Machines that learn better
In the last 20 years or so, many of the key advances in artificial-intelligence research have come courtesy of machine learning, in which computers learn how to make predictions by looking for patterns in large collections of training data. 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.Historically, building a machine-learning system capable of learning a new task would take a graduate student somewhere between a few weeks and several months, says Daniel Roy, a PhD student in the Department of Electrical Engineering and Computer Science who along with Cameron Freer, an instructor in pure mathematics, led the new research. A handful of new, experimental,...