CS@CU Distinguished Lecture Series

Cognitive Workforce Revolution with Trustworthy and Self-Learning Generative AI

Monica Lam, Stanford University

Abstract:

Generative AI, and in particular Large Language Models (LLMs), have already changed how we work and study. To truly transform the cognitive workforce however, LLMs need to be trustworthy so they can operate autonomously without human oversight. Unfortunately, language models are not grounded and have a tendency to hallucinate. Our research hypothesis is that we can turn LLM into useful workers across different domains if we (1) teach them how to acquire and apply knowledge in external corpora such as written documents, knowledge bases, and APIs; (2) have them self-learn through model distillation of simulated conversations. We showed that by supplying different external corpora to our Genie assistant framework, we can readily create trustworthy agents that can converse about topics in open domains from Wikidata, Wikipedia, or StackExchange; help navigate services and products such as restaurants or online stores; persuade users to donate to charities; and improve the social skills of people with autism spectrum disorder.

Bio:

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in the School of Engineering at Stanford, in the Departments of Computer Science and, by courtesy, Electrical Engineering. She is the Faculty Director of the Stanford Open Virtual Assistant Laboratory. Prof. Lam is a member of the National Academy of Engineering and an ACM Fellow. Prof. Lam has won numerous best paper awards, and has published over 150 papers on natural language processing, machine learning, compilers, computer architecture, operating systems, high-performance computing, and HCI. Prof. Lam's recent research on natural language processing led to the creation of the first conversational virtual assistant based on deep learning, which received Popular Science's Best of What's New Award in Security in 2019. She co-authored the "Dragon Book", the definitive text on compiler technology. She was on the founding team of Tensilica, the first startup in configurable processor cores. She received a B.Sc. from University of British Columbia and a Ph.D. from Carnegie Mellon University.