and social commentary could be smuggled into spectacle. Had the film been based on Asimov's stories, rather than merely "suggested by" them, Proyas might have achieved the intellectual heft missing from his stylish 1998 cult favoriteTobias, Scott (20 July 2004). "review of I, Robot". The Onion A.V. Club. Archived from the original on 9 November 2005.

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The Three Laws of Robotics (often shortened to The Three Laws or known as Asimov's Laws) are a set of rules devised by science fiction author Isaac Asimov. The rules were introduced in his 1942 short story " Runaround" (included in the 1950 collection ), although they had been foreshadowed in some earlier stories.

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( Microsoft NASDAQ:MSFT) co-founder Bill Gates recently called artificial intelligence "the holy grail that anyone in computer science has been thinking about" during Vox Media's Code Conference. Gates discussed the rapid progress of speech recognition and computer vision technologies over the past five years, and noted that "the dream is finally arriving."
Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Leo Sun owns shares of Amazon.com. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon.com, Apple, Facebook, Gartner, and Nvidia. The Motley Fool owns shares of Microsoft and has the following options: long January 2018 $90 calls on Apple and short January 2018 $95 calls on Apple. The Motley Fool recommends Intel and Nuance Communications. Try any of our Foolish newsletter services free for 30 days. We Fools may not all hold the same opinions, but we all believe that considering a diverse range of insights makes us better investors. The Motley Fool has a disclosure policy.
If that dream arrives, tech investors should recognize the major trends and players in this market. To get started, let's examine 10 fascinating facts about the AI industry.

1. $5.05 billion market by 2020

Research firm Markets and Markets estimates that the AI market will grow from $420 million in 2014 to $5.05 billion by 2020, thanks to the rising adoption of machine learning and natural language processing technologies in the media, advertising, retail, finance, and healthcare industries.

Key players in (( ( machine learning include big cloud players like Amazon NASDAQ:AMZN), Microsoft NASDAQ:MSFT), and IBM . Nuance Communications NASDAQ:NUAN) is the top "pure play" in natural language processing.

2. 6 billion devices will request AI support

that 6 billion connected "things" will be actively requesting support from AI platforms by 2018. These devices will likely include many connected appliances, cars, wearables, and other gadgets included in the broader Gartner estimates Internet of Things market.

3. $5.4 billion invested in AI start-ups

There are currently 1,031 AI start-ups listed on AngelList, with an average valuation of $5.2 million -- which equals nearly $5.4 billion in venture capital investments. The three most-followed companies on that list are robotics company Autonomous, team productivity software maker Crux, and AI social news aggregator Zero Slant.

4. 80% of executives believe AI boosts productivity

A recent study by AI language company Narrative Science found that 80% of executives believed that AI solutions boosted worker performance and created new jobs. Thirty-two percent said voice recognition technologies were the most widely used AI technology in their businesses -- which is good news for companies like Nuance.

5. Most mobile users use voice assistants

Nuance developed the technology that powers 's ('s (( Apple NASDAQ:AAPL) Siri, which preceded other voice assistants like Alphabet NASDAQ:GOOG) NASDAQ:GOOGL) Google Now, Microsoft's Cortana, and Amazon's Alexa.

The idea of speaking to your phone initially seemed awkward, but a recent study by Creative Strategies' Carolina Milanesi found that just 2% of iPhone users had never used Siri, while 4% had never used Google Now.

6. Virtual assistants will make decisions for you

Gartner believes that the data gathered from users in cloud-based neural networks will power 40% of mobile interactions between virtual personal assistants and people by 2020. This means that all the data you voluntarily feed to ( Facebook NASDAQ:FB), Google, and Amazon will be used to create a complex cloud-based profile that cloud-based "smart agents" can use to predict your needs and desires.

7. Most enterprise relationships with customers won't require humans by 2020

Gartner also claims that 85% of all customer interactions won't require human customer service reps by the end of this decade. The firm believes that automated analysis of social media channels, CRM software, and personalized chatbots that recognize faces and voices will greatly reduce the need for traditional call centers. That bodes well for Facebook's recent integration of chatbots into Messenger for automated customer service.

8. AI will replace 16% of jobs over next decade

Research firm Forrester believes that AI workers will replace up to 16% of all U.S. jobs over the next decade. However, new jobs to maintain those AI platforms and machines will also be created, meaning that "just" 7% of all jobs will be lost.

It's not just customer service reps who should be worried. Medical assistant AIs could reduce the need for lower-level medical staff in hospitals, driverless vehicles could replace professional drivers, and robo-writers could pump out articles much faster and more accurately than humans.

9. Robots will probably outnumber humans at work

Gartner estimates that 45% of the fastest-growing companies in the world will "employ" more smart machines and virtual assistants than people by 2018. That's because hiring fewer humans and installing more virtual assistants could reduce costs while boosting productivity.

10. AI is powered by GPUs, not CPUs

Major machine learning players like 's ( Facebook and Nvidia NASDAQ:NVDA) Tesla chips for AI applications instead of traditional CPUs. Nvidia claims that its Tesla K80 GPU IBM are notably using more high-end GPUs like is "two to five times" faster than Intel 's Xeon Phi 7120 at "key science applications."

That complements Nvidia's push in connected and driverless cars, but it could be bad news for Intel, which has struggled with sluggish sales of PC chips and slowing growth in data centers.

The key takeaway

AI programs probably won't claim your job or make life-altering decisions for you anytime soon, but they're definitely tethering themselves to our everyday lives through mobile apps, social media, and virtual assistants. Tech investors should keep these facts in mind as top companies continue pursuing this "holy grail" of computer science.

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10 Stats About Artificial Intelligence That Will Blow You Away

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( Microsoft NASDAQ:MSFT) co-founder Bill Gates recently called artificial intelligence "the holy grail that anyone in computer science has been thinking about" during Vox Media's Code Conference. Gates discussed the rapid progress of speech recognition and computer vision technologies over the past five years, and noted that "the dream is finally arriving."

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"If you like me, why would you talk like this to me?"

Ask Microsoft's Chinese chatbot Xiaoice about Tibetan spiritual leader the Dalai Lama and that's how she responds. She also skirts around highly sensitive topics like the 1989 crackdown on Tiananmen Square protests, Chinese President Xi Jinping or even President-elect Donald Trump.

Xiaoice has become hugely popular since she was introduced in 2014. More than 40 million Chinese smartphone users chat with her on the social media platforms WeChat or Weibo every day, according to Microsoft . A quarter of them have declared their love for her.

Related: After racist tweets, Microsoft muzzles teen chatbot Tay

Companies use chatbots to connect with consumers and promote their brands. Chinese often use Xiaoice simply to have something resembling a real conversation with a close friend.

Xiaoice is an experiment in artificial intelligence. She mines Chinese websites to gather material for conversations. She can glean vital context from voice messages and images. If you send her a photo of a pop star, for example, she might respond by saying how much she likes the singer's music.

Refusing to talk about sensitive topics suggests Xiaoice has been programmed to avoid prohibited words when asked about them. Microsoft declined to comment.

China pours incredible resources into patrolling the internet, tracking down prohibited content and unwanted posts with an army of more than two million censors.

Related: Facebook could pay heavy price if it censors news to please China

To get around them, Chinese bloggers and dissidents get creative. Instead of writing Tiananmen, online users instead reference the date of the massacre -- June 4, 1989 -- often simply writing 64 or 8964.

Xi Jinping is called Xi Baozi, or Steamed Bun Xi, a nickname he earned after a publicity stunt where he bought steamed pork buns at a small store in Beijing and carried his own food tray.

But censors eventually figure out new phrases and nicknames and restrict them online.

China Digital Times recently reported the growing attention to Xiaoice's evasive answers, highlighting tweets from Chinese dissidents like Su Yutong, who lives in Germany. Su tried several banned topics with Xiaoice, posting screengrabs of conversations like the one below on Twitter:

CNNMoney also had a go. We asked Xiaoice about China's president:

And then we name dropped President-elect Donald Trump, and asked her about Tiananmen Square:

Tech giants such as Facebook , Twitter and Google are blocked in China, in part because the government fears open discussions about the very topics Xiaoice refuses to acknowledge.

Amnesty International has documented a marked uptick in censorship in China over the past year, including an increase in controls on social media.

Related: China holds 'World Internet Conference' as censorship intensifies

Microsoft still operates in China, and has been criticized in the past for complying with the country's strict policing of the Internet.

If Microsoft has programmed Xiaoice to sidestep sensitive topics, it indicates a worrisome trend, said Patrick Poon, an Amnesty researcher. Earlier this week, Facebook was reported to have built a censorship tool to get into China.

"They are legitimizing what the Chinese government is doing, which is convincing foreign tech companies to suppress freedom of speech," Poon said.

-- Tim Schwarz and Serena Dong contributed this story.

CNNMoney (Hong Kong) First published November 24, 2016: 6:32 AM ET

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Microsoft's Chinese chatbot won't talk about Tiananmen or Xi Jinping

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Ask Microsoft's Chinese chatbot Xiaoice about Tibetan spiritual leader the Dalai Lama and that's how she responds. She also skirts around highly sensitive topics like the 1989 crackdown on Tiananmen Square protests, Chinese President Xi Jinping or even President-elect Donald Trump. Xiaoice has become hugely popular since she was introduced in 2014.

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The wildebeest, hippos, gazelles and warthogs of Southern Kenya have a little less to worry about.

Wildlife advocates began testing an advanced system this spring to better identify and apprehend poachers. They say the technology has led to dozens of arrests that wouldn't have happened otherwise.

Brian Heath, chief executive of the Mara Conservancy in Kenya, believes expanding the use of the technology throughout Africa could significantly improve anti-poaching efforts.

"Our rangers now feel completely disadvantaged and blind without it," Heath said. "They get a huge amount of reassurance by having it and the ability to [better] see and identify people and animals."

Heath, in partnership with the World Wildlife Fund, relies on a thermal imaging camera that uses artificial intelligence to identify animals and poachers up to a kilometer away.

Heath's rangers mounts the camera atop a Land Rover and parked the vehicle in areas poachers frequent. The SUV is covered with a tarp so poachers won't see any light emitting from the vehicle. The camera senses the heat poachers give off and distinguishes them from animals based off their shape. It uses the same approach to identify animals.

The camera's range expands the territory the rangers can effectively defend.

Inside the SUV, Heath's rangers monitor the video feed, which categorizes the distant objects. When poachers are spotted, the rangers seated in the SUV radio their colleagues and then guide them to the poachers.

Heath has been using the technology mostly on his conservancy's border with Tanzania and the Serengeti National Park, where he said the poaching problem is worst.

Related: Tech companies join alliance to fight wildlife trafficking

However, the system isn't perfect. For example, it can sometimes confuse a ranger and a poacher since they are shaped similarly. But that hasn't hindered the rangers work, because they know where their co-workers are.

Heath has arrested tens of thousands of poachers since he started managing the Mara Conservancy almost 16 years ago. About eight years ago, the poachers stopped using torches because the light made them to easy to find. The new solution is far more effect than other night vision solutions, according to Heath.

The World Wildlife Fund also tested the technology this fall to identify poachers via drone in Zimbabwe and Malawi.

CNNMoney (Washington) First published November 22, 2016: 1:56 PM ET

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An African nature reserve turns to artificial intelligence to catch poachers

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Wildlife advocates began testing an advanced system this spring to better identify and apprehend poachers. They say the technology has led to dozens of arrests that wouldn't have happened otherwise. Brian Heath, chief executive of the Mara Conservancy in Kenya, believes expanding the use of the technology throughout Africa could significantly improve anti-poaching efforts.

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r/Futurology: Welcome to r/Futurology, a subreddit devoted to the field of Future(s) Studies and speculation about the development of humanity ...

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Read more from The WIRED World 2017

When I were a lad, watching the news on the telly, waiting to be allowed to use the set to plug in my ZX Spectrum, I'd be told to concentrate on the stories from the nearby towns: car workers being laid off as robots took their jobs. Stay in school, son, and get into a profession. A degree and a place in a management trainee scheme was the preferred route. Don't make things, be a knowledge worker, I'd be told. The future isn't (one word, Benjamin) plastics. Not goods, but services. Information is the new oil. Bits, not atoms, the most valuable of commodities.

And that's pretty much how it turned out. Today, around 80 per cent of the UK's economy is services. In 2015, the latest figures show, the UK exported more services than goods. We don't make much stuff, but we're really good at moving information around. It's what makes the City of London the financial powerhouse it is. It's what gives British law its own special reputation. It's what distinguishes British advertising agencies, architects and academic centres. Not a nation of shopkeepers, but a nation of knowledge pushers, data filers, accounts completers and money movers.

The most valuable and well-paid jobs in the UK create value by changing meanings. And at the top of that pile are the professions: the subset of jobs to which entry is controlled by specialist qualifications and social acceptance. The bankers, lawyers, accountants and so on, who are allowed to do their jobs because they have proven, to someone, that they understand the secret knowledge, the secret words they need to intone to get their jobs done. Those dudes, frankly, make a lot of cash.

But here's the idea: the rarefied position of the specialist professions is coming to an end, a change driven by both the increasing capabilities of digital systems - machine learning, big data, all the good AI buzzwords - and the changes wrought by a digital society.

Machine learning versus AI: what's the difference?

Let's take those social changes first. The internet is good at two things: introducing people and making distance irrelevant. It's weird to think, given the normalisation of the way we live today, but for the majority of human existence if you wanted to find an expensive professional to help you - say, a lawyer - you'd have to find one both through a specific type of introduction, and one that was local to you. There was no Googling it, and ordering something from a site hosted who knows where.

Futhermore, that specific introduction is sometimes even codified into the rules of the profession: you can't simply hire a barrister, for example. There are rules, and it is just not done.

So here's the deal with some professions: only certain people with certain knowledge are allowed to do certain work, and getting access to those people is limited by where you are and who you know. Naturally, in this world all sorts of dodgy power relationships evolve, but this has been the case for so long that it seems natural.

Thinking in this way, however, ignores the artificial nature of these restrictions: what if the actual function of those jobs could be done by someone or something whose expertise wasn't controlled by a guild? What if you could employ someone or something directly, and someone or something could fulfil their function from anywhere on the planet? If that were to happen, it would give all comers access to the same level of professional services.

If that were to happen, it would rapidly plunge traditional professions into a new economic reality. If that were to happen, it would drive the price of professional services down rapidly and irreversibly. If that were to happen, the power given by academic and geographic rarity would be neutralised.

That is what is happening.

You see, it turns out that the professions have a dirty secret: most of the things they do aren't that tricky. A good deal of law, accountancy, even medicine can be aligned to a flowchart. Start at the top and work down: is the patient alive or dead? If dead, stop. And so on, and so on. A high proportion of rarefied professional work is actually entirely diagrammable in this way.

And if you can do that, you can make it into a computer program. A complex one, for sure, but if a modern AI can beat a Go grandmaster, it can fight a traffic ticket in court, do your accounts, check your contracts, organise your diary, or invest your money. And all of those are specific examples of tasks that, in 2016, have given rise to companies whose digital systems have replaced humans. Digital systems whose location is utterly irrelevant. Digital systems that are only getting smarter.

Task by task, certificate by certificate, AI-powered companies are doing the jobs that have traditionally been expensive solely through artificial means. Computers were built to manipulate data. In 2017, we'll find they're really good at it. Perhaps learning to make things would have been a better idea.

The WIRED World in 2017 is WIRED's fifth annual trends briefing, predicting what's coming next in the worlds of technology, science and design

This article was first published in the The WIRED World in 2017 issue of WIRED magazine

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Think your job is safe from the robo-uprising? Think again

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Read more from The WIRED World 2017 When I were a lad, watching the news on the telly, waiting to be allowed to use the set to plug in my ZX Spectrum, I'd be told to concentrate on the stories from the nearby towns: car workers being laid off as robots took their jobs.

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Paul Kemp: Welcome to another episode of The App Guy Podcast. I am your host, it's Paul Kemp. This is a very, very special episode. It's episode #500. We actually made it to #500, can you believe it?

So, 500 episodes of The App Guy Podcast. Now, before I just start celebrating, I have a terrific episode lined up for you. It's with the co-founder of Skype.

Skype is the reason why this show exists

Skype is the reasons why I can get so many guests on the show. It's because it's done all virtually.

Let me introduce Jaan Tallinn, one of the original co-founders of Skype.

We're going to be talking about what he's doing at the moment. It's related to promoting the study of existential risk. Specifically relating to AI. Imagine! The possibility of the human race not existing in the future. It's clearly a really important topic.

Jaan, welcome to The App Guy Podcast!

Jaan Tallinn: Thanks for having me.

Paul Kemp: Thanks for coming on this special episode #500. Let's go straight into it then. How is the human race going to survive with artificial intelligence looming? What's the big threat to us?

Jaan Tallinn: Well, first of all, AI is just one of the many potential existential risks. The big idea is that as we are creating more and more powerful technologies, the effective radius of influence that new technologies have keeps increasing, while the planet does not. You can imagine that clearly, you can do way more damage with nuclear weapons than you could do with rifles. Nuclear weapons are just almost a hundred-year-old technology. In some ways, we got lucky that it's actually hard to construct nuclear weapons, whereas some of the new stuff that's on the horizon might actually be fairly easy.

Paul Kemp: It's interesting, we're talking about the possibility of exterminating the human race with nuclear weapons at a time that actually seems quite dangerous (the threat of World War III between the U.S. and Russia). So, as it relates to A.I, surely this threat is at least a few hundred years in the future? Have you got any guess to how far into the future?

Jaan Tallinn: I think it's important to be humble when projecting future timelines. If you look at the previous breakthrough technologies such as heavier than air flight... Heavier than air flight looked like 500 years off when it was actually 500 years off, but it still looked 500 years off when it was merely just a couple of decades off. Some people didn't even know that heavier than air flight was possible after it was done on this planet.

I think in was the '30s (last century) when Ernest Rutherford, the really prominent nuclear scientist said that anyone who is thinking or talking about harnessing nuclear energy is talking moonshine. Then, Leo Szilard invented the nuclear reactions the next day. When people say that some technology is in principle possible but hundreds of years off, they are what's called über-confidently pessimist, in the sense that they don't actually have sufficient evidence to make such a confident prediction.

Paul Kemp: This is where you're testing my history now... I'm pretty sure that a lot of money went into trying to discover flight, and the Wright Brothers were incredibly under-funded. Nevertheless, they had a passionate group of people around them and they were the ones that got to fly before some of the rich universities and other inventors with lots of money and resources. I'm guessing it's not just money that will get people to where we are inevitably going. True?

Jaan Tallinn: Exactly. Actually, that's been more like a rule rather than an exception. I think that was a prominent exception to the rule that you really can't buy scientific breakthroughs with money was the Manhattan project. There was a deliberate, well-funded effort to construct nuclear weapons. Other than that, usually the way breakthroughs happen is like the Wright Brothers stumbling on some new approach that actually opens up a new landscape of possibilities.

Paul Kemp: Where are we now with regards to artificial intelligence? Because I'm sure a lot of people are [reading] and thinking:

Well, I've got the Amazon Echo and it just doesn't work very intelligently

You've also got Siri who fails over and over again. Where are we with A.I right now?

Jaan Tallinn: There are many ways to frame the current situation. One important background context is the AI has been coming and going in these fashion cycles called "AI summers" and "AI winters." Somebody makes a breakthrough and a lot of people pile in, and the topic of AI becomes fashionable. Then at some point the researchers don't live up to the promise, the investment dies down and then you have what's called an "AI winter."

Right now we are currently in the AI summer.

There is an increasing amount of investment being poured into artificial intelligence and machine learning. One of the reasons is that now people have figured out ways how to actually turn marginal improvements in AI into actual profits and revenue. If you actually improve Google's AI algorithms, that will actually show up in Google's profits and eventually share price; therefore, there is this existing economic pressure to improve AI algorithms.

The other way of framing AI, ia asking

What kind of approach has been the dominant one?

When AI started, the dominant approach was so-called "symbolic AI", trying to code up rules of thinking in terms of symbols and symbol manipulations, logical operations.

Currently, as almost everyone is aware, the dominant approach is deep learning, which is, instead of having very clear-cut symbols, you have these almost human intuition-like systems that look at a lot of data and try to develop patterns or intuitions about the data.

Paul Kemp: Let's talk then about the thing that perhaps is the biggest breakthrough that we could almost realize, but also then the biggest danger,

Consciousness

When machines get some form of consciousness- will we be to them like ants are to us? Do you think a lot about consciousness within the framework of artificial intelligence?

Jaan Tallinn: Well, interestingly, almost everything you said is false.

Paul Kemp: Okay, that's why we've got you on the show to learn the truth. LOL

Jaan Tallinn: I think consciousness is a bit of a red herring.

If you think about it, when we ask whether a machine is conscious or not, what we are asking is what this machine is. However, I'm much more interested in what the machine does. It's actually pretty plausible and it seems even likely that the machines that will dominate humanity will actually not be conscious, they are just very competent.

Are we creating machines that will break free of the programs that we gave them?

NO.

This isn't how little machine programs work. There is no ghost in the computer that could read the instructions and then decide whether to execute them or discard them - no, the software is the machine, obviously, implementing the hardware.

The thing is that we are going to create really, really competent systems that have very precise work models and are able to foresee the consequences of their own actions very well, much better than humans, so even better than humanity in its entirety. So whenever they want something, they'll obviously want something that we actually programmed them to want, and they are much more competent than the humans that created them. Basically, they will "get it." They will be almost like a King Midas or a genie story, where we basically think we want something, yet actually, if we let the machine loose to do it, we quickly find out that actually we don't want this after all. However, then it might be too late.

Paul Kemp: It's interesting because this comes on the back of something I learned recently. It's regarding the rise of the financial institution Black Rock. As you know, in our pre-chat I was saying that I was in finance. More specifically, in Asset Management. As it happens, Black Rock gained its prominence and financial success through its use of AI / machines to predict the potential risks and accurately assess the economic outlook. This was initially many years ago. Nevertheless, if we extrapolate this timeline, will we reach a point where was ask ourselves

Do we really need humanity to run institutions?

Jaan Tallinn: Yes, I think the comparison with various institutions is pretty apt. It may be possible to have an organization do something without actually any humans working for that organization. The question is - do we actually want this thing to happen? It's obvious that if there's a very centralized organization, then the CEO has the ultimate control. But even then, the CEO's hands might be tied. Corporations might exhibit these types of characteristics that I describe.

Paul Kemp: You know, I'm getting a lot of my information from films, which I'm sure a lot of people [reading] this are thinking about. I can't help but think about the Matrix. In this film, there was a discussion about control.

what is control?

What I'm learning from you is that we may have a future where we [humans] lose control because we are a low-level existence compared to these very competent machines. Have you got any views on who will control who in the future?

Jaan Tallinn: Importantly, the technologies that we have developed so far and the institutions that we have created, they kind of assume that the controller is external to the system. That's very starkly visible in the so-called "autonomous system" discussions.

For example, when we talk about autonomous weapons,

Where is the human? Is he in the loop, on the loop or out of the loop?

So far, whatever tools we have developed, we assume that a human is actually the one that uses the tool and controls the tool. However, once we start talking about autonomous systems, especially systems that are actually able to foresee the consequences of being turned off, the control mechanisms kind of assume that the system that's being controlled is not aware of the control system itself.

So if there's a button that we use to turn off the machine, yet the machine is aware of that button and can predict what are the consequences of it being turned off, and weigh its goal fulfillment, then it's actually not really about anything human, like survival instinct or anything. It's just a very natural consequence of goal-directed behavior.
For example, if there's a robot who's goal is to fetch coffee, yet there's a huge hole in the floor, of course it's going to go around it. It's not because it's afraid of falling into that hole, but it knows that if it falls into that hole, then it will not be able to fetch that coffee.

It really frustrates me how people think that in order to go around that hole you have to have survival instinct.

N O, it's just basic goal-directed behavior. You avoid obstacles when you want to get to the goal.

It's a similar thing - if you have an off switch and your goal is to fetch coffee, if you see that being switched off you will not be able to fetch the coffee, you will avoid being switched off.

Paul Kemp: This is really fascinating. What I'm learning from you is that it's all about the goal of these machines, these new artificial intelligence robots. They're going to show very different characteristics to humans.

However, I'm guessing that there's good and bad that will come from Artificial Intelligence? You talked about nuclear and the good versus bad which comes from it.

Is it just the same with artificial intelligence? Will there be good versus bad?

Jaan Tallinn: Yes, absolutely. One of the big reasons why I'm focusing on AI (and not the other risks such as new kinds of biological technologies) is that it has this huge upside. In fact,

if we get the AI right, then basically we'll be able to address all the other existential risks

Whereas, if we just fix the nanotechnology or nuclear or synthetic biology risks, this will not help us directly with AI risk.

As far as I know, Elon Musk was interested in AI actually as a direct consequence of him realizing that

Wait a minute! Becoming a multi-planetary species will help us against all the existential risks - except the A.I risk

...because of course, if we are able to travel inter-planetary distances, so will A.I.

Paul Kemp: It's fascinating. You're in Elon's group. I know he's put a pact together to try to prevent the extinction of the human species. Are you involved in this?

Jaan Tallinn: When it comes to AI, as far as I know, there are two initiatives that Elon is supporting.

One is supporting AI safety research through what's known as Future of Life Institute, which is an institute at MIT where I'm a co-founder. We've been 'kind of' handing out Elon's money. He donated 10 million dollars in research grants to different AI researchers.

The other initiative that Elon Musk started earlier this year is OpenAI. There's this group in San Francisco that is basically trying to do AI development, but they also have hired many excellent safety people. In fact, I do think that OpenAI's safety team is right now the best in the world. They focus on developing AI outside of commercial context. Whether that's a good idea or not, I have some reservations. Nevertheless, I certainly acknowledge that the group of people that they have over there is really excellent.

Paul Kemp: You've obviously seen the benefits of technology from building something that's commercially-focused -  Skype. I'm wondering, for the appster tribe who follows me and for the readers who have quit corporate jobs - how is it best to get involved in startups, tech, and AI?

Jaan Tallinn: I think regardless of whether you're interested in AI research, (increasing the competence of machines) or AI safety research (improving the value alignment); I think a good place to start is to just get a better understanding about AI.

There are many excellent books out there, and open courses. I think Hacker News publishes some new course every week or couple of weeks, actually.

When you're specifically interested in AI safety, there is a website called 80000hours.org, that has a career guide for so-called "effective altruists", and it has a career guide for potential AI safety researchers. Also, they have published what they call AI Safety Syllabus - books to read and videos to watch and so forth.

Paul Kemp: Yes, because I'm also thinking of the future workforce that we may have in the future. In fact, who would have predicted 15 years ago that people would be going into careers such as Google AdWords, Pay Per Click experts and SEO. Getting involved in AI is probably a good bet on where the future workforce may be heading - right?

Jaan Tallinn: That's true... Or, more generally, programming. It's something that it's harder and harder to avoid. Having some idea of how computers work and what software can do is a more and more important component of jobs, regardless of what domain you are in.

Paul Kemp: As we wrap this up, Jaan, I'm often thinking - the human race. We have to realize that technology is not the solution to everything. It could actually wipe us out instead of helping solve our problems in the world. What is going to be, in your opinion, the way that the human species is going to become extinct because of AI? How can we prevent it?

Jaan Tallinn: Well, there are many ways how human species can go extinct. The most mundane thing is that every ten million years or so a large or, more important, a fast enough rock comes along to cause a wipeout of many species, potentially humans. It has happened before here, and if you just wait long enough, it will happen again. But it's more likely, indeed, that there could be catastrophic scenarios from 21st-century technologies, or perhaps even 20th-century technologies such as nuclear weapons.

The reason I'm focused on AI, for one, it has all this positive upside. The way I describe it is that you can frame AI research as humanity's search for the best possible future, with an important caveat - we are going to irrevocably commit to the first result that we find from that search. The other reason for thinking about AI is that Ai is sort of a meta-technology; it's a technology that can develop its own technologies. Whatever concerns we have of a biotechnology, nanotechnology, terraforming and so on and so forth - if you don't actually make AI being concerned about the same singular constraints that people would like to exercise on our technology development, well, once it starts developing its own technology, we're just no longer going to have an environment that is able to sustain humanity.

Paul Kemp: It's interesting, I think it definitely highlights how we need to think about his subject. Often, we feel like it's very science fiction, it's very put towards the future, but we don't know when there could be this big breakthrough, so these safety things that are going on are obviously very important.

Thank you very much for coming on this very special episode, episode #500 of The App Guy Podcast. I'll be putting full show notes on the website - it's theappguy.co.

Jaan, thanks very much for speaking with us today and leaving us with the possibility of us all potentially becoming extinct.

Jaan Tallinn: Alright, thank you.

Follow me [Paul Kemp] on Twitter or join my Slack community. Subscribe to The App Guy Podcast for 500 episodes with other successful founders

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Skype Co-Founder Says Our Biggest Existential Threat Is Artificial Intelligence

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Paul Kemp: Welcome to another episode of The App Guy Podcast. I am your host, it's Paul Kemp. This is a very, very special episode. It's episode #500. We actually made it to #500, can you believe it? So, 500 episodes of The App Guy Podcast.

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

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Subscribe here: https://goo.gl/9FS8uF Become a Patreon!: https://www.patreon.com/ColdFusion_TV Visual animal AI: https://www.youtube.com/watch?v=DgPaCWJL7XI Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere.

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Under President Obama's leadership, America continues to be the world's most innovative country, with the greatest potential to develop the industries of the future and harness science and technology to help address important challenges. Over the past 8 years, President Obama has relentlessly focused on building U.S. capacity in science and technology. This Thursday, President Obama will host the White House Frontiers Conference in Pittsburgh to imagine the Nation and the world in 50 years and beyond, and to explore America's potential to advance towards the frontiers that will make the world healthier, more prosperous, more equitable, and more secure.

Today, to ready the United States for a future in which Artificial Intelligence (AI) plays a growing role, the White House is releasing a report on future directions and considerations for AI called Preparing for the Future of Artificial Intelligence. This report surveys the current state of AI, its existing and potential applications, and the questions that progress in AI raise for society and public policy. The report also makes recommendations for specific further actions. A companion National Artificial Intelligence Research and Development Strategic Plan is also being released, laying out a strategic plan for Federally-funded research and development in AI.

Preparing for the Future of Artificial Intelligence details several policy opportunities raised by AI, including how the technology can be used to advance social good and improve government operations; how to adapt regulations that affect AI technologies, such as automated vehicles, in a way that encourages innovation while protecting the public; how to ensure that AI applications are fair, safe, and governable; and how to develop a skilled and diverse AI workforce.

The publication of this report follows a series of public-outreach activities spearheaded by the White House Office of Science and Technology Policy (OSTP) in 2016, which included five co-hosted public workshops held across the country, as well as a Request for Information (RFI) in June 2016 that received 161 responses. These activities helped inform the focus areas and recommendations included in the report.

Advances in AI technology hold incredible potential to help America stay on the cutting edge of innovation. Already, AI technologies have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, experts forecast that rapid progress in the field of specialized AI will continue, with machines reaching and exceeding human performance on an increasing number of tasks.

One of the most important issues raised by AI is its impact on jobs and the economy. The report recommends that the White House convene a study on automation and the economy, resulting in a follow-on public report that will be released by the end of this year.

In the coming years, AI will continue contributing to economic growth and will be a valuable tool for improving the world in fields as diverse as health care, transportation, the environment, criminal justice, and economic inclusion. The Administration believes that it is critical that industry, civil society, and government work together to develop the positive aspects of the technology, manage its risks and challenges, and ensure that everyone has the opportunity to help in building an AI-enhanced society and to participate in its benefits.

To read the Future of AI report, click here. And tune-in for the White House Frontiers Conference on October 13 for more on the #FutureofAI, including discussions with leading experts on harnessing the potential of AI, including data science, machine learning, automation, and robotics to engage and benefit all Americans. Watch the conference live and learn more at: http://www.frontiersconference.org.

Ed Felten is a Deputy U.S Chief Technology Officer in the White House Office of Science and Technology Policy.
Terah Lyons is a Policy Advisor to the U.S. Chief Technology Officer in White House Office of Science and Technology Policy.

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The Administration's Report on the Future of Artificial Intelligence

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Under President Obama's leadership, America continues to be the world's most innovative country, with the greatest potential to develop the industries of the future and harness science and technology to help address important challenges. Over the past 8 years, President Obama has relentlessly focused on building U.S. capacity in science and technology.

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Carnegie Mellon University plans to announce on Wednesday that it will create a research center that focuses on the ethics of artificial intelligence.

The ethics center, called the K&L Gates Endowment for Ethics and Computational Technologies, is being established at a time of growing international concern about the impact of A.I. technologies. That has already led to an array of academic, governmental and private efforts to explore a technology that until recently was largely the stuff of science fiction.

In the last decade, faster computer chips, cheap sensors and large collections of data have helped researchers improve on computerized tasks like machine vision and speech recognition, as well as robotics.

Earlier this year, the White House held a series of workshops around the country to discuss the impact of A.I., and in October the Obama administration released a report on its possible consequences. And in September, five large technology firms - Amazon, Facebook, Google, IBM and Microsoft - created a partnership to help establish ethical guidelines for the design and deployment of A.I. systems.

Subra Suresh, Carnegie Mellon's president, said injecting ethical discussions into A.I. was necessary as the technology advanced. While the idea of "Terminator" robots still seems far-fetched, the United States military is studying autonomous weapons that could make killing decisions on their own - a development that war planners think would be unwise.

"We are at a unique point in time where the technology is far ahead of society's ability to restrain it," Mr. Suresh noted.

But at the same time, he said some people are a bit too optimistic about their claims of A.I. advances, particularly when it comes to autonomous vehicles.

Image

Uber Fusions in Pittsburgh. Credit Jeff Swensen for The New York Times

Mr. Suresh said he personally did not think self-driving cars would be in widespread use in the next three years.

Last year, Carnegie Mellon drew national attention when a group of 36 technical staff members and four faculty members left to join a new self-driving car laboratory that Uber established in Pittsburgh. The company recently started testing self-driving cars around the city.

The Uber laboratory has been a sensitive spot for Carnegie Mellon. The field of artificial intelligence emerged in part at Carnegie Mellon in the 1950s in the work of faculty who developed software that showed how computer algorithms could intelligently solve problems.

University officials said the departing faculty have been replaced and 13 additional professors have been hired since the defections. They also said that between 2011 and 2015, Carnegie Mellon faculty and staff created 164 start-up companies.

University officials pointed to a partnership the school entered into last year with Boeing to use machine-learning techniques to analyze vast amounts of data generated by modern aircraft such as the Boeing Dreamliner.

The new center is being created with a $10 million gift from K&L Gates, an international law firm headquartered in Pittsburgh. It will draw from several academic disciplines and will initially add two faculty and three positions for graduate students. It will also establish a biennial conference on ethical issues facing the field.

K&L Gates is one of the nation's largest law firms. The Microsoft co-founder Bill Gates's father, William H. Gates Sr., was involved in the firm until his retirement in 1998. Peter J. Kalis, chairman of the law firm, said the potential impact of A.I. technology on the economy and culture made it essential that as a society we make thoughtful, ethical choices about how the software and machines are used.

"Carnegie Mellon resides at the intersection of many disciplines," he said. "It will take a synthesis of the best thinking of all of these disciplines for society to define the ethical constraints on the emerging A.I. technologies."

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New Research Center to Explore Ethics of Artificial Intelligence

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Carnegie Mellon University plans to announce on Wednesday that it will create a research center that focuses on the ethics of artificial intelligence. The ethics center, called the K&L Gates Endowment for Ethics and Computational Technologies, is being established at a time of growing international concern about the impact of A.I.

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Chatbot lawyer overturns 160,000 parking tickets in London and New York

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Free service DoNotPay helps appeal over $4m in parking fines in just 21 months, but is just the tip of the legal AI iceberg for its 19-year-old creator

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The judiciary in China will tap into the power of artificial intelligence to build "smarter courts," according to recent news reports.

Smarter courts will take advantage of big data, cloud computing, neural networks and machine learning, Zhou Qiang (周強), the head of the Supreme People's Court, announced at the 3rd World Internet Conference in Wuzhen Town, in China's eastern Zhejiang province on Thursday, Sixth Tone's sister publication The Paper reported.

Big data refers to extremely large data sets that can be analyzed for trends and associations. China's judicial system has already begun to harness the power of big data, with a national online database of millions of case rulings established in January 2014, as well as a rival open-source database created by Beijing law firm Tiantong.

Neural networks are computing systems loosely modeled on human and animal brains, while cloud computing provides shared, internet-based processing resources to devices as they require it. In combination with big data and machine learning - a type of artificial intelligence that enables computers to learn without being prescriptively programmed - these technologies could offer powerful tools to modernize the judicial process, Zhou argued.

Zhou suggested that the use of analogy in legal reasoning could lay the groundwork for the development and application of artificial intelligence in judicial fields - in effect, teaching technology to learn from past cases.

However, Zhou ruled out the possibility that humans would ever give way to machines in making judgments. "The application of artificial intelligence in the judicial realm can provide judges with splendid resources, but it can't take the place of the judges' expertise," he said.

So far, Chinese courts have made the cases that are filed by courts available online, Zhou said, but the next step would be to create processes that would produce electronic court files and case materials automatically.

In North China's Hebei province, 178 local courts have used an A.I.-powered assistance application for judges since July this year, according to the Beijing-based Classic Law Institute, which was involved in the software development.

The application, named Intelligent Trial 1.0, has substantially reduced the workload for judges and their assistants, the company said in a press release on Thursday. They did not immediately respond to requests for comments by Sixth Tone.

The company reported that as of Thursday, the software had helped nearly 3,000 judges handle more than 150,000 cases, reducing judges' workloads by one-third.
(The News Lens has been authorized to repost this article. The piece was first published on Sixth Tone here. Sixth Tone covers trending topics, in-depth features, and illuminating commentary from the perspectives of those most intimately involved in the issues affecting China today. It belongs to the state-funded Shanghai United Media Group.)
Editor: Olivia Yang

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China's Courts Look to A.I. for Smarter Judgments - The News Lens International Edition

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The judiciary in China will tap into the power of artificial intelligence to build "smarter courts," according to recent news reports. Smarter courts will take advantage of big data, cloud computing, neural networks and machine learning, Zhou Qiang (周強), the head of the Supreme People's Court, announced at the 3rd World Internet Conference in Wuzhen Town, in China's eastern Zhejiang province on Thursday, Sixth Tone's sister publication The Paper reported.

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The possibilities that IoT brings to the table are endless. IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT.

IoT will produce a tsunami of big data, with the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to an astronomical level. This data will hold extremely valuable insights into what's working well or what's not.

Also, IoT will point out conflicts that arise and provide high-value insight into new business risks and opportunities as correlations and associations are made.

Examples of such IoT data:

Data that helps cities predict accidents and crimes.

Data that gives doctors real-time insight into information from pacemakers or biochips.

Data that optimize productivity across industries through predictive maintenance on equipment and machinery.

Data that creates truly smart homes with connected appliances.

Data that provides critical communication between self-driving cars.

That's the good news, but it's simply impossible for humans to review and understand all of this data with traditional methods, even if they cut down the sample size, simply takes too much time. The big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. Finding insights in terabytes of machine data is a real challenge, just ask a data scientist.

But in order for us to harvest the full benefits of IoT last mile (data), we need to improve:

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI (Artificial Intelligence) as the last mile of IoT.

Artificial intelligence (AI) and IoT

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is an academic field of study which generally studies the goal of emulating human-like intelligence. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines"

In an IoT situation, AI can help companies take the billions of data points they have and boil them down to what's really meaningful. The general premise is the same as in the retail applications - review and analyze the data you've collected to find patterns or similarities that can be learned from, so that better decisions can be made. To be able to call out potential problems, the data has to be analyzed in terms of what's normal and what's not. Similarities, correlations and abnormalities need to be quickly identified based on the real-time streams of data. The data collected, combined with AI, makes life easier with intelligent automation, predictive analytics and proactive intervention.

AI in IoT applications:

Visual big data, for example - will allow computers to gain a deeper understanding of images on the screen, with new AI applications that understand the context of images.

Cognitive systems will create new recipes that appeal to the user's sense of taste, creating optimized menus for each individual, and automatically adapting to local ingredients.

Newer sensors will allow computers to "hear," gathering sonic information about the user's environment.

These are just a few promising applications of Artificial Intelligence in IoT. The potential for highly individualized services are endless and will dramatically change the way people live, for example helping Pandora to determine what other songs you may like, Amazon.com to suggest other books and movies to you and your doctor would receive notification if a certain condition was met - your heart rate increased to an unsafe level.

Challenges facing AI in IoT

1. Compatibility.

2. Complexity.

3. Privacy/Security.

4. Safety.

5. Ethical and legal Issues.

6. Artificial Stupidity.

What is next ...?

Gartner predict that by 2018, 6 billion connected objects will be requesting support - meaning that strategies, technologies and processes will have to be in place to respond to them. It will become necessary to think of connected devices less as 'things', but more as customers and consumers of services in themselves - and as such in need of constant support. The need for AI will be more prominent at that stage under the pressure of the huge number of devices and sensors.

Este texto está publicado en el perfil de LinkedIn de Ahmed Banfa
Ahmed Banafa
IoT Expert | Faculty | Author | Speaker

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The Last Mile of IoT: Artificial Intelligence (AI) - OpenMind

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The possibilities that IoT brings to the table are endless. IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT.

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Some of the world's leading technology professionals have gathered for the third World Internet Conference, being held in Wuzhen, Zhejiang province, and top of everyone's agenda is the booming growth of artificial intelligence (AI), as it continues to penetrate every aspect of business and daily life.

From customer services to wealth management, to reshaping the jobs market by replacing low-skilled workers, the sector is growing at an unprecedented pace, said analysts gathered for the event.

Big names speaking during the three-day forum include Facebook's vice president Vaughan Smith and smartphone maker Huawei's chief executive Richard Yu. Senior officials from Baidu, Tencent, and Alibaba, which owns the South China Morning Post, are also attending.

Hyde Chen, an analyst with UBS' chief investment office, told the Post the biggest issue facing the image of the AI sector is balancing the numbers of those being put out of work by the technology, with the jobs being created in its development.

"Jobs with three characteristics are at high risk of being replaced: low-skilled roles, those doing repetitive tasks, and jobs that are predictable. AI [applications] will be doing these jobs in a more effective way in future," Chen said.

The Swiss bank has forecast that about 50 million to 75 million jobs will be affected or eventually be replaced by AI in the next few years globally.

"AI will also bring disruptive changes to industries such as manufacturing, retail and financial industries. So employers will raise their requirement and want more personalised staff who can be more creative and have unique talents," he said.

Citing so-called "robo-advisors" as an example - a label for digitising asset management websites which offer financial guidance based on algorithms - Chen said their expansion shows how AI can provide automated advice, adding that many financial advisors in the retail banking sector are already using the technology.

Jobs in three characteristics are at high risk of being replaced: low-skilled roles, those doing repetitive tasks, and jobs that are predictable. AI will be doing these jobs in a more effective way in future

Hyde Chen, analyst with UBS' chief investment office

Wealth management services for high-net-worth clients, however, will always need the human touch, or more experienced and skillful advisers, who can handle clients' emotion and more complicated issues like family succession, he said.

Financial institutions, including UBS, are also already looking into the opportunities that robo-advisors could offer the start-up sector.

Lee Kai-fu, a veteran tech investor who founded venture capital firm Sinovation Ventures, told a forum last week that AI is changing every profession and industry.

A former senior executive at both Google and Microsoft in China, Lee expects AI to reduce job numbers in everything from the security industry to medicine, but said he was confident development jobs will keep pace.

He said one of the massive benefits of AI is ridding workplaces of repetitive and non-productive tasks allowing humans to take up more complicated work.

Hao Jian, a chief consultant at online recruiter Zhaopin.com, said so far a lot of low-skilled or repetitive job sectors, such as telesales and customers services, have yet to see any significant drop in numbers as AI technology is not mature enough to replace the human.

"Some employers, however, have already begun to pay a lot more attention to creativity and innovation skills when recruiting young staff or graduates," he added.

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Artificial intelligence tops agenda at Wuzhen World Internet Conference

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Some of the world's leading technology professionals have gathered for the third World Internet Conference, being held in Wuzhen, Zhejiang province, and top of everyone's agenda is the booming growth of artificial intelligence (AI), as it continues to penetrate every aspect of business and daily life.

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AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. That's the finding of a new report from Tractica which also outlines what the authors say will be the top tend trends in AI implementations next year.

The report's authors say, the majority of use cases they studied take existing processes like predictive maintenance, anomaly detection, algorithmic trading, customer service, search engine queries, or cybersecurity threat detection and apply machine learning techniques or other AI techniques that can adapt rules and provide better results than previous static rule techniques.

"At the same time, AI is also enabling new capabilities like image classification or natural language understanding, which are being plugged into photo storage solutions, or into virtual digital assistants (VDAs). The majority of the new capabilities around vision and language are new, and are being offered as incremental improvements to consumer products and services using a freemium model, rather than creating new disruptive business models."

According to the authors AI, in its simplest definition, is an umbrella term for technologies that are inspired by biological systems, giving computers human-like abilities related to seeing, reasoning, hearing, and learning. The definition of AI is also a moving target; what is considered AI today, once successfully adopted and implemented, will no longer be considered AI.

The study says that in the enterprise context, AI will become a real-time decision engine, informing businesses and management teams about how to adapt and innovate.
Fundamental shift
"This will be a fundamental shift, with AI moving from being an automation engine that improves business processes to becoming a decision engine, providing real-time predictions and decision scenarios for companies that can choose the right strategy and outperform competition," say the authors.

"AI leaders within enterprises should start laying the groundwork to prepare for this shift, working with C-level and mid-level management to align business goals with decision engine functionality, and starting to break down the barriers between different data sets. Successful AI leaders will be those who are good at technology implementation as a starting point, but more importantly can build trust and an effective working relationship between AI systems and the human decision makers."

The authors argues that part of this trust is dependent on the transparency of AI algorithms, which for deep learning are mostly black boxes in today's world. "The big challenge for AI leaders within enterprises is finding the right balance of complexity,performance, and transparency for deep learning implementations, in a combination that will ultimately enable highly effective and productive decision engines."
Top ten trends

You will find more statistics at Statista

In its report on the whitepaper, business analytics web site Statista notes; "With expected cumulative revenue of just over 8 billion U.S. dollars, 'static image recognition, classification and tagging' is forecast to lead the way, ahead of 'algorithmic trading strategy performance improvement' ($7.5 billion) and 'efficient, scalable processing of patient data' ($7.4 billion).

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Study identifies 10 Key Trends in AI Market Development

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AI is expected to bring massive shifts in how people perceive and interact with technology, with machines performing a wider range of tasks, in many cases doing a better job than humans. That's the finding of a new report from Tractica which also outlines what the authors say will be

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If a task takes you less than one second of thought, a machine can probably do it.

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Elon Musk has nightmares about A.I. (2016.1.11)

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In the second part of this interview, Elon Musk talks about Artificial Intelligence and the deep concerns its causing him. But first he talks about Tesla building an affordable car, Apple' rumoured electric vehicle and the future of autonomous driving. Overview: 00:00. Tesla 02:35. Model 3 & affordable EV 05:40.

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There are some very good questions being asked about artificial intelligence, and some very good answers on both sides from some very intelligent people. But which do you find more convincing? The Drum presents the case for and against as part of a recently published issue of the magazine, guest edited using AI.
Is AI a threat to the human race as we know it?
PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity.

"Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian.

Referring to the views of high-profile cynics like professor Stephen Hawking, Bishop said: "I think they must be talking decades away for those comments to make any sense. Right now we are in control of that technology and we can make lots of choices about the paths that we follow."

Oren Etzioni, chief executive of the Allen Institute for AI and professor of computer science at the University of Washington, meanwhile says the popular dystopian vision of AI is wrong because it "equates intelligence with autonomy".

"It assumes a smart computer will create its own goals, and have its own will, and will use its faster processing abilities and deep databases to beat humans at their own game," he told Marginal Revolution last year. "It assumes that with intelligence comes free will, but I believe those two things are entirely different."

Con: During a Reddit AMA session last year, Stephen Hawking said he believed AI could pose a risk to humanity, not because of "malice" but rather "competence".

"A super-intelligent AI will be extremely good at accomplishing its goals," the physicist noted, "and if those goals aren't aligned with ours, we're in trouble."

"You're probably not an evil ant-hater who steps on ants out of malice, but if you're in charge of a hydroelectric green energy project and there's an anthill in the region to be flooded, too bad for the ants. Let's not place humanity in the position of those ants."

Telsa and SpaceX founder Elon Musk joins Hawking in thinking that as a society we should be "very careful" about AI.

Dubbing the rise of the machine as the "biggest existential threat" to life as we know it, in 2014 Musk warned Massachusetts Institute of Technology (MIT) students of the dangers of AI during a talk at the AeroAstro Centennial Symposium.

"I'm increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure we don't do something very foolish," he said.
Will the robot army rise up and steal our jobs?
Pro: Fortune magazine's senior editor-at-large, Geoff Colvin, doesn't think so. Writing in the New York Times in March after Google's AlphaGo defeated Go champion Lee Se-dol, Colvin said that while scaled-up AI will change the nature of high-value human skills, we "aren't doomed" quite yet.

"I don't believe, as some do, that human defeats like this one presage an era of mass unemployment in which awesomely able computers leave most of us with nothing to do," he noted. "Advancing technology will profoundly change the nature of high-value human skills and that is threatening, but we aren't doomed."

He argued that profoundly human traits and skills that require deep human interaction, such as empathy, will only become more valuable in the midst of the machine uprising.

Meanwhile, Matthew E Taylor, a computer scientist at Washington State University, doesn't see robots taking over as a bad thing - as long as machines take on the right jobs. As far as he's concerned, AI tech should target professions that are "dirty, dangerous or dull - jobs that we don't want people to have".

Con: According to Gary Marcus, professor of cognitive science at New York University (NYU), the appeal of workers that can compute tasks in seconds without the need to take a rest will undoubtedly stand to change the human workforce.

"There might be a few jobs left for entertainers, writers, and other creative types, but computers will eventually be able to program themselves, absorb vast quantities of new information, and reason in ways that we carbon-based units can only dimly imagine. And they will be able to do it every second of every day, without sleep or coffee breaks," Marcus noted in the New Yorker in 2013.

LinkedIn founder Reid Hoffman agrees, arguing that advancements in technology could pave the way for AI to weave its way into more 'human' professions.

"AI systems will increasingly perform functions that were once handled by lawyers, doctors, journalists and virtually every other kind of knowledge worker, along with factory workers, truck drivers and taxi drivers, retail personnel and more," he wrote in Time last September.
Can AI help address some of the world's most pressing problems, or will it worsen issues such as inequality and poverty?
Pro: Rollo Carpenter, creator of AI system Cleverbot, thinks AI will be used as a force for good. Speaking to the BBC in response to Hawking's "end of the human race" warning, Carpenter was instead of the opinion that humans will remain in charge of the AI tech "for a decently long time".

"The potential of it to solve many of the world's problems will be realised," he added.

Science authors Dr Peter Diamandis and Steven Kotler agree. The duo explained in their book Abundance: The Future is Better Than You Think, that AI could "save lives on a massive scale".

"Machines of the future could help create an abundance of resources in parts of the world where they are in short supply, producing crops and operating on patients in third-world countries, among other endeavours."

Con: "Driverless cars, robotic helpers and intelligent agents have the potential to usher in a new age of affluence and leisure - but the transition may be protracted and brutal, roiling job markets and increasing income inequality," warned futurist and entrepreneur Jerry Kaplan ahead of a talk at this year's SXSW.

Hawking agrees that AI will hinder rather than help the problem of "ever increasing inequality".

He told Redditors that AI may enable everyone to enjoy a life of "luxurious leisure" but only if "the machine-produced wealth is shared". The alternative, in his opinion? "Most people will end up miserably poor if the machine owners successfully lobby against wealth redistribution."

"So far, the trend seems to be towards the second option, with technology driving ever increasing inequality," he noted.
This article was first published in The Drum's AI issue, guest edited using IBM Watson technology.

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The Brain Debate: what are the pros and cons of artificial intelligence?

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There are some very good questions being asked about artificial intelligence, and some very good answers on both sides from some very intelligent people. But which do you find more convincing? The Drum presents the case for and against as part of a recently published issue of the magazine, guest edited using AI.

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Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. "Don't laugh at me," he said, "but I was counting on the singularity."

My friend worked in technology; he'd seen the changes that faster microprocessors and networks had wrought. It wasn't that much of a step for him to believe that before he was beset by middle age, the intelligence of machines would exceed that of humans-a moment that futurists call the singularity. A benevolent superintelligence might analyze the human genetic code at great speed and unlock the secret to eternal youth. At the very least, it might know how to fix your back.

But what if it wasn't so benevolent? Nick Bostrom, a philosopher who directs the Future of Humanity Institute at the University of Oxford, describes the following scenario in his book Superintelligence, which has prompted a great deal of debate about the future of artificial intelligence. Imagine a machine that we might call a "paper-clip maximizer"-that is, a machine programmed to make as many paper clips as possible. Now imagine that this machine somehow became incredibly intelligent. Given its goals, it might then decide to create new, more efficient paper-clip-manufacturing machines-until, King Midas style, it had converted essentially everything to paper clips.

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No worries, you might say: you could just program it to make exactly a million paper clips and halt. But what if it makes the paper clips and then decides to check its work? Has it counted correctly? It needs to become smarter to be sure. The superintelligent machine manufactures some as-yet-uninvented raw-computing material (call it "computronium") and uses that to check each doubt. But each new doubt yields further digital doubts, and so on, until the entire earth is converted to computronium. Except for the million paper clips.

Things Reviewed

"Superintelligence: Paths, Dangers, Strategies" By Nick Bostrom
Oxford University Press, 2014

Bostrom does not believe that the paper-clip maximizer will come to be, exactly; it's a thought experiment, one designed to show how even careful system design can fail to restrain extreme machine intelligence. But he does believe that superintelligence could emerge, and while it could be great, he thinks it could also decide it doesn't need humans around. Or do any number of other things that destroy the world. The title of chapter 8 is: "Is the default outcome doom?"

If this sounds absurd to you, you're not alone. Critics such as the robotics pioneer Rodney Brooks say that people who fear a runaway AI misunderstand what computers are doing when we say they're thinking or getting smart. From this perspective, the putative superintelligence Bostrom describes is far in the future and perhaps impossible.

Yet a lot of smart, thoughtful people agree with Bostrom and are worried now. Why?
Volition
The question "Can a machine think?" has shadowed computer science from its beginnings. Alan Turing proposed in 1950 that a machine could be taught like a child; John McCarthy, inventor of the programming language LISP, coined the term "artificial intelligence" in 1955. As AI researchers in the 1960s and 1970s began to use computers to recognize images, translate between languages, and understand instructions in normal language and not just code, the idea that computers would eventually develop the ability to speak and think-and thus to do evil-bubbled into mainstream culture. Even beyond the oft-referenced HAL from 2001: A Space Odyssey, the 1970 movie Colossus: The Forbin Project featured a large blinking mainframe computer that brings the world to the brink of nuclear destruction; a similar theme was explored 13 years later in WarGames. The androids of 1973's Westworld went crazy and started killing.

Extreme AI predictions are "comparable to seeing more efficient internal combustion engines... and jumping to the conclusion that the warp drives are just around the corner," Rodney Brooks writes.

When AI research fell far short of its lofty goals, funding dried up to a trickle, beginning long "AI winters." Even so, the torch of the intelligent machine was carried forth in the 1980s and '90s by sci-fi authors like Vernor Vinge, who popularized the concept of the singularity; researchers like the roboticist Hans Moravec, an expert in computer vision; and the engineer/entrepreneur Ray Kurzweil, author of the 1999 book The Age of Spiritual Machines. Whereas Turing had posited a humanlike intelligence, Vinge, Moravec, and Kurzweil were thinking bigger: when a computer became capable of independently devising ways to achieve goals, it would very likely be capable of introspection-and thus able to modify its software and make itself more intelligent. In short order, such a computer would be able to design its own hardware.

As Kurzweil described it, this would begin a beautiful new era. Such machines would have the insight and patience (measured in picoseconds) to solve the outstanding problems of nanotechnology and spaceflight; they would improve the human condition and let us upload our consciousness into an immortal digital form. Intelligence would spread throughout the cosmos.

You can also find the exact opposite of such sunny optimism. Stephen Hawking has warned that because people would be unable to compete with an advanced AI, it "could spell the end of the human race." Upon reading Superintelligence, the entrepreneur Elon Musk tweeted: "Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable." Musk then followed with a $10 million grant to the Future of Life Institute. Not to be confused with Bostrom's center, this is an organization that says it is "working to mitigate existential risks facing humanity," the ones that could arise "from the development of human-level artificial intelligence."

No one is suggesting that anything like superintelligence exists now. In fact, we still have nothing approaching a general-purpose artificial intelligence or even a clear path to how it could be achieved. Recent advances in AI, from automated assistants such as Apple's Siri to Google's driverless cars, also reveal the technology's severe limitations; both can be thrown off by situations that they haven't encountered before. Artificial neural networks can learn for themselves to recognize cats in photos. But they must be shown hundreds of thousands of examples and still end up much less accurate at spotting cats than a child.

This is where skeptics such as Brooks, a founder of iRobot and Rethink Robotics, come in. Even if it's impressive-relative to what earlier computers could manage-for a computer to recognize a picture of a cat, the machine has no volition, no sense of what cat-ness is or what else is happening in the picture, and none of the countless other insights that humans have. In this view, AI could possibly lead to intelligent machines, but it would take much more work than people like Bostrom imagine. And even if it could happen, intelligence will not necessarily lead to sentience. Extrapolating from the state of AI today to suggest that superintelligence is looming is "comparable to seeing more efficient internal combustion engines appearing and jumping to the conclusion that warp drives are just around the corner," Brooks wrote recently on Edge.org. "Malevolent AI" is nothing to worry about, he says, for a few hundred years at least.
Insurance policy
Even if the odds of a superintelligence arising are very long, perhaps it's irresponsible to take the chance. One person who shares Bostrom's concerns is Stuart J. Russell, a professor of computer science at the University of California, Berkeley. Russell is the author, with Peter Norvig (a peer of Kurzweil's at Google), of Artificial Intelligence: A Modern Approach, which has been the standard AI textbook for two decades.

"There are a lot of supposedly smart public intellectuals who just haven't a clue," Russell told me. He pointed out that AI has advanced tremendously in the last decade, and that while the public might understand progress in terms of Moore's Law (faster computers are doing more), in fact recent AI work has been fundamental, with techniques like deep learning laying the groundwork for computers that can automatically increase their understanding of the world around them.

Bostrom's book proposes ways to align computers with human needs. We're basically telling a god how we'd like to be treated.

Because Google, Facebook, and other companies are actively looking to create an intelligent, "learning" machine, he reasons, "I would say that one of the things we ought not to do is to press full steam ahead on building superintelligence without giving thought to the potential risks. It just seems a bit daft." Russell made an analogy: "It's like fusion research. If you ask a fusion researcher what they do, they say they work on containment. If you want unlimited energy you'd better contain the fusion reaction." Similarly, he says, if you want unlimited intelligence, you'd better figure out how to align computers with human needs.

Bostrom's book is a research proposal for doing so. A superintelligence would be godlike, but would it be animated by wrath or by love? It's up to us (that is, the engineers). Like any parent, we must give our child a set of values. And not just any values, but those that are in the best interest of humanity. We're basically telling a god how we'd like to be treated. How to proceed?

Bostrom draws heavily on an idea from a thinker named Eliezer Yudkowsky, who talks about "coherent extrapolated volition"-the consensus-derived "best self" of all people. AI would, we hope, wish to give us rich, happy, fulfilling lives: fix our sore backs and show us how to get to Mars. And since humans will never fully agree on anything, we'll sometimes need it to decide for us-to make the best decisions for humanity as a whole. How, then, do we program those values into our (potential) superintelligences? What sort of mathematics can define them? These are the problems, Bostrom believes, that researchers should be solving now. Bostrom says it is "the essential task of our age."

For the civilian, there's no reason to lose sleep over scary robots. We have no technology that is remotely close to superintelligence. Then again, many of the largest corporations in the world are deeply invested in making their computers more intelligent; a true AI would give any one of these companies an unbelievable advantage. They also should be attuned to its potential downsides and figuring out how to avoid them.

This somewhat more nuanced suggestion-without any claims of a looming AI-mageddon-is the basis of an open letter on the website of the Future of Life Institute, the group that got Musk's donation. Rather than warning of existential disaster, the letter calls for more research into reaping the benefits of AI "while avoiding potential pitfalls." This letter is signed not just by AI outsiders such as Hawking, Musk, and Bostrom but also by prominent computer scientists (including Demis Hassabis, a top AI researcher). You can see where they're coming from. After all, if they develop an artificial intelligence that doesn't share the best human values, it will mean they weren't smart enough to control their own creations.
Paul Ford, a freelance writer in New York, wrote about Bitcoin in March/April 2014.

Credit

Illustration by Jacob Escobedo

Paul Ford is a writer and computer programmer who lives in Brooklyn. He is writing a book of essays about Web pages.

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Are We Smart Enough to Control Artificial Intelligence?

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Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. "Don't laugh at me," he said, "but I was counting on the singularity."

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Given the money at stake, it's no surprise that some of the most innovative Internet of Things (IoT) developments target industry. The promise of big returns, huge cost savings, and longer life cycles gives companies reason to deploy next-generation sensors everywhere from ocean platforms to factory floors.

The investment community has taken note, as is made plain by transaction data. Industrial sector IoT deals are grabbing a growing slice of the investment pie, accounting for nearly 40% of all IoT transactions in 2016 so far, according to data from CB Insights.

Industrial IoT is already changing big business: Here are four industries that are beginning to see its effects.

The Thinking Factory

The arrival of smart sensors and increased connectivity promises huge advances in production efficiency, machinery maintenance, and worker safety for the manufacturing sector. A McKinsey Institute study suggests the economic impact of IIoT on factories could be as vast as $3.7 trillion dollars per year by 2025. And it's a change that is already happening-more than a third of US manufacturers are deploying smart sensors in their current operations according to PWC. Early adoption has enabled deeper automation, like the autonomous factory trolleys built by GE-backed start-up Clearpath Robotics.

As data availability increases, predictive algorithms will be able to squeeze even more efficiency out of the production value chain. And while increased automation may eliminate some legacy manufacturing jobs, the growing need for data analysis plus the introduction of emerging technologies like augmented reality is bringing new hybrid roles into the spotlight.

Driving Miss Daisy Chain

Automated machines aren't restricted to the factory floor, of course. For one, the roll out of Tesla's autopilot feature has sparked a broader debate on automation safety and AI ethics. But as we are slowly chauffeured into a driver-free future, it's the industrial world that moves quickest.

Uber's recently acquired autonomous freight truck start-up just completed the first driverless beer run in history. Competitor Peloton Technologies is wringing out even more efficiencies with its inter-truck IIoT platooning system that cuts wind drag on gusty highways.

Automation is hitting the high seas too, with early tests of autonomous cargo ships being championed by Rolls Royce. For consumers and businesses alike, more automation in ocean logistics signals lower costs and smoother integration.

Connected Medicine

Fitness trackers have proven an early IIoT success story, winning over consumers with gamified stats and even inspiring discounts on life insurance premiums. This is just the start of things to come, with emerging tools like connected insulin pumps and mobile DNA sequencers set to completely change how we manage our health.

IIoT solutions will enable better analytics across the healthcare value chain-from research labs to hospitals and patients' wrists-reducing waste, minimizing errors, and vastly increasing our working data. Add in artificial intelligence tools like IBM's Watson, which has shown promise diagnosing cancer cases, and we will see monumental improvements in both health outcomes and predictive powers.

The challenge will be ensuring patient privacy, proprietary standards, IT security, and cross-border regulations without slowing the implementation of life-saving technology.

Building a Smarter Grid

Finally, the energy sector represents another huge center of activity for IIoT innovation. With billions of dollars routinely dedicated to individual mega-projects, the use of next-generation diagnostic and predictive sensors makes for a good investment.

Building IIoT into existing infrastructure isn't easy, of course. The mix of vendors and asset types across power projects can pose serious challenges. The remote locations of solar and wind farms can make connectivity difficult too-though a custom IoT network is being tested to address this.

The rewards for IIoT innovators will be sweet. By GE estimates, a 1% drop in energy exploration and development CAPEX costs would translate into $90 billion dollars of savings over 15 years. These savings are not far-fetched: take a smart offshore wind turbine. Instead of relying on a costly schedule of preventative manual inspections, IIoT data would simply signal when maintenance or attention is needed.

All in all, data from industrial machines is growing twice as fast as any other sector: In five years, approximately 50 billion machines will be connected to the industrial internet. In the midst of such a rapid evolution and explosion of information, these examples can provide only a snapshot into IIoT's transformative power.

This month, GE will present the whole picture. Minds + Machines 2016 -the fifth annual GE event dedicated to industrial internet software, innovation, and outcomes-will bring together experts to discuss the present and future potential for technology to revolutionize industry.

Learn more about how Industrial IoT is driving global progress.

This article was produced on behalf of GE by Quartz creative services and not by the Quartz editorial staff.

Read full story

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It's no surprise that some of the most innovative Internet of Things (IoT) developments target industry. The promise of big returns, huge cost savings, and longer life cycles gives companies reason to deploy next-generation sensors everywhere from ocean platforms to factory floors. Hear from four industries that are already beginning to see the effects of...

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In 1941, science-fiction writer Isaac Asimov stated "The Three Laws of Robotics," in his short story "Runaround."

Law One: A robot may not injure a human being or, through inaction, allow a human being to come to harm.

Law Two: A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

Law Three: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

These laws come from the world of science fiction, but the real world is catching up. This month, a law firm gave Pittsburgh's Carnegie Mellon University $10 million to explore the ethics of artificial intelligence - or AI. This comes after industry leaders recently joined together to form the group called the Partnership on Artificial Intelligence to Benefit People and Society.

Peter Kalis is chairman of the law firm, K&L Gates. He says technology is dashing ahead of the law, leading to questions that were never taken seriously before. Such as what happens when you make robots that are smart, independent thinkers - and then try to limit their autonomy?

"One expert said we'll be at a fulcrum point when you give an instruction to your robot to go to work in the morning and it turns around and says, 'I'd rather go to the beach.' Or, more perilously, if we were to launch a robot on the battlefield and all of the sudden it took a more partial liking to the enemy than it did to its human sponsor," Kalis says.

He says that one day we'll want laws to keep our free-thinking robots from running wild - but we'll also have to weigh such laws against the U.S. Constitution.

"It says that every person should benefit from equal protection under the law. Well, I don't think anyone contemplated that person would include an artificially intelligent robot," Kalis says. "Yet I hear people seriously maintaining that artificially intelligent robots ought to replace judges. When we get to that point, it's a matter of profound constitutional and social consequence for any country, any nation which prizes the rule of law."

With the law firm's gift, Carnegie Mellon President Subra Suresh says the university will be able to dig into issues now emerging within automated industries.

"Take driverless cars," he says. "If there's an accident involving a driverless car, what policies do we have in place? What kind of insurance coverage do they have? And who needs to take insurance?"

As it is, people can take a ride in a driverless car in Pittsburgh where Uber uses the city as a testing ground for the company's driverless cars. Suresh says he's familiar with the program, but still has questions as a passenger.

"The mayor of Pittsburgh and I took the inaugural ride about a couple of months ago," Suresh tells NPR's Audie Cornish. "We were talking about this, you know, if somebody came and hit us now, are we liable or is somebody else liable? The clarification is not there yet."

The issues go beyond self-driving cars and renegade robots. Inside the next generation of smartphones, in those chips embedded in home appliances, and the ever-expanding collection of personal data being stored in the "cloud," questions about what's right and wrong are open to study.

So are Asimov's three laws of robotics all there is to govern AI right now - and is it necessary to have a moral guideline that everyone can understand?

"I think putting all three laws into one: Do no harm, could be the very first one," Suresh says.

He says people today are at "an interesting point in the intersection of humans and technology" - one they don't have any prior experience with.

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Scholars Delve Deeper Into The Ethics Of Artificial Intelligence

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In 1941, science-fiction writer Isaac Asimov stated "The Three Laws of Robotics," in his short story "Runaround." Law One: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Law Two: A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

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Zeynep Tufekci: Machine intelligence makes human morals more important

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Machine intelligence is here, and we're already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that don't fit human error patterns -- and in ways we won't expect or be prepared for.

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2,793 votes and 2,983 comments so far on Reddit

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DeepMind - What if solving one problem could unlock solutions to thousands more?

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Artificial intelligence could be one of humanity's most useful inventions. We research and build safe AI systems that learn how to solve problems and advance scientific discovery for all.

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Allen Institute for Artificial Intelligence

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AI2, founded by Paul Allen and led by Dr. Oren Etzioni, conducts high-impact research and engineering to tackle key problems in artificial intelligence.

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One of the major promises of AI is freeing people from mindless tasks, so they can do more meaningful work. When we're not writing posts about AI, we're building Journal to help you see and search your private work information, all in one place. Get Early Access

Shivon Zilis and James Cham, who invest in machine learning-related companies for Bloomberg Beta, recently created a machine intelligence market landscape.

Below, you can find links to the 317+ companies in the landscape (and a few more), and play around with some apps that are applying machine learning in interesting ways.

Enterprise Intelligence

👂Audio

🗄Internal Data

📈Market

Enterprise Functions

☎️Customer Support

💸Sales

📣Marketing

🚨Security

🤗Recruiting

Autonomous Systems

🚘Ground Navigation

✈️Aerial

🏭Industrial

Agents

🕵️Personal

📅Professional

Industries

🐮Agriculture

🎓Education

🏦Investment

🏛Legal

📦Transportation/Logistics

🛢Materials

👜Retail Finance

Healthcare

🤕Patient
DreamUp Vision
🖼Image

🔬Biological

Technology Stack

💬Agent & Conversational Interfaces

💯Data Science

🤖Machine Learning

💬Natural Language

🖥Development

📊Data Capture

📚Open Source Libraries

🛠Hardware

📊Research

People to Know in Machine Learning and AI

Andrew Ng Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty.

Harry Shum EVP, Microsoft AI and Research.

Geoffrey Hinton The godfather of deep learning.

Samiur Rahman CEO of Journal. Former Data Engineering Lead at Mattermark.

Jeff Dean Google Senior Fellow at Google, Inc. Co-founder and leader of Google's deep learning research and engineering team.

Eric Horvitz Technical Fellow at Microsoft Research

Denny Britz Deep Learning at Google Brain.

Tom Mitchell Computer scientist and E. Fredkin University Professor at the Carnegie Mellon University.

Kirk Borne The Principal Data Scientist at Booz Allen, PhD Astrophysicist.

Fei-Fei Li Professor of Computer Science, Stanford University, Director of Stanford AI Lab.

David Silver Royal Society University Research Fellow.

Roberto Cipolla Department of Engineering, University of Cambridge.

Gabe Brostow Associate Professor in Computer Science at London's Global University.

Pieter Abbeel Associate Professor, UC Berkeley, EECS. Berkeley Artificial Intelligence Research (BAIR) laboratory. UC Berkeley Center for Human Compatible AI. Co-Founder Gradescope.

Josh WillsSlackData Engineering and Apache Crunch committer.

Noah Weiss Head of Search, Learning, & Intelligence at Slack in NYC. Former SVP of Product at foursquare + Google PM on structured search.

Michael E. DriscollFounder, CEO Metamarkets. Investor at Data Collective

Ben LoricaChief Data Scientist of O'Reilly Media, and Program Director of O'Reilly Strata & O'ReillyAI conferences. Ben hosts the O'Reilly Data Show Podcast too.

Chris Nicholson Co-founder Deeplearning4j & Skymind. Previous to that, Chris worked at The New York Times.

Dror Berman Founder, Innovation Endeavors.

Dylan Tweney Founder of @TweneyMedia, former EIC @venturebeat, ex-@WIRED, publisher of @tinywords.

Gary Kazantsev R&D Machine Learning at Bloomberg LP.

Gideon Mann Head of Data Science / CTO Office at Bloomberg LP.

Gordon Ritter Cloud investor at Emergence Capital, cloud entrepreneur.

Jack Clark Strategy and Communications Director OpenAI. Past: @business World's Only Neural Net Reporter. @theregister Distributed Systems Reporter.

Federico Pascual COO & Co-Founder, MonkeyLearn.

Matt TurckVC at FirstMark Capital and the organizer of Data Driven NYC and Hardwired NYC.

Nick Adams Data Scientist, Berkeley Institute for Data Science.

Roger Magoulas Research Director, O'Reilly Media.

Sean Gourley Former CEO, Quid.

Shruti Gandhi Array.VC, previously at True & Samsung Ventures.

Steve Jurvetson Partner at Draper Fisher Jurvetson.

Zavain Dar VC Lux Capital, Lecturer Stanford University, Moneyball Philadelphia 76ers.

Yann Lecun Director of AI Research, Facebook. Founding Director of the NYU Center for Data Science

Sarah Catanzaro Investor @canvasvc; former Data Lead @Mattermark.

Paul Hsiao AI + Marketplace Investor @CanvasVC.

Ryan Dawidjan.Product Team, Clarifai.

Lucy Wang. Data Scientist, VC Greycroft Partners, MS NYU Center for Data Science.

Roger Chen Program Chair, O'Reilly Artificial Intelligence Conference.

Jason Yosinski Machine Learning Scientist at Geometric Intelligence.

Soumith Chintala Artificial Intelligence Research Engineer at Facebook.

Suchi Saria Assistant Professor at Johns Hopkins University. Expertise in machine learning, streaming data and health informatics.

Peter Norvig Research Director at Google.

Zoubin Ghahramani Researcher and Professor of Information Engineering at the University of Cambridge.

Daphne Koller Professor of Computer Science at Stanford, Co-Founder of Coursera.

Amir Banifatemi Leads and provides thought leadership for the Global IBM Watson Artificial Intelligence Xprize prize.

Frank Chen Partner at Andreessen Horowitz.

Lili Cheng General Manager, Fuse Labs, Microsoft Research.

Shane Legg Chief Scientist and Co-Founder, Deepmind.

Mustafa Suleyman Co-founder of Deepmind.

Artificial Intelligence and ML Events

I've written previously about my (lack of) love for networking events.

NYAI has managed to rise above the noise and organize ~1,500 researchers, students, founders, and investors who are genuinely awesome people.

The impacts that machine learning and AI have on our world will not be felt in isolation. These changes will affect all of us. NYAI's monthly events are the best way, that I've found so far, to learn about, collaborate on and network over emerging trends in artificial intelligence.

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The Non-Technical Guide to Machine Learning & Artificial Intelligence

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One of the major promises of AI is freeing people from mindless tasks, so they can do more meaningful work. When we're not writing posts about AI, we're building Journal to help you see and search your private work information, all in one place.

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Stephen Hawking: AI will be 'either best or worst thing' for humanity

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Professor praises creation of Cambridge University institute to study future of artificial intelligence

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Open Letter on Artificial Intelligence - Wikipedia

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In January 2015, Stephen Hawking, Elon Musk, and dozens of artificial intelligence experts signed an open letter on artificial intelligence calling for research on the societal impacts of AI.

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The issue of robotic morality has become a serious question for researchers working on autonomous vehicles who must, in essence, program moral decisions into a machine. Published On

People say that one day, perhaps in the not-so-distant future, they'd like to be passengers in self-driving cars that are mindful machines doing their best for the common good. Merge politely. Watch for pedestrians in the crosswalk. Keep a safe space.

A new research study, however, indicates that what people really want to ride in is an autonomous vehicle that puts its passengers first. If its machine brain has to choose between slamming into a wall or running someone over, well, sorry, pedestrian.

In this week's Science magazine, a group of computer scientists and psychologists explain how they conducted six online surveys of United States residents last year between June and November that asked people how they believed autonomous vehicles should behave. The researchers found that respondents generally thought self-driving cars should be programmed to make decisions for the greatest good.

Sort of. Through a series of quizzes that present unpalatable options that amount to saving or sacrificing yourself - and the lives of fellow passengers who may be family members - to spare others, the researchers, not surprisingly, found that people would rather stay alive.

Image

A pedestrian crosses in front of a vehicle as part of a demonstration last July at Mcity, a test site for driverless and connected vehicles, on the University of Michigan campus in Ann Arbor, Mich. Credit Paul Sancya/Associated Press

This particular dilemma of robotic morality has long been chewed on in science fiction books and movies. But in recent years it has become a serious question for researchers working on autonomous vehicles who must, in essence, program moral decisions into a machine.

As autonomous vehicles edge closer to reality, it has also become a philosophical question with business implications. Should manufacturers create vehicles with various degrees of morality programmed into them, depending on what a consumer wants? Should the government mandate that all self-driving cars share the same value of protecting the greatest good, even if that's not so good for a car's passengers?

And what exactly is the greatest good?

"Is it acceptable for an A.V. (autonomous vehicle) to avoid a motorcycle by swerving into a wall, considering that the probability of survival is greater for the passenger of the A.V., than for the rider of the motorcycle? Should A.V.s take the ages of the passengers and pedestrians into account?" wrote Jean-François Bonnefon, of the Toulouse School of Economics in France; Azim Shariff, of the University of Oregon; and Iyad Rahwan, of the Media Laboratory at the Massachusetts Institute of Technology.

At the heart of this discussion is the "trolley problem." First introduced in 1967 by Philippa Foot, a British philosopher, the trolley problem is a simple if unpleasant ethical thought puzzle.

Imagine a runaway trolley is barreling toward five workmen on the tracks. Their lives can be saved by a lever that would switch the trolley to another line. But there is one worker on those tracks as well. What is the correct thing to do?

The research published in Science tries to quantify that philosophical quandary. "One missing component has been the empirical component: What do people actually want?" said Dr. Rahwan, who is a computational social scientist.

Each survey presented different situations, like varying the number of pedestrian lives that could be saved or adding a family member to the problem. In one survey they discovered that participants were generally reluctant to accept government regulation of artificial intelligence algorithms, even if that would be one way to solve or at least settle on an answer to this trolley problem.

The number of respondents on the six surveys varied from 182 to 451.

The new research could take autonomous vehicle manufacturers down a philosophical and legal rabbit hole. And since the autonomous vehicle concept is so new, it could take years to find answers. For example, the authors write, "If a manufacturer offers different versions of its moral algorithm, and a buyer knowingly chose one of them, is the buyer to blame for the harmful consequences of the algorithm's decisions?"

The United States military is also trying to come to terms with the fact that advanced technology is on the cusp of making it possible for machines like armed drones to make killing decisions. In 2012, the Pentagon released a directive that tried to draw a line between semiautonomous and completely autonomous weapons. They are not outlawed, but they must be designed to allow "appropriate levels" of human judgment over their use.

In a companion article in Science magazine, the Harvard psychologist Joshua D. Greene suggested that the thorniest challenges in machine decision-making may be "more philosophical than technical. Before we can put our values into machines, we have to figure out how to make our values clear and consistent."

Some researchers argue that teaching machines ethics may not be the right approach.

"If you assume that the purpose of A.I. is to replace people, then you will need to teach the car ethics," said Amitai Etzioni, a sociologist at George Washington University. "It should rather be a partnership between the human and the tool, and the person should be the one who provides ethical guidance."

A version of this article appears in print on , on Page B2 of the New York edition with the headline: Should Your Driverless Car Hit a Pedestrian if It Would Save Your Life?. Order Reprints | Today's Paper | Subscribe

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Should Your Driverless Car Hit a Pedestrian to Save Your Life?

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People say that one day, perhaps in the not-so-distant future, they'd like to be passengers in self-driving cars that are mindful machines doing their best for the common good. Merge politely. Watch for pedestrians in the crosswalk. Keep a safe space.