Peter Norvig: Education for AI and by AI | 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

eventSeminar

Peter Norvig: Education for AI and by AI

Status
Past
Date
Wednesday, October 05, 2022 10:00 AM - 11:00 AM PST/PDT
Location
Hybrid
Share
Link copied to clipboard!
Event Contact
Madeleine Wright
mwright7@stanford.edu

Related Events

Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata
Apr 13, 202612:00 PM - 1:00 PM
April
13
2026

Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

Event

Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata

Apr 13, 202612:00 PM - 1:00 PM

Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?
SeminarApr 15, 202612:00 PM - 1:15 PM
April
15
2026

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

Seminar

Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?

Apr 15, 202612:00 PM - 1:15 PM

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

Matt Beane | Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work
Apr 20, 202612:00 PM - 1:00 PM
April
20
2026

Systems like ChatGPT and Claude assist billions through proactive dialogue—offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI assisted knowledge work.

Event

Matt Beane | Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work

Apr 20, 202612:00 PM - 1:00 PM

Systems like ChatGPT and Claude assist billions through proactive dialogue—offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI assisted knowledge work.

Audio Transcript

HAI Weekly Seminar

Education for AI and by AI

We have seen great advances in Artificial Intelligence in recent years. To me as an AI practitioner and educator, this raises two questions: First, what do learners need to know about AI (and machine learning, and data science) today, and how can they best learn that? Second, how can the technologies of AI be used to facilitate learning in all subjects?

Speaker

Peter NorvigPeter Norvig

Distinguished Education Fellow, Stanford HAI

No tweets available.