Meet the Man Google Hired to Make AI a Reality

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Meet the Man Google Hired to Make AI a Reality
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Geoffrey Hinton is leaving Google and somewhat renouncing AI, but it didn't start off that way. Let's take a look back to how it all started. (From 2014)

>'We have never seen machine learning or artificial intelligence technologies so quickly make an impact in industry.'The deep learning revolution was inevitable, they say, but developments like the speech recognition and artificial vision systems adopted by Microsoft, Google, Yahoo, and other giants of the web came sooner because of the NCAP -- and Hinton in particular.

Others outside the group agree. "Over the last 20 to 30 years, he has been pushing forward the frontier of neural networks and deep learning," says Kai Yu, the director of Baidu’s Institute of Deep Learning. "We have never seen machine learning or artificial intelligence technologies so quickly make an impact in industry. It's very impressive."

Hinton also travels the world giving talks on deep learning, and he mentors graduate students at the University of Toronto and beyond. Welling says that Hinton has a habit of suddenly yelling: "I understand how the brain works now!" It's an infectious thing. "He would do this every week," Welling says. "That's hard to match."

Through NCAP and CIFAR, Hinton runs a summer school for students to learn from NCAP members, working to foster the next generation of AI researchers. With so many commercial companies moving into the field, that is more important than ever. It's not just the tech giants who are joining the movement. We've seen a slew of deep learning startups, including companies like Ersatz, Expect Labs, and Declara.

Where will this next generation of researchers take the deep learning movement? The big potential lies in deciphering the words we post to the web -- the status updates and the tweets and instant messages and the comments -- and there’s enough of that to keep companies like Facebook, Google, and Yahoo busy for an awfully long time. The aim to give these services the power to actually understand what their users are saying -- without help from other humans.

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