“It’s the best possibility we really have for understanding the brain at present,” said Sue Becker, a professor of psychology, neuroscience, and behavior at McMaster University in Hamilton, Ontario. “I don’t know of a model that explains a wider range of phenomena in terms of learning and the structure of the brain.”
Hinton, a pioneer in the field of artificial intelligence, has always wanted to understand the rules governing when the brain beefs a connection up and when it whittles one down — in short, the algorithm for how we learn. “It seemed to me if you want to understand something, you need to be able to build one,” he said. Following the reductionist approach of physics, his plan was to construct simple computer models of the brain that employed a variety of learning algorithms and “see which ones work,” said Hinton, who splits his time between the University of Toronto, where he is a professor of computer science, and Google.
I think this approach—trying to approximately model the brain’s functions—is ultimately going to provide much more understanding our brain than studying actual brains and nervous systems. The latter is absolutely useful, but not only are our tools for doing so remarkably primitive, but even relatively simple brains are monumentally complex, which means it’s fairly difficult to truly understand how they function by directly studying them.
Learning more from computer models may seem counterintuitive, but doing so forces us to actually understand the system’s design, which should indicate more universal principles that can be applied to biological brains as well.
If you can’t tell, I find the brain to be one of the most fascinating things we have to study. We are truly in the early days of understanding how they work, and not only is discovering it wonderfully exciting, but I think it will provide us with the tools to build computer systems and software of an entirely new magnitude, and answer many questions we’ve had about ourselves that have been relegated to religion and philosophy out of necessity.