Generalization
Dated sources are over 18 months old; other dates are unknown.
Publication dates and source age
Sources counted: 3
Newest dated source: 2023
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Open in the glossary Reading notes · Structured record
In plain language
Doing useful work on examples outside the training set; success depends on how different those examples are. [1] [2]
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Limits & distinctions
A new example can still closely resemble training data. Success there does not establish success in a different setting, and generalization is not a declaration of AGI. [2] [3]
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A fuller explanation
A model generalizes when patterns learned during training support good results on new examples. Recognizing a new photo of a familiar kind of object is an illustrative case. [1]
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How it relates to the map
Atlas reading question: does a claimed improvement hold beyond the examples used to develop the system? [2]
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https://theaiatlas.org/ideas/generalization/
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Sources and what we read
1. Machine Learning Glossary
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read the artificial intelligence, deep model, context window, inference and chat entries. Used for terminology, not product performance claims. Updated date follows the earlier displayed page date; initial publication is unspecified. The chat entry was reread during the same-day beginner-content review. Also read the generalization and compute entries.
2. AI Risks and Trustworthiness
Source is over 18 months old.
Publication dates and source age
Sources counted: 1
Newest dated source: 2023
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read sections 3.1 to 3.3 of the online AI RMF 1.0 excerpt: validity, reliability, robustness, safety and security. Guidance and definitions are not certification of a particular system.
3. Holistic Evaluation of Language Models
Source is over 18 months old.
Publication dates and source age
Sources counted: 1
Newest dated source: 2022-11-16
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.
Read the abstract and version history, including multiple use cases and metrics, standardized comparisons and acknowledged coverage gaps. Used for evaluation principles; historical model scores are not presented as current rankings.
Edition and machine-readable evidence
Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.
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