That's all thanks to deep learning. It's one of the fastest-moving areas in AI today; the pioneers of deep learning work at Google, at Facebook, at Baidu -- and at Microsoft.
In 2009, when Geoff Hinton of the University of Toronto proposed creating a neural network that would recognize speech by gradually building up its understanding of more and more words (a vastly simplified version of one of the techniques the human brain uses to recognize patterns in images sounds), most researchers weren't interested. In a testament to MSR's willingness to experiment, an intern and a graduate student of Hinton got approval to work with his researchers and try out this deep network with real data.
Their results weren't only a little bit better; they were 25 percent more accurate. Once they were published, Lee points out, "not only Microsoft but most of the industry transitioned to using them."
Bringing machine learning to the masses
As Microsoft offers its own machine learning tools to developers, the company may enjoy greater recognition for its pioneering work. "We have a treasure trove of knowledge and algorithms and code across a vast array of machine problems that would be incredibly powerful and satisfying to get into wider use," says Lee.
Azure Machine Learning is how Microsoft is trying to do that. Joseph Sirosh calls it "the fastest way to build predictive models and deploy them. All you need is a Web browser to start machine learning. It allows simple, one-click creation of APIs in the cloud and that makes the deployment easy. It's easy to hook up a Web page, it's easy to hook up a mobile app. That's why I think it's transforming how development is done."
The new Azure ML service started out as a MSR Excel demo, sending data to experimental machine learning-driven data analytics running on Azure. A couple of years before he became CEO, Satya Nadella came across the demo and immediately saw the potential of turning it into a product for business customers. He persuaded the researcher, Roger Barga, to join a team inside his cloud division. "Satya got excited, and he got me excited," Barga remembers.
The idea was to combine the machine learning tools from the research team with the expertise that product teams across Microsoft had gained by implementing machine learning algorithms. Making machine learning work well isn't only about having a good algorithm or even making it perform at scale. You also need to make it consistent. The same algorithm in different machine learning packages often delivers different answers; using heuristics to find the model that best matches your data takes a lot of experience.
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