Are people still smarter than machines?

"We live in a world where AI seems to be improving in its capabilities everyday, exceeding human abilities in many domains. AI systems use principles and processes similar to those at work in our brains when we perceive, think, experience the world and reason, and their strengths and weaknesses are like ours. Understanding these similarities, I argue, can help us understand — and relate to — both ourselves and our machines. But we get by with far less than today's AI systems in many ways — far less data, far less parallel computation, and far less exact storage of our recent mental states. How do we do it? This remains an important unanswered question about the human mind. I will describe some of the directions we are pursuing to try to answer this question."

Professor James L. (Jay) McClelland received his Ph.D. in Cognitive Psychology from the University of Pennsylvania in 1975.

Today, he is a Professor in the Psychology Department at Stanford, where he directs the Center for Mind, Brain, Computation, and Technology, and a Consulting Research Scienist at Google DeepMind. McClelland collaborated with David Rumelhart in the late 1970's and early 1980's, co-leading the two-volume work, Parallel Distributed Processing, which laid out a neural-network-based framework for modeling human cognition.

Among other joint awards for this work, McClelland, Rumelhart and Geoff Hinton, who also contributed to the PDP effort, received the American Association for the Advancement of Science's Golden Goose Award, which honors research conducted to address basic science questions that turned out to have practical applications.

Additonal honors and awards include Membership in the US National Academy of Sciences and the British Academy, the David E. Rumelhart prize in Cognitive Science, the NAS Atkinson Prize in Psychological and Cognitive Sciences, and the Heineken Prize in Cognitive Science.