Flickr users tagged a photograph similar to this one
"architecture," "tourism," and "travel." A machine-learning system that
used a novel training strategy developed at MIT proposed "sky," "roof,"
and "building"; when it used a conventional training strategy, it came up
with "art," "sky," and "beach."
Machine learning, which is the basis for most commercial artificial-intelligence systems, is intrinsically probabilistic. An object-recognition algorithm asked to classify a particular image, for instance, might conclude that it has a 60 percent chance of depicting a dog, but a 30 percent chance of depicting a cat. At the Annual Conference on Neural Information Processing Systems in December, MIT researchers will present a new way of doing machine learning that enables semantically related concepts to reinforce each other. So, for instance, an object-recognition algorithm would learn to weigh the co-occurrence of the classifications "dog" and "Chihuahua" more heavily than it would the co-occurrence of "dog" and "cat." In experiments, the researchers found that a machine-learning algorithm that used their training strategy did a better job of predicting the tags that human users applied to images on the Flickr website than it did when it used a conventional training strategy. "When you have a lot of possible categories, the conventional way of dealing with it is that, when you want to learn a model for each one of those categories, you use only data associated with that category," says Chiyuan Zhang, an MIT graduate student in electrical engineering and computer science and one of the new paper's lead authors. "It's treating all other categories equally unfavorably.
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