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Machines That Learn Better


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MIT's Cameron Freer and Daniel Roy

Cameron Freer, left, an MIT instructor in pure mathematics, and Daniel Roy, right, a Ph.D. student in the Department of Electrical Engineering and Computer Science, have described an inference algorithm that can handle a large class of problems involving continuous data.

Credit: Jason Dorfman / CSAIL

In the last 20 years or so, many of the key advances in artificial-intelligence research have come courtesy of machine learning. A new approach called probabilistic programming makes it much easier to build machine-learning systems, but it's useful for a relatively narrow set of problems. Now, MIT researchers have discovered how to extend the approach to a much larger class of problems, with implications for subjects as diverse as cognitive science, financial analysis and epidemiology.

A handful of new, experimental, probabilistic programming languages—one of which, Church, was developed at MIT—promise to cut the time of building a machine-learning system down to a matter of hours, says Daniel Roy, a Ph.D. student in the Department of Electrical Engineering and Computer Science, who along with Cameron Freer, an instructor in pure mathematics, led the new research.

From MIT News
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