Universal intelligence: a definition of machine intelligence
Open accessAbstract
A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
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Highly intelligent agents are very effective at achieving their goals.
“Furthermore, we should expect highly intelligent agents to be very effective at achieving their goals [Legg and Hutter, 2007].”
From The alignment problem from a deep learning perspective (2022) · cites this paper as Legg and Hutter, 2007
The Legg and Hutter definition draws from a 'mainstream scientific definition of intelligence' developed by Linda Gottfredson.
“The Legg and Hutter definition is a good example here: they draw from a 'mainstream scientific definition of intelligence', a public statement published in the Wall Street Journal in 1994, as developed by Linda Gottrfredson.”
From Abundant intelligences: placing AI within Indigenous knowledge frameworks (2025) · cites this paper as Legg and Hutter 2007
Legg and Hutter's conception relies heavily on a framework built on the idea of hierarchy between races and the measurability of that hierarchy via intelligence tests.
“Legg and Hutter's only disclaimer about this is to say that they do not completely agree with Gottfredson's definition (Legg and Hutter 2007, p. 12); however, their conception relies heavily on this framework that is built on the idea of hierarchy between races and the measurability of that hierarchy via intelligence tests.”
From Abundant intelligences: placing AI within Indigenous knowledge frameworks (2025) · cites this paper as Legg and Hutter 2007
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“What capabilities are necessary for general intelligence (Legg & Hutter, 2007)?”
Background
- Authors
- Legg, S., & Hutter, M.
- Affiliations
- Dalle Molle Institute for Artificial Intelligence Research (CH)
- Corresponding author
- Legg, Shane
- Venue
- Minds and Machines
- Published
- 2007-11-10
- Volume
- 17
- Issue
- 4
- Pages
- 391-444
- ISSN
- 0924-6495 (ISSN-L)
- 1572-8641
- OpenAlex
- W2101493843
- Cited by
- 803
- References
- 111
- Citations by year
- 2026: 86 · 2025: 113 · 2024: 71 · 2023: 89 · 2022: 63 · 2021: 60 · 2020: 65 · 2019: 48 · 2018: 36 · 2017: 27 · 2016: 14 · 2015: 23 · 2014: 14 · 2013: 15 · 2012: 23
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Minds and Machines (publishedVersion)
- reroDoc Digital Library (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Keywords
- machine intelligence, definition of intelligence, universal intelligence, human intelligence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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