How Language Makes us Smart (without Big Data) presented by Charles Yang

Abstract: Language provides the glue that combines simpler concepts into complex ones. To study how language guides conceptual development, we need precise accounts of how rules are learned from the child's linguistic experience, which is extremely limited in comparison to the amount of data available to current machine learning methods. In this talk, I discuss a mathematical model of inductive generalization, which enables language learning with very small amount of data. Such a view of learning has strong implications for the cross-cultural/linguistic variation of development. As a case study, I show that Hong Kong children learning Cantonese, which has a relatively simpler formal counting system, develop understanding of symbolic numbers a full year ahead of English-learning children in the United States, which is precisely predictable from the learning model. The new conception of learning adds another wrinkle to the eternal question of how language and thought are related to each other.

Bio: Charles Yang studied at the MIT AI lab and now teaches linguistics, computer science and psychology and directs the Program in Cognitive Science at the University of Pennsylvania. He is the author of several books: The Price of Linguistic Productivity (2016 MIT Press) won the Leonard Bloomfield Award from the Linguistic Society of America. His honors include a Guggenheim fellowship.