Daily Archives: June 22, 2017

People often think of artificial intelligence as something akin to the being from the film "I, Robot" depicted here, but experts are divided on what the future actually holds.

It's a Saturday morning in June at the Royal Society in London. Computer scientists, public figures and reporters have gathered to witness or take part in a decades-old challenge. Some of the participants are flesh and blood; others are silicon and binary. Thirty human judges sit down at computer terminals, and begin chatting. The goal? To determine whether they're talking to a computer program or a real person.

The event, organized by the University of Reading, was a rendition of the so-called Turing test, developed 65 years ago by British mathematician and cryptographer Alan Turing as a way to assess whether a machine is capable of intelligent behavior indistinguishable from that of a human. The recently released film "The Imitation Game," about Turing's efforts to crack the German Enigma code during World War II, is a reference to the scientist's own name for his test.

In the London competition, one computerized conversation program, or chatbot, with the personality of a 13-year-old Ukrainian boy named Eugene Goostman, rose above and beyond the other contestants. It fooled 33 percent of the judges into thinking it was a human being. At the time, contest organizers and the media hailed the performance as an historic achievement, saying the chatbot was the first machine to "pass" the Turing test. [Infographic: History of Artificial Intelligence]

Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri.

When people think of artificial intelligence (AI) the study of the design of intelligent systems and machines talking computers like Eugene Goostman often come to mind. But most AI researchers are focused less on producing clever conversationalists and more on developing intelligent systems that make people's lives easier from software that can recognize objects and animals, to digital assistants that cater to, and even anticipate, their owners' needs and desires.

But several prominent thinkers, including the famed physicist Stephen Hawking and billionaire entrepreneur Elon Musk, warn that the development of AI should be cause for concern.

Thinking machines

The notion of intelligent automata, as friend or foe,dates back to ancient times.

"The idea of intelligence existing in some form that's not human seems to have a deep hold in the human psyche," said Don Perlis, a computer scientist who studies artificial intelligence at the University of Maryland, College Park.

Reports of people worshipping mythological human likenesses and building humanoid automatons date back to the days of ancient Greece and Egypt, Perlis told Live Science. AI has also featured prominently in pop culture, from the sentient computer HAL 9000 in Stanley Kubrick's "2001: A Space Odyssey" to Arnold Schwarzenegger's robot character in "The Terminator" films. [A Brief History of Artificial Intelligence]

Since the field of AI was officially founded in the mid-1950s, people have been predicting the rise of conscious machines, Perlis said. Inventor and futurist Ray Kurzweil, recently hired to be a director of engineering at Google, refers to a point in time known as "the singularity," when machine intelligence exceeds human intelligence. Based on the exponential growth of technology according to Moore's Law (which states that computing processing power doubles approximately every two years), Kurzweil has predicted the singularity will occur by 2045.

But cycles of hype and disappointment the so-called "winters of AI" have characterized the history of artificial intelligence, as grandiose predictions failed to come to fruition. The University of Reading Turing test is just the latest example: Many scientists dismissed the Eugene Goostman performance as a parlor trick; they said the chatbot had gamed the system by assuming the persona of a teenager who spoke English as a foreign language. (In fact, many researchers now believe it's time to develop an updated Turing test.)

Nevertheless, a number of prominent science and technology experts have expressed worry that humanity is not doing enough to prepare for the rise of artificial general intelligence, if and when it does occur. Earlier this week, Hawking issued a dire warning about the threat of AI.

"The development of fullartificial intelligencecould spell the end of the human race," Hawking told the BBC, in response to a question about his new voice recognition system, which uses artificial intelligence to predict intended words. (Hawking has a form of the neurological disease amyotrophic lateral sclerosis, ALS or Lou Gehrig's disease, and communicates using specialized speech software.)

And Hawking isn't alone. Musk told an audience at MIT that AI is humanity's "biggest existential threat." He also once tweeted, "We need to be super careful with AI. Potentially more dangerous than nukes."

In March, Musk, Facebook CEO Mark Zuckerberg and actor Ashton Kutcher jointly invested $40 million in the company Vicarious FPC, which aims to create a working artificial brain. At the time, Musktold CNBCthat he'd like to "keep an eye on what's going on with artificial intelligence," adding, "I think there's potentially a dangerous outcome there."

Fears of AI turning into sinister killing machines, like Arnold Schwarzenegger's character from the "Terminator" films, are nothing new.

But despite the fears of high-profile technology leaders, the rise of conscious machines known as "strong AI" or "general artificial intelligence" is likely a long way off, many researchers argue.

"I don't see any reason to think that as machines become more intelligent which is not going to happen tomorrow they would want to destroy us or do harm," said Charlie Ortiz, head of AI at the Burlington, Massachusetts-based software company Nuance Communications."Lots of work needs to be done before computers are anywhere near that level," he said.

Machines with benefits

Artificial intelligence is a broad and active area of research, but it's no longer the sole province of academics; increasingly, companies are incorporating AI into their products.

And there's one name that keeps cropping up in the field: Google. From smartphone assistants to driverless cars, the Bay Area-based tech giant is gearing up to be a major player in the future of artificial intelligence.

Google has been a pioneer in the use of machine learning computer systems that can learn from data, as opposed to blindly following instructions. In particular, the company uses a set of machine-learning algorithms, collectively referred to as "deep learning," that allow a computer to do things such as recognize patterns from massive amounts of data.

For example, in June 2012, Google created a neural network of 16,000 computers that trained itself to recognize acatby looking at millions of cat images from YouTube videos, The New York Timesreported. (After all, what could be more uniquely human than watching cat videos?)

The project, called Google Brain, was led by Andrew Ng, an artificial intelligence researcher at Stanford University who is now the chief scientist for the Chinese search engine Baidu, which is sometimes referred to as "China's Google."

Today, deep learning is a part of many products at Google and at Baidu, including speech recognition, Web search and advertising, Ng told Live Science in an email.

Current computers can already complete many tasks typically performed by humans. But possessing humanlike intelligence remains a long way off, Ng said. "I think we're still very far from the singularity. This isn't a subject that most AI researchers are working toward."

Gary Marcus, a cognitive psychologist at NYU who has written extensively about AI, agreed. "I don't think we're anywhere near human intelligence [for machines]," Marcus told Live Science. In terms of simulating human thinking, "we are still in the piecemeal era."

Instead, companies like Google focus on making technology more helpful and intuitive. And nowhere is this more evident than in the smartphone market.

Artificial intelligence in your pocket

In the 2013 movie "Her," actor Joaquin Phoenix's character falls in love with his smartphone operating system, "Samantha," a computer-based personal assistant who becomes sentient. The film is obviously a product of Hollywood, but experts say that the movie gets at least one thing right: Technology will take on increasingly personal roles in people's daily lives, and will learn human habits and predict people's needs.

Anyone with an iPhone is probably familiar with Apple's digital assistant Siri, first introduced as a feature on the iPhone 4S in October 2011. Siri can answer simple questions, conduct Web searches and perform other basic functions. Microsoft's equivalent is Cortana, a digital assistant available on Windows phones. And Google has the Google app, available for Android phones or iPhones, which bills itself as providing "the information you want, when you need it."

For example, Google Now can show traffic information during your daily commute, or give you shopping list reminders while you're at the store. You can ask the app questions, such as "should I wear a sweater tomorrow?" and it will give you the weather forecast. And, perhaps a bit creepily, you can ask it to "show me all my photos of dogs" (or "cats," "sunsets" or a even a person's name), and the app will find photos that fit that description, even if you haven't labeled them as such.

Given how much personal data from users Google stores in the form of emails, search histories and cloud storage, the company's deep investments in artificial intelligence may seem disconcerting. For example, AI could make it easier for the company to deliver targeted advertising, which some users already find unpalatable. And AI-based image recognition software could make it harder for users to maintain anonymity online.

But the company, whose motto is "Don't be evil," claims it can address potential concerns about its work in AI by conducting research in the open and collaborating with other institutions, company spokesman Jason Freidenfelds told Live Science. In terms of privacy concerns, specifically, he said, "Google goes above and beyond to make sure your information is safe and secure," calling data security a "top priority."

While a phone that can learn your commute, answer your questions or recognize what a dog looks like may seem sophisticated, it still pales in comparison with a human being. In some areas, AI is no more advanced than a toddler. Yet, when asked, many AI researchers admit that the day when machines rival human intelligence will ultimately come. The question is, are people ready for it?

In the film "Transcendence," Johnny Depp's character uploads his mind to a computer, but it doesn't end well.

Taking AI seriously

In the 2014 film "Transcendence," actor Johnny Depp's character uploads his mind into a computer, but his hunger for power soon threatens the autonomy of his fellow humans. [Super-Intelligent Machines: 7 Robotic Futures]

Hollywood isn't known for its scientific accuracy, but the film's themes don't fall on deaf ears. In April, when "Trancendence" was released, Hawking and fellow physicist Frank Wilczek, cosmologist Max Tegmark and computer scientist Stuart Russell published an op-ed in The Huffington Post warning of the dangers of AI.

"It's tempting to dismiss the notion of highly intelligent machines as mere science fiction," Hawking and others wrote in the article."But this would be a mistake, and potentially our worst mistake ever."

Undoubtedly, AI could have many benefits, such as helping to aid the eradication of war, disease and poverty, the scientists wrote. Creating intelligent machines would be one of the biggest achievements in human history, they wrote, but it "might also be [the] last." Considering that the singularity may be the best or worst thing to happen to humanity, not enough research is being devoted to understanding its impacts, they said.

As the scientists wrote, "Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all."

Follow Tanya Lewis on Twitter. Follow us @livescience, Facebook& Google+. Original article on Live Science.

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Artificial Intelligence: Friendly or Frightening?

Much of the recent progress in AI research has been courtesy of an approach known as deep learning.

When most people think of artificial intelligence (AI) they think of HAL 9000 from "2001: A Space Odyssey," Data from "Star Trek," or more recently, the android Ava from "Ex Machina." But to a computer scientist that isn't what AI necessarily is, and the question "what is AI?" can be a complicated one.

One of the standard textbooks in the field, by University of California computer scientists Stuart Russell and Google's director of research, Peter Norvig, puts artificial intelligence in to four broad categories:

The differences between them can be subtle, notes Ernest Davis, a professor of computer science at New York University. AlphaGo, the computer program that beat a world champion at Go, acts rationally when it plays the game (it plays to win). But it doesn't necessarily think the way a human being does, though it engages in some of the same pattern-recognition tasks. Similarly, a machine that acts like a human doesn't necessarily bear much resemblance to people in the way it processes information.

Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri.

Even IBM's Watson, which acted somewhat like a human when playing Jeopardy, wasn't using anything like the rational processes humans use.

Davis says he uses another definition, centered on what one wants a computer to do. "There are a number of cognitive tasks that people do easily often, indeed, with no conscious thought at all but that are extremely hard to program on computers. Archetypal examples are vision and natural language understanding. Artificial intelligence, as I define it, is the study of getting computers to carry out these tasks," he said.

Computer vision has made a lot of strides in the past decade cameras can now recognize faces in the frame and tell the user where they are. However, computers are still not that good at actually recognizing faces, and the way they do it is different from the way people do. A Google image search, for instance, just looks for images in which the pattern of pixels matches the reference image. More sophisticated face recognition systems look at the dimensions of the face to match them with images that might not be simple face-on photos. Humans process the information rather differently, and exactly how that process works is still something of an open question for neuroscientists and cognitive scientists.

Other tasks, though, are proving tougher. For example, Davis and NYU psychology professor Gary Marcus wrote in the Communications of the Association for Computing Machinery of "common sense" tasks that computers find very difficult. A robot serving drinks, for example, can be programmed to recognize a request for one, and even to manipulate a glass and pour one. But if a fly lands in the glass the computer still has a tough time deciding whether to pour the drink in and serve it (or not).

The issue is that much of "common sense" is very hard to model. Computer scientists have taken several approaches to get around that problem. IBM's Watson, for instance, was able to do so well on Jeopardy! because it had a huge database of knowledge to work with and a few rules to string words together to make questions and answers. Watson, though, would have a difficult time with a simple open-ended conversation.

Beyond tasks, though, is the issue of learning. Machines can learn, said Kathleen McKeown, a professor of computer science at Columbia University. "Machine learning is a kind of AI," she said.

Some machine learning works in a way similar to the way people do it, she noted. Google Translate, for example, uses a large corpus of text in a given language to translate to another language, a statistical process that doesn't involve looking for the "meaning" of words. Humans, she said, do something similar, in that we learn languages by seeing lots of examples.

That said, Google Translate doesn't always get it right, precisely because it doesn't seek meaning and can sometimes be fooled by synonyms or differing connotations.

One area that McKeown said is making rapid strides is summarizing texts; systems to do that are sometimes employed by law firms that have to go through a lot of it.

McKeown also thinks personal assistants is an area likely to move forward quickly. "I would look at the movie 'Her,'" she said. In that 2013 movie starring Joaquin Phoenix, a man falls in love with an operating system that has consciousness.

"I initially didn't want to go see it, I said that's totally ridiculous," McKeown said. "But I actually enjoyed it. People are building these conversational assistants, and trying to see how far can we get."

The upshot is AIs that can handle certain tasks well exist, as do AIs that look almost human because they have a large trove of data to work with. Computer scientists have been less successful coming up with an AI that can think the way we expect a human being to, or to act like a human in more than very limited situations.

"I don't think we're in a state that AI is so good that it will do things we hadn't imagined it was going to do," McKeown said.

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What Is Artificial Intelligence?

Artificial intelligence can predict the behavior of Congress.

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By Matthew HutsonJun. 21, 2017 , 2:30 PM

The health care bill winding its way through the U.S. Senate is just one of thousands of pieces of legislation Congress will consider this year, most doomed to failure. Indeed, only about 4% of these bills become law. So which ones are worth paying attention to? A new artificial intelligence (AI) algorithm could help. Using just the text of a bill plus about a dozen other variables, it can determine the chance that a bill will become law with great precision.

Other algorithms have predicted whether a bill will survive a congressional committee, or whether the Senate or House of Representatives will vote to approve itall with varying degrees of success. But John Nay, a computer scientist and co-founder of Skopos Labs, a Nashville-based AI company focused on studying policymaking, wanted to take things one step further. He wanted to predict whether an introduced bill would make it all the way through both chambersand precisely what its chances were.

Nay started with data on the 103rd Congress (19931995) through the 113th Congress (20132015), downloaded from a legislation-tracking website call GovTrack. This included the full text of the bills, plus a set of variables, including the number of co-sponsors, the month the bill was introduced, and whether the sponsor was in the majority party of their chamber. Using data on Congresses 103 through 106, he trained machine-learning algorithmsprograms that find patterns on their ownto associate bills text and contextual variables with their outcomes. He then predicted how each bill would do in the 107th Congress. Then, he trained his algorithms on Congresses 103 through 107 to predict the 108th Congress, and so on.

Nays most complex machine-learning algorithm combined several parts. The first part analyzed the language in the bill. It interpreted the meaning of words by how they were embedded in surrounding words. For example, it might see the phrase obtain a loan for education and assume loan has something to do with obtain and education. A words meaning was then represented as a string of numbers describing its relation to other words. The algorithm combined these numbers to assign each sentence a meaning. Then, it found links between the meanings of sentences and the success of bills that contained them. Three other algorithms found connections between contextual data and bill success. Finally, an umbrella algorithm used the results from those four algorithms to predict what would happen.

Because bills fail 96% of the time, a simple always fail strategy would almost always be right. But rather than simply predict whether each bill would or would not pass, Nay wanted to assign each a specific probability. If a bill is worth $100 billionor could take months or years to pull togetheryou dont want to ignore its possibility of enactment just because its odds are below 50%. So he scored his method according to the percentages it assigned rather than the number of bills it predicted would succeed. By that measure, his program scored about 65% better than simply guessing that a bill wouldnt pass, Nay reported last month in PLOS ONE.

Nay also looked at which factors were most important in predicting a bills success. Sponsors in the majority and sponsors who served many terms were at an advantage (though each boosted the odds by 1% or less). In terms of language, words like impact and effects increased the chances for climate-related bills in the House, whereas global or warming spelled trouble. In bills related to health care, Medicaid and reinsurance reduced the likelihood of success in both chambers. In bills related to patents, software lowered the odds for bills introduced in the House, and computation had the same effect for Senate bills.

Nay says he is surprised that a bills text alone has predictive power. At first I viewed the process as just very partisan and not as connected to the underlying policy thats contained within the legislation, he says.

Nays use of language analysis is innovative and promising, says John Wilkerson, a political scientist at the University of Washington in Seattle. But he adds that without prior predictions relating certain words to successthe word impact, for examplethe project doesnt do much to illuminate how the minds of Congress members work. We dont really learn anything about process, or strategy, or politics.

But it still seems to be the best method out there. Nays way of looking at bill text is new, says Joshua Tauberer, a software developer at GovTrack with a background in linguistics who is based in Washington, D.C., and who had been using his own machine-learning algorithm to predict bill enactment since 2012. Last year, Nay learned of Tauberers predictions, and the two compared notes. Nays method made better predictions, and Tauberer ditched his own version for Nays.

So how did the new algorithm rank the many (failed) bills to repeal the Affordable Care Act? A simple, base-rate prediction would have put their chances at 4%. But for nearly all of them, Nays program put the odds even lower.

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Artificial intelligence can predict which congressional bills will pass - Science Magazine