The Science of Cause and Effect: From Deep-Learning to Deep Understanding and Personalized Decision-Making
The 176th Cutter Lecture on Preventive Medicine
Speaker:
Judea Pearl, PhD
Chancellor’s Professor of Computer Science and Statistics
University of California, Los Angeles
Discussant:
James Robins, MD
Mitchell L Robin LaFoley Dong Professor
of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract:
This talk will define a coherent framework for designing machines that exhibit “Deep Understanding,” that is, a capacity to answer questions of three types: predictions, interventions and counterfactuals.
Pearl will describe a computational model that facilitates reasoning at these three levels, and demonstrate how features normally associated with “understanding” follow from this model. These include generating explanations, generalizing across domains, integrating data from several sources, assigning credit and blame, recovering from missing data, and more. Pearl will conclude by outlining future research directions, including the challenge of Large Language Models and personalized decision-making.
Biosketch:
Judea Pearl is Chancellor’s professor of computer science and statistics at UCLA, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human cognition, and philosophy of science. He has authored three fundamental books, Heuristics (1983), Probabilistic Reasoning (1988) and Causality (2000, 2009) which won of the London School of Economics Lakatos Award in 2002. More recently, he co-authored Causal Inference in Statistics (2016, with M. Glymour and N. Jewell) and “The Book of Why” (2018, with Dana Mackenzie) which brings causal analysis to a general audience. Pearl is a member of the National Academy of Sciences the National Academy of Engineering, a Fellow of the Cognitive Science Society, the Royal Statistical Society and the Association for the Advancement of Artificial Intelligence. In 2012, he won the Technion’s Harvey Prize and the ACM Alan Turing Award “for fundamental contribution to artificial intelligence through the development of a a calculus for probabilistic and causal reasoning.”
In 2022 he won the BBVA Frontiers of Knowledge Award for “laying the foundations of modern artificial intelligence, so computer systems can process uncertainty and relate causes to effects.”