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How machine learning ate Microsoft

Mary Branscombe | Feb. 23, 2015
Yesterday's announcement of Azure Machine Learning offers the latest sign of Microsoft's deep machine learning expertise -- now available to developers everywhere.

That sounds rather more achievable when you talk to Peter Lee about the advances he believes Microsoft can make in the next decade.

Last year he showed off early work on a machine learning system that could use your phone camera to not only recognize a dog, but identify the breed, or tell you whether a plant was poisonous. That's Project Adam, which is trying to apply cloud principles of scale to machine learning. Normally, machine learning happens on a single system that you can't scale beyond a cluster because it has to be synchronous; with Project Adam, the learning can be asynchronous, so you could spread it out over a whole data center.

Project Adam is only one of what Lee calls several machine learning "moonshots" -- "efforts that are truly aspirational but have really concrete, easy-to-assess goals so you know whether you've done it or not." He's very protective of them ("the pressure can be very distracting"), so he won't name the other projects or say what the goals are -- but they're big.

"Project Adam truly pertains to going beyond speech and vision to really a deep understanding of human discourse. Ultimately, it's the next stage of a true AI where we really understand at scale how to get a machine to understand what human beings are talking about. The goals there are so interesting. From a scientific perspective there are tremendous implications for our understanding; from an engineering perspective the scale is really dazzling and from a commercial perspective the prospects for applications are incredibly enticing. We have very significant efforts in the foundations of speech and translation along the same lines."

Lee is both excited and pragmatic about the potential of these big projects -- and the side benefits "that have started to dribble out already" -- from OneDrive (which now uses machine learning to tag your photographs) to the demonstrations of Skype Translator (where performance improvements from new techniques have left even researchers "stunned"). Plus, there's a ready-made platform in Azure Machine Learning for bringing those new techniques to product groups inside Microsoft and developers elsewhere.

"With these large aspirational efforts, there's always a part of me that harbors some skepticism about whether we'll ever get there," Lee admits. "Some of these things are so fantastical, but you never know! You get surprised. And as a research manager, I'm comfortable that whether we get there or not, there are going to be a tremendous number of spinoffs and new knowledge."

Whether or not Microsoft makes more fundamental breakthroughs in AI, what it learns about using machine learning will carry on showing up in all the products you use -- including ones you build yourself.


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