What are Embeddings? | 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

What are Embeddings?

Embeddings are numerical representations that convert complex data (like words, images, or other objects) into vectors of numbers that capture their meaning or characteristics. The core idea is that similar items end up close together in this mathematical space—for example, the words "dog" and "puppy" would have similar embeddings, while "dog" and "toast" would be far apart.

Navigate
  • About
  • Events
  • 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


Embeddings mentioned at Stanford HAI

Explore Similar Terms:

Latent Space | Dimensionality Reduction | Vector Database

See Full List of Terms & Definitions

Enroll in a Human-Centered AI Course

This HAI program covers technical fundamentals, business implications, and societal considerations.
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Re
Jan 01
Research
Your browser does not support the video tag.

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Re
Jan 01

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Your browser does not support the video tag.
Research
Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency
Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
Mar 16
Research
Your browser does not support the video tag.

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
Mar 16

Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency

Your browser does not support the video tag.
Research
Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency
Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
May 31
Research
Your browser does not support the video tag.

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt
May 31

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

Your browser does not support the video tag.
Research