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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.


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

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A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

#artificialintelligence

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


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.