More-flexible machine learning

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…


Link to Full Article: More-flexible machine learning

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!