How Insights into Human Learning Fosters Smarter A.I.

Recent breakthroughs in creating artificial systems that outplay humans in a diverse array of challenging games have their roots in neural networks inspired by information processing in the brain. In a Review published June 14 in Trends in Cognitive Sciences, researchers from Google DeepMind and Stanford University update a theory originally developed to explain how humans and other animals learn - and highlight its potential importance as a framework to guide the development of agents with artificial intelligence. First published in 1995 (Psychol Rev., 102(3):419-57), the theory states that learning is the product of two complementary learning systems. The first system gradually acquires knowledge and skills from exposure to experiences, and the second stores specific experiences so that these can be replayed to allow their effective integration into the first system. The paper built on an earlier theory by influential British computational neuroscientist David Marr and on then-recent discoveries in neural network learning methods.