Language Emerges from Computability

摘要

Human language is an extremely complex yet tightly constrained system. 语言学家的研究 which classes of constraints are necessary and sufficient, how systems effectively learn them from sparse data, and what representations they use when doing so. 这 talk will show how each of these properties emerges from basic principles of computability, drawing on insights from automata theory and algorithmic learning theory. 我也会 show how these principles can be applied to explore the generalization abilities of distributed "neural" machine learning models.

生物

Jon Rawski is an assistant professor in the Dept of Linguistics & language Development, where he teaches courses on general and computational linguistics. 他的工作涉及 the mathematics of language and learning, by intersecting cognitive science, linguistics, and theoretical computer science. He received his PhD from Stony Brook University in 2021.

时间和地点

Tuesday, March 15, 2022 at 1:30PM in MH 225 or 变焦 http://sjsu.zoom.us/j/86925918978?pwd=U1V6UDNnNi9jaDFMU2dDZS92bTNpUT09