Research on understanding and building artificially intelligent systems has moved from symbolic approaches to statistical learning, and is now beginning to study interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data, as well as conceptual progress on what constitutes a causal representation and a causal world model. I will present basic concepts and thoughts, and - time permitting - some applications to astronomy.
* This event is open to the public with emphasis on graduate students in machine learning, computer science, ECE, statistics, mathematics, linguistics, medicine, as well as PhD-level data scientists in the GTA.