Abstract: Recent rapid progress in the creation of artificial intelligence (AI) systems has been driven in large part by innovations in architectures and algorithms for developing large scale artificial neural networks. As a consequence, it’s natural to ask what role abstract principles of intelligence — such as Bayes’ rule — might play in developing intelligent machines. In this talk, I will argue that there is a new way in which Bayes can be used in the context of AI, more akin to how it is used in cognitive science: providing an abstract description of how agents should solve certain problems and hence a tool for understanding their behavior. This new role is motivated in large part by the fact that we have succeeded in creating intelligent systems that we do not fully understand, making the problem for the machine learning researcher more closely parallel that of the cognitive scientist. I will talk about how this perspective can help us think about making machines with better informed priors about the world and give us insight into their behavior by directly creating cognitive models of neural networks.
Bio: I am interested in developing mathematical models of higher level cognition, and understanding the formal principles that underlie our ability to solve the computational problems we face in everyday life. My current focus is on inductive problems, such as probabilistic reasoning, learning causal relationships, acquiring and using language, and inferring the structure of categories. I try to analyze these aspects of human cognition by comparing human behavior to optimal or "rational" solutions to the underlying computational problems. For inductive problems, this usually means exploring how ideas from artificial intelligence, machine learning, and statistics (particularly Bayesian statistics) connect to human cognition. These interests sometimes lead me into other areas of research such as nonparametric Bayesian statistics and formal models of cultural evolution.
I am the Director of the Computational Cognitive Science Lab at Princeton University. Here is a reasonably up-to-date curriculum vitae.
My friend Brian Christian and I recently wrote a book together about the parallels between the everyday problems that arise in human lives and the problems faced by computers. Algorithms to Live By outlines practical solutions to those problems as well as a different way to think about rational decision-making.
I am interested in how novel approaches to data collection and analysis - particularly "big data" - can change psychological research. Read my manifesto and check out the Center for Data on the Mind.