The Science of Causal and Effect: From Deep Learning to Deep Understanding

The UCI Department of Computer Science (CS) and Distinguished CS Lecture Series is proud to present Judea Pearl, UCLA. Learn more at https://www.cs.uci.edu/events/seminar-series/

Title: The Science of Causal and Effect: From Deep Learning to Deep Understanding

Abstract:
We will define “deep understanding” as the capacity to answer questions at all three levels of the reasoning hierarchy: predictions, actions, and imagination. Accordingly I will describe a language, calculus and algorithms that facilitate all three modes of reasoning. The talk will then summarize several reasoning tasks that have benefitted from this calculus, including attribution, mediation, data-fusion and missing-data. I will conclude with future applications, which include: automated scientific explorations, personalized decision making and social intelligence

Bio:
Judea Pearl is Chancellor 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.”

Website: http://bayes.cs.ucla.edu/jp_home.html