May 7, 2024 - 4:00 pm Speaker/s:  Bruno Olshausen, UC Berkeley

Abstract: The goal of building machines that can perceive and act in the world as humans and other animals do has been a focus of AI research efforts for over half a century.   Over this same period, neuroscience has sought to achieve a mechanistic understanding of the brain processes underlying perception and action.  It stands to reason that these parallel efforts could inform one another.  However recent advances in deep learning and transformers have, for the most part, not translated into new neuroscientific insights;  and other than deriving loose inspiration from neuroscience, AI has mostly pursued its own course which now deviates strongly from the brain.  Here I propose an approach to building both invariant and equivariant representations in vision that is rooted in observations of animal behavior and informed by both neurobiological mechanisms (recurrence, dendritic nonlinearities, phase coding) and mathematical principles (group theory, residue numbers).  What emerges from this approach is a neural circuit for factorization that can learn about shapes and their transformations from image data, and a model of the grid-cell system based on high-dimensional encodings of residue numbers.  These models provide efficient solutions to long-studied problems that are well-suited for implementation in neuromorphic hardware or as a basis for forming hypotheses about visual cortex and entorhinal cortex.

Bio: Professor Bruno Olshausen is a Professor in the Helen Wills Neuroscience Institute, the School of Optometry, and has a below-the-line affiliated appointment in EECS. He holds B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. He did his postdoctoral work in the Department of Psychology at Cornell University and at the Center for Biological and Computational Learning at the Massachusetts Institute of Technology. From 1996-2005 he was on the faculty in the Center for Neuroscience at UC Davis, and in 2005 he moved to UC Berkeley. He also directs the Redwood Center for Theoretical Neuroscience, a multidisciplinary research group focusing on building mathematical and computational models of brain function (see http://redwood.berkeley.edu).

Olshausen's research focuses on understanding the information processing strategies employed by the visual system for tasks such as object recognition and scene analysis. Computer scientists have long sought to emulate the abilities of the visual system in digital computers, but achieving performance anywhere close to that exhibited by biological vision systems has proven elusive. Dr. Olshausen's approach is based on studying the response properties of neurons in the brain and attempting to construct mathematical models that can describe what neurons are doing in terms of a functional theory of vision. The aim of this work is not only to advance our understanding of the brain but also to devise new algorithms for image analysis and recognition based on how brains work.

Organizer:  Hector Penagos Organizer Email:  cbmm-contact@mit.edu

Mar 12, 2024 - 4:00 pm Venue:  Singleton Auditorium (46-3002) Speaker/s:  Tom Griffiths, Princeton University

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.

Organizer:  Hector Penagos Organizer Email:  cbmm-contact@mit.edu