The same machine learning technique makes it easier to touch the right menu on a Windows tablet with your finger and helps OneNote figure out your handwriting. Launch an app in Windows 8, and three-quarters of the time it opens almost instantly, thanks to machine learning that tells the system which apps to preload into memory because you're going to need them.
Machine learning takes enormous amounts of data -- whether it's a server log, a stream of information from sensors or a huge collection of images, videos, or audio recordings -- and merges it into a system that's better at handling complex situations than any algorithm. The idea has been around for 50 years, but as more and more data becomes available, machine learning has become increasingly useful, going from academic research to powering breakthroughs like usable voice recognition.
"I honestly can't think of any recent product development that Microsoft has been involved in that hasn't involved machine learning," says Microsoft's director of research, Peter Lee, who left DARPA to run Microsoft's research arm. "Everything we do now is influenced, one way or another, by machine learning."
Take the recent Microsoft Band, the flagship device for Microsoft's new Health platform. "We wanted to get the blood flow sensor to provide accurate readings even under extreme athletic duress like rowing," Lee explains (the vice president who approved the project is an avid rower). "It's a very low-cost sensor; just to interpret the reading from the sensor, we've found machine learning is the only practical approach to doing that."
Decades in the lab
How did Microsoft get this good at machine learning? Thank the often underestimated Microsoft Research (MSR) division. "Some of the earliest roots [of this success] go back more than 20 years, with the arrival of people like Eric Horvitz who really brought the whole vision of machine learning to the company," says Lee. "They very quickly came up with the idea of applying this to Microsoft products."
Horvitz, now managing director of MSR's Redmond Lab, has won awards from the Association for the Advancement of Artificial Intelligence to the American Academy of Arts and Sciences, and he recently funded a hundred-year study of artificial intelligence. Having someone that influential at MSR helped attract other pioneers as machine learning became relevant to one field of research after another.
"When we established the lab in Cambridge 15 years ago it added to the momentum, with people who worked on probabilistic modelling, like Chris Bishop, being attracted to MSR."
Bishop literally wrote the book on neural networks and pattern recognition; his textbook made statistical methods common in machine learning. He's now the chief research scientist at MSR Cambridge, where he runs the Machine Learning and Perception group behind skeletal tracking in Kinect, the AI in Forza Motorsport, the TrueSkill ranking system on Xbox, as well as some of the search features in Bing and SharePoint.
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