A Collection of Definitions of Intelligence

By Shane Legg, Marcus Hutter. Artificial General Intelligence, 2007

562 citations recorded by the source corpus.

This chapter is a survey of a large number of informal definitions of “intelligence” that the authors have collected over the years. Naturally, compiling a complete list would be impossible as many definitions of intelligence are buried deep inside articles and books. Nevertheless, the 70 odd definitions presented here are, to the authors' knowledge, the largest and most well referenced collection there is.

Subfields: Cognitive Neuroscience, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/921328ec369ac8f30d719ec553dde0cb8b708ef4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Strategy and Management

Citations in the corpus

Artificial General Intelligence: Concept, State of the Art, and Future Prospects (2009, methodology): “In a 2010 paper, I sought to modify Legg and Hutter’s mathematical approach in an attempt to account for the factors Wang’s deﬁnition highlights (Goertzel, 2010); • The pragmatic general intelligence is deﬁned relative to a given probability distribution over environments and goals, as the average g”
The Measure of Intelligence (2019, unknown): “In the context of AI research, Legg and Hutter [53] summarized in 2007 no fewer than 70 definitions from the literature into a single statement: “Intelligence measures an agent’s ability to achieve goals in a wide range of environments.”
Behaviour Suite for Reinforcement Learning (2019, background): “According to some deﬁnitions, an agent that can learn to perform at or above human level across a wide variety of tasks is an artiﬁcial general intelligence (AGI) (Minsky, 1961; Legg et al., 2007).”
Trustworthy Artificial Intelligence: A Review (2022, background): “Legg and Hutter [122] define AI as a process of imitating human behavior and decision-making capabilities.”
40 years of cognitive architectures: core cognitive abilities and practical applications (2018, background): “Given the issues with defining intelligence (Legg and Hutter 2007), a more practical solution is to treat it as a set of competencies and behaviors demonstrated by the system.”
Artificial Intelligence and the Public Sector—Applications and Challenges (2018, background): “” Considering a variety of previous definitions, Legg and Hutter (2007); Legg & Hutter (2006) develop an integrated definition of intelligence, describing it as the ability to interact, learn, adopt, and resort to information from experiences, as well as to deal with uncertainty.”
Transparency in artificial intelligence (2020, background): “This is linked to the fact that intelligence alone has been defined in at least 70 different ways (Legg & Hutter, 2007).”
Neuroscience-Inspired Artificial Intelligence. (2017, background): “When we say AI, we mean work in machine learning, statistics, and AI research that aims to build intelligent machines (Legg and Hutter, 2007).”
Comparing continual task learning in minds and machines (2018, background): “I systems must learn to perform multiple distinct tasks over their lifetimes while avoiding mutual interference among them (1).”
Randomized Prior Functions for Deep Reinforcement Learning (2018, background): “However, if our goal is artificial general intelligence, then it is disconcerting that our best agents can perform very poorly even in simple problems [33, 39].”
Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, background): “Whether one surveys psychologists, sociologists, biologists, neuroscientists, or philosophers, the term “ intelligence ” can take more than 70 different definitions (Legg & Hutter, 2007).”
Generative AI at Work (2023, unknown): “There are many definitions of artificial intelligence and of intelligence itself—Legg et al. (2007) list over 70 of them.”