Past talk · AI/ML Seminar Series
Losing bits and finding meaning: Efficient compression underlies meaning in language
Assistant Professor of Language Science, University of California, Irvine
- Date & time
- Monday, October 30, 2023 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
Our world is extremely complex, and yet we are able to exchange our thoughts and beliefs about it using a relatively small number of words. What computational principles can explain this extraordinary ability? In this talk, I argue that in order to communicate and reason about meaning while operating under limited resources, both humans and machines must efficiently compress their representations of the world. In support of this claim, I present a series of studies showing that: (i) human languages evolve under pressure to efficiently compress meanings into words via the Information Bottleneck (IB) principle; (ii) the same principle can help ground meaning representations in artificial neural networks trained for vision; and (iii) these findings offer a new framework for emergent communication in artificial agents. Taken together, these results suggest that efficient compression underlies meaning in language.
About the speaker
Noga Zaslavsky is an Assistant Professor in UCI's Language Science department. Before joining UCI, she was a postdoctoral fellow at MIT. She holds a Ph.D. (2020) in Computational Neuroscience from the Hebrew University. Her research aims to understand the computational principles that underlie language and cognition by integrating methods from machine learning, information theory, and cognitive science.