Christopher Manning: Linguistics and the Development of NLP
A conversation with Christopher Manning, Director of the Stanford AI Lab.
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In episode 41 of The Gradient Podcast, Daniel Bashir speaks to Christopher Manning.
Chris is the Director of the Stanford AI Lab and an Associate Director of the Stanford Human-Centered Artificial Intelligence Institute. He is an ACM Fellow, an AAAI Fellow, and past President of ACL. His work currently focuses on applying deep learning to natural language processing; it has included tree recursive neural networks, GloVe, neural machine translation, and computational linguistic approaches to parsing, among other topics.
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(02:40) Chris’s path to AI through computational linguistics
(06:10) Human language acquisition vs. ML systems
(09:20) Grounding language in the physical world, multimodality and DALL-E 2 vs. Imagen
(26:15) Chris’s Linguistics PhD, splitting time between Stanford and Xerox PARC, corpus-based empirical NLP
(34:45) Rationalist and Empiricist schools in linguistics, Chris’s work in 1990s
(45:30) GloVe and Attention-based Neural Machine Translation, global and local context in language
(50:30) Different Neural Architectures for Language, Chris’s work in the 2010s
(58:00) Large-scale Pretraining, learning to predict the next word helps you learn about the world
(1:00:00) mBERT’s Internal Representations vs. Universal Dependencies Taxonomy
(1:01:30) The Need for Inductive Priors for Language Systems
(1:05:55) Courage in Chris’s Research Career
(1:10:50) Outro (yes Daniel does have a new outro with ~ music ~)