The Science of Cause and Effect: From Deep Learning to Deep Understanding
149Judea Pearl (UCLA)
https://simons.berkeley.edu/events/theoretically-speaking-science-of-cause-and-effect
Theoretically Speaking
What does it mean to “understand” a phenomenon, a domain, or a situation?
Machine learning systems have long been labeled “opaque,” “black boxes,” or plain “dumb” for not “understanding” the purpose or the implications of their predictions. But what does it take to qualify as an “understander,” and what computational capacities are needed to meet these requirements?
In this talk, Judea Pearl will propose a formal definition of “understanding” as the capacity to answer questions of three types: predictions, actions, and imagination. He will describe a computational model, a language, and a calculus that facilitate reasoning at these three levels, and demonstrate how features normally associated with understanding follow from this model. They include generating explanations, generalization across domains, integrating data from several sources, recovery from missing data, and more. Pearl will conclude by describing future horizons, including automated scientific explorations, personalized decision-making, and social intelligence.
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 (1984), Probabilistic Reasoning in Intelligent Systems (1988), and Causality (2000, 2009), which won the London School of Economics’ Lakatos Award for 2001. 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 and the National Academy of Engineering and is a fellow of the Cognitive Science Society, the Royal Statistical Society, and the Association for the Advancement of Artificial Intelligence. In 2011, he won the Technion’s Harvey Prize and the ACM’s A.M. Turing Award “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.”
In 2022 he won a BBVA Foundation Frontiers of Knowledge Award for “laying the foundations of modern artificial intelligence, so computer systems can process uncertainty and relate causes to effects.”