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AI gets its groove back

After decades of start-and-stop, artificial intelligence is being advanced by major computing firms from Facebook and Google to IBM.

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Deep learning

Beyond today's big data and massive computational resources, sources cite a third factor pushing AI past an inflection point: improved algorithms, especially the widespread adoption of a decade-old algorithm called "deep learning." Yann LeCun, director of Facebook's AI Group, describes it as a way to more fully automate machine learning by using multiple layers of analysis that can compare their results with other layers.

He explains that previously, anyone designing a machine-learning system had to submit data to it, but not before they hand-crafted software to identify sought-after features in the data and also hand-crafted software to classify the identified features. With deep learning, both of these manual processes are replaced with trainable machine-learning systems.

"The entire system from end to end is now multiple layers that are all trainable," LeCun says.

(LeCun attributes the development of deep learning to a team led by Geoff Hinton, a professor at the University of Toronto who now works part-time for Google; LeCun was, in fact, part of Hinton's deep learning development team. Hinton did not respond to interview requests.)

Even so, "deep learning can only take us so far," counters Gary Marcus, a professor at New York University. "Despite its name it's rather superficial -- it can pick up statistical tendencies and is particularly good for categorization problems, but it's not good at natural language understanding. There needs to be other advances as well so that machines can really understand what we are talking about."

He hopes the field will revisit ideas that were abandoned in the 1960s since, with modern computer power, they now might produce results, such as a machine that would be as good as a four-year-old child at learning language.

In the final analysis, "About half of the progress in the performance of AI has been from improved computing power, and half has been from improvements by programmers. Sometimes, progress is from brute force applied to get a one percent improvement. But the ingenuity of people like Hinton should not be downplayed," says MIRI's Muehlhauser.

The AI rush

If the spectacle of large corporations investing major sums in a technology is evidence that the technology has gone mainstream, future historians may say that AI reached that point in the winter of 2013-2014.

In January, Rob High, vice president and chief technology officer of the Watson Group, announced IBM's plans to invest $1 billion in AI over the next few years. This includes $100 million as venture capital seed money to invest in Watson-based startups.

IBM has made no secret of its embrace of AI, especially after its Watson natural-language AI system (with access to four terabytes of information) famously won the TV quiz show Jeopardy! against two human champions in 2011.

   IBM's Michael Rhodin
Michael Rhodin, the new head of IBM's Watson Group, announcing in January that IBM will invest more than $1 billion to establish a new business unit around its Watson cognitive supercomputer. REUTERS/Brendan McDermid

High explains that Watson involves "a major shift from classical AI, which relied heavily on ontology for evaluating questions or answers. Instead we are aggregating multiple technologies and multiple strategies to disambiguate results and enhance fidelity. My wife calls me to say that she will stop at the store on the way home. That is ambiguous, but I have enough history to know what she is talking about."

The result of these aggregated technologies is that Watson can read natural-language material and derive information from it with success approaching that of a human being, he explains.

IBM is exploring the use of Watson in a number of industries, especially medicine, where it could digest all available clinical literature associated with a case. "Doctors see a demo and walk away giddy about how it affects their ability to make decisions," High says.

As a product, Watson will be based in the cloud, but developers can embed access in their applications, he adds.

Google, meanwhile, also spent this past winter making significant AI-related investments. In March, Google acquired DNN Research, which works in the field of deep neural networks.

In January Google reportedly paid $400 million or more (reports vary) for DeepMind Technologies, a London-based machine learning firm.

Google spokespersons declined to discuss Google's AI-related actions and plans. Facebook's LeCun, however, is familiar with DeepMind. "They had hired some of my students," he recalls. "They had a presentation where they connected their system to an old video game like Space Invaders and had it try to maximize points by trial and error, learning the game from scratch. After a week it was better than a human," he says.

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