Mitchell is testing the concept of having two classes of learning algorithms in essence train each other, so that together they can do better than either would alone. #HOW TO MAKE A COMPUTER PROGRAM THAT LEARNS SOFTWARE#In machine-learning applications, software is "trained" on test cases devised and labeled by humans, scored so it knows what it got right and wrong, and then sent out to solve real-world cases. "I could develop code in a day that would have taken me half a month to develop by hand," he says.Ĭomputer scientist Tom Mitchell, director of the Center for Automated Learning and Discovery at Carnegie Mellon University, says machine learning is useful for the kinds of tasks that humans do easily - speech and image recognition, for example - but that they have trouble explaining explicitly in software rules. And machine-learning techniques gave his team a productivity boost as well, Thrun says. The car learned road-surface characteristics as it went. Thrun used several new machine-learning techniques in software that literally drove an autonomous car 132 miles across the desert to win a $2 million prize for Stanford in a recent contest put on by the Defense Advanced Research Projects Agency. This is a really big item for the future." So there's a trend to make software that can adapt itself. "The problem is, software is becoming larger and larger and less and less manageable. "In the past, someone would look at a problem, write some code, test it, improve it by hand, test it again and so on," says Sebastian Thrun, a computer science professor at Stanford University and the director of the Stanford Artificial Intelligence Laboratory. Stanford professor Sebastian Thrun with "Stanley," the car that used machine-learning techniques to drive itself 132 miles across the desert.Īnd as the technology grows, so does the need for it.
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