HAI Weekly Seminar with Percy Liang | Stanford HAI
Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Your browser does not support the video tag.
eventSeminar

HAI Weekly Seminar with Percy Liang

Status
Past
Date
Wednesday, September 30, 2020 10:00 AM - 11:00 AM PST/PDT
Topics
Natural Language Processing

Natural language promises to be the ultimate interface for interacting with computers, allowing users to effortlessly tap into the wealth of digital information and extract insights from it. 

Today, virtual assistants such as Alex, Siri, and Google Assistant have given a glimpse into how this long-standing dream can become a reality, but there is still much work to be done.

In this talk, I will discuss building natural language interfaces based on semantic parsing, which converts natural language into programs that can be executed by a computer.  There are multiple challenges for building semantic parsers: how to acquire data without requiring laborious annotation, how to represent the meaning of sentences, and perhaps most importantly, how to widen the domains and capabilities of a semantic parser.  Finally, I will talk about a new promising paradigm for tackling these challenges based on learning interactively from users.

Speaker
Percy Liang
Percy Liang
Associate Professor of Computer Science, Stanford University | Director, Stanford Center for Research on Foundation Models | Senior Fellow, Stanford HAI

Watch Event Recording

Share
Link copied to clipboard!
More from HAI and SDS seminars
  • Inside the 2026 AI Index Report | Stanford HAI
    SeminarMay 20, 202612:00 PM - 1:15 PM
    May
    20
    2026

    The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.

Related Events

Wolfgang Lehrach | Code World Models for General Game Playing
SeminarMay 13, 202612:00 PM - 1:15 PM
May
13
2026

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

Seminar

Wolfgang Lehrach | Code World Models for General Game Playing

May 13, 202612:00 PM - 1:15 PM

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

AI+Science: Accelerating Discovery
ConferenceMay 05, 20268:30 AM - 6:45 PM
May
05
2026

AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.

Conference

AI+Science: Accelerating Discovery

May 05, 20268:30 AM - 6:45 PM

AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.