Google Hires Brains that Helped Supercharge Machine Learning

Google has hired the man who showed how to make computers learn much like the human brain.
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Geoffrey Hinton (right), one of the machine learning scientists hard at work on The Google Brain. Photo: University of TorontoU of T

Google has hired the man who showed how to make computers learn much like the human brain.

His name is Geoffrey Hinton, and on Tuesday, Google said that it had hired him along with two of his University of Toronto graduate students – Alex Krizhevsky and Ilya Sutskever. Their job: to help Google make sense of the growing mountains of data it is indexing and to improve products that already use machine learning – products such as Android voice search.

Google paid an undisclosed sum to buy Hinton's company, DNNresearch. It's a bit of a best-of-both-worlds deal for the researcher. He gets to stay in Toronto, splitting his time between Google and his teaching duties at the University of Toronto, while Krizhevsky and Sutskever fly south to work at Google's Mountain View, California campus.

Back in the 1980s, Hinton kicked off research into neural networks, a field of machine learning where programmers can build machine learning models that help them to sift through vast quantities of data and put together patterns, much like the human brain.

Once a hot research topic, neural networks had apparently failed to live up to their initial promises until around 2006, when Hinton and his researchers – spurred on by some new kick-ass microprocessors – developed new "deep learning" techniques that fine-tuned the tricky and time consuming process of building neural network models for computer analysis.

"Deep learning, pioneered by Hinton, has revolutionized language understanding and language translation," said Ed Lazowska, a computer science professor at the University of Washington. In an email interview, he said that a pretty spectacular December 2012 live demonstration of instant English-to-Chinese voice recognition and translation by Microsoft Research chief Rick Rashid was "one of many things made possible by Hinton's work."

"Hinton has been working on neural networks for decades, and is one of the most brilliant minds of the field," said Andrew Ng, the Stanford University professor who set up Google's neural network team in 2011. Ng invited Hinton to Google last summer, where the Toronto academic spent a few months as a visiting professor. "I'm thrilled that he'll be continuing this work there, and am sure he'll help drive forward deep learning research at Google," Ng said via email.

Google didn't want to comment, or let Hinton talk to us about his new job, but clearly, it's going to be important to Google's future. Neural network techniques helped reduce the error rate with Google's latest release of its voice recognition technology by 25 percent. And last month Google Fellow Jeff Dean told us that neural networks are becoming widely used in many areas of computer science.

"We're not quite as far along in deploying these to other products, but there are obvious tie-ins for image search. You'd like to be able to use the pixels of the image and then identify what object that is," he said. "There are a bunch of other more specialized domains like optical character recognition."

"I am betting on Google’s team to be the epicenter of future breakthroughs," Hinton wrote in a Google+ post announcing his move.

You can watch Rick Rashid's cool demo here: