Event Details

In 1986, Dave Rumelhart, Geoff Hinton, and I began the first chapter of Parallel Distributed Processing, a two-volume work proposing neural network based models of human cognition, with the question ‘Why are people smarter than machines?’  At the time, people were far better than existing machine systems in many ways.  Since then, machines have come a long way, and many of their successes rely on the kinds of mechanisms we promoted in the PDP volumes a third of a century ago.  Neural-network based artificial systems now dominate humans at games like Chess and Go, and they have achieved breakthroughs in vision, language, any many other domains.  Yet, it seems clear that these systems have not yet captured all aspects of human intelligence.  I will compare human and artificial neural networks and point out some of the ways in which human still exceed our current machine approaches.  Do these shortcomings mean we need a radically different approach?  This is an important open question.  In the last part of the talk, I will share my thoughts on it, and give suggestions for next steps toward addressing the limitations of our current machine systems.

Speaker Bio

James L. (Jay) McClelland received his Ph.D. in Cognitive Psychology from the University of Pennsylvania in 1975. He served on the faculty of the University of California, San Diego, before moving to Carnegie Mellon in 1984, where he was a founding Co-Director of the Center for the Neural Basis of Cognition, a joint project of Carnegie Mellon and the University of Pittsburgh. In 2006 McClelland moved to the Department of Psychology at Stanford University, where he founded the Center for Mind, Brain, and Computation in 2007 and served as department chair from fall 2009 through summer 2012. He is currently the Lucie Stern Professor in the Social Sciences and Director of the Center for Mind, Brain, Computation and Technology.

Over his career, McClelland has contributed to both the experimental and theoretical literatures in a number of areas, most notably in the application of connectionist/parallel distributed processing models to problems in perception, cognitive development, language learning, and the neurobiology of memory. He was a co-founder with David E. Rumelhart of the Parallel Distributed Processing (PDP) research group, and together with Rumelhart he led the effort leading to the publication in 1986 of the two-volume book, Parallel Distributed Processing, in which the parallel distributed processing framework was laid out and applied to a wide range of topics in cognitive psychology and cognitive neuroscience. McClelland and Rumelhart jointly received the Distinguished Scientific Contribution Award from the American Psychological Association, the 2001 Grawemeyer Prize in Psychology, and the 2002 IEEE Neural Networks Pioneer Award for this work. In addition, McClelland has received the David E. Rumelhart prize for contributions to the theoretical foundations of Cognitive Science, the NAS Atkinson Prize in Psychological and Cognitive Sciences, and the Heineken Prize in Cognitive Science, and he is a member of the National Academy of Sciences.

McClelland currently teaches on the PDP approach to cognition and its neural basis in the Psychology Department and in the Symbolic Systems Program at Stanford and conducts research on learning, memory, conceptual development, language processing, and mathematical cognition at Stanford and as a consulting research scientist at DeepMind.