# On a Functional Definition of Intelligence

arXiv: https://arxiv.org/abs/2312.09546 · HTML: https://mlsanity.com/paper/2312.09546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [What learning algorithm is in-context learning? Investigations with linear models](https://mlsanity.com/paper/2211.15661/) (2022)
- [AI model GPT-3 (dis)informs us better than humans](https://mlsanity.com/paper/2301.11924/) (2023)
- [HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving](https://mlsanity.com/paper/1703.00426/) (2017)
- [Towards Moral Autonomous Systems](https://mlsanity.com/paper/1703.04741/) (2017)
- [The Turing Deception](https://mlsanity.com/paper/2212.06721/) (2022)

## Abstract (arXiv)

Without an agreed-upon definition of intelligence, asking "is this system intelligent?"" is an untestable question. This lack of consensus hinders research, and public perception, on Artificial Intelligence (AI), particularly since the rise of generative- and large-language models. Most work on precisely capturing what we mean by "intelligence" has come from the fields of philosophy, psychology, and cognitive science. Because these perspectives are intrinsically linked to intelligence as it is demonstrated by natural creatures, we argue such fields cannot, and will not, provide a sufficiently rigorous definition that can be applied to artificial means. Thus, we present an argument for a purely functional, black-box definition of intelligence, distinct from how that intelligence is actually achieved; focusing on the "what", rather than the "how". To achieve this, we first distinguish other related concepts (sentience, sensation, agency, etc.) from the notion of intelligence, particularly identifying how these concepts pertain to artificial intelligent systems. As a result, we achieve a formal definition of intelligence that is conceptually testable from only external observation, that suggests intelligence is a continuous variable. We conclude by identifying challenges that still remain towards quantifiable measurement. This work provides a useful perspective for both the development of AI, and for public perception of the capabilities and risks of AI.