Neural Networks Show New Promise for Machine Vision

In the past years, Geoffrey Hinton had an idea that was ahead of its time: to help computers learn from their mistakes. he wanted to create artificial neural networks that could learn to "see" images and recognize patterns through a kind of trial and error in a way that mimicked some models of human brain function. He and his colleagues developed the first practical method for training neural networks, but there was just one problem. "We could never make it work properly," Hinton says. At least, not the way they wanted to. Obstacles such as the lack of computing power stood in their way, and controversy erupted when studies in neuroscience suggested that the human brain probably didn't function like their model network. Flash ahead to 2006, and computing power is no longer a problem. Neuroscience has discovered much about how the brain works, but much of how we see is still a mystery. Hinton, a computer scientist at the University of Toronto and the Canadian Institute for Advanced Research, has discovered some creative strategies to help neural networks fulfil their potential in pattern recognition and artificial intelligence, which he reported in a recent issue of Science (vol. 313, no. 5786, 2006, pp. 504-507). Machine vision is his near-term goal, but the real prize could be insight into the human brain