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Inside Microsoft's AI Comeback

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But while his peer scientists Yann LeCun and Geoffrey Hinton have signed on to Facebook and Google, respectively, Bengio, 53, has chosen to continue working from his small third-floor office on the hilltop campus of the University of Montreal. Shum, who is in charge of all of AI and research at Microsoft, has just finished a dress rehearsal for next week's Build developers conference, and he wants to show me demos. Shum has spent the past several years helping his boss, CEO Satya Nadella, make good on his promise to remake Microsoft around artificial intelligence. Bill Gates showed off a mapping technology in 1998, for example, but it never came to market; Google launched Maps in 2005.


*Applause* YouTube's caption upgrade shows how machine learning is helping the disabled

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FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.


The New Intel: How Nvidia Went From Powering Video Games To Revolutionizing Artificial Intelligence

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It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. "We've been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia's platform," says Marc Andreessen of venture capital firm Andreessen Horowitz. Starting in 2006, Nvidia released a programming tool kit called CUDA that allowed coders to easily program each individual pixel on a screen. From his bedroom, Krizhevsky had plugged 1.2 million images into a deep learning neural network powered by two Nvidia GeForce gaming cards.


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The takeaway here is the machine learning allows companies to build better applications that interact with things people create: pictures, speech, text, and other messy things. Interfaces powered by machine learning will make computing omnipresent. Like their other products, both Google Search and Facebook Photos demonstrate how RDAs generate significant network effects. Or, you could have a giant install base which only occasionally codes data (Facebook, whose users tag photos usually when they're uploaded).