Geoffrey Hinton discusses how AI could inform our understanding of the brain | VentureBeat

In other deep-thinking news, see What We’ve Built Is a Computational Language (and That’s Very Important!) | Stephen Wolfram Blog

"To be clear, Hinton thinks that neuroscientists have much to learn from AI researchers. In fact, he believes that AI systems of the future will mostly be of the unsupervised variety. Unsupervised learning — a branch of machine learning that gleans knowledge from unlabeled, unclassified, and uncategorized test data — is almost humanlike in its ability to learn commonalities and react to their presence or absence, he says.

“If you take a system with billions of parameters, and you do scholastic gradient descent in some objective function, it works much better than you’d expect … The bigger you scale things, the better it works,” he said. “That makes it far more plausible that the brain is computing the gradient of some objective function and updating the strength of synapses to follow that gradient. We just have to figure out how it gets the gradient and what the objective function is.”"

Geoffrey Hinton discusses how AI could inform our understanding of the brain | VentureBeat