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arXiv:1911.01547cs.AI

On the Measure of Intelligence

François Chollet

Abstract

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.

arXiv comments
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence

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Linked by 25 identifiable commenters in 39 comments across 33 threads, from Nov 2019 to Sep 2026. Found in 13 HN submissions from Nov 2019 to Sep 2026. Open submission discussions.

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Discussed in comments33 threads

    • I rather like this paper, but I think it is generous to say their benchmarks measure "intelligence". [0] https://arxiv.org/pdf/1911.01547 We have no dang clue what intelligence is, nor how to measure it. [1] https://taylor.town/crowpower

      surprisetalkView on HNParent context

  1. GPT-6 Astra 1 comment
    • I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed…

      astrobiasedView on HNParent context

  2. Grok 4.6 1 comment
    • You're take basically lines up with Francois Chollet: https://arxiv.org/abs/1911.01547 intelligence is more like polishing a ball smooth than growing the ball to infinity. For many tasks, it will be smooth enough.

      LarsDu88View on HNParent context

    • Yeah, but there's attempts to fix it. The Cholet paper (https://arxiv.org/abs/1911.01547) is a good attempt, shifting from measuring ~skill to measuring ~acquiring skill. It's the framework behind ARC-AGI benchmarks.

      gobdovanView on HNParent context

  3. ARC-AGI-3 2 comments
    • It may have been tested on the full set, but the score you quote is for a single game environment. Not the full public set. That fact is verbatim in what you responded to and vbarrielle quoted. It scored 97% in one game, and 0% in another game. The full prelude to what…

      daveguyView on HNParent context

    • Just because humans are usually tested in a particular way that allows them to make up for a lack of generality with an outstanding performance in their specialization doesn't mean that is a good way to test generalization itself. Apparently someone here doesn't know how…

      daveguyView on HNParent context

    • You're not the only one. ARC-AGI is a laudable effort, but its fundamental premise is indeed debatable: "We argue that human cognition follows strictly the same pattern as human physical capabilities: both emerged as evolutionary solutions to specific problems in specific…

      whiplash451View on HNParent context

    • ARC-AGI was introduced in 2019: https://arxiv.org/abs/1911.01547 GPT-3 didn't come out until 2020.

      cubefoxView on HNParent context

    • I understand Chollet is transparent that the "branding" of the ARC-AGI-n suites is meant to be suggestive of its purpose, than substantial. However, it does rub me the wrong way - as someone who's cynical of how branding can enable breathless AI hype by bad journalism. A…

      nxobjectView on HNParent context

    • > Getting a high score on ARC doesn't mean we have AGI and Chollet has always said as much AFAIK He only seems to say this recently, since OpenAI cracked the ARC-AGI benchmark. But in the original 2019 abstract he said this: > We argue that ARC can be used to measure a…

      cubefoxView on HNParent context

    • > We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans. https://arxiv.org/abs/1911.01547

      cubefoxView on HNParent context

    • Assessing AI's progress toward replicating the full breadth and depth of human intelligence is a deceptively hard problem. A paper by François Chollet, who was until recently a researcher at Google, called "On the Measure of Intelligence" is the best overview of the challenges…

      mrandishView on HNParent context

    • Unnamed AI research lab | ML research engineer | Zurich, Switzerland | Full-time | Onsite | 100-200k CHF I am starting a research lab funded by a hedge fund that I created 5 years ago. The hedge is doing well, and applies ML to quant finance. The research and trading activities…

      not-a-catView on HNParent context

    • Unnamed AI research lab | ML research engineer | Zurich, Switzerland | Full-time | Onsite | 100-200k CHF I am starting a research lab funded by a hedge fund that I created 5 years ago. The hedge is doing well, and applies ML to quant finance. The research and trading activities…

      not-a-catView on HNParent context

    • What they mean by "specific learned skill" is that each task illustrates the use of certain "core knowledge priors" that François Chollet has claimed are necessary to solve said tasks. You can find this claim in Chollet's white paper that introduced ARC, linked below: On the…

      YeGoblynQueenneView on HNParent context

    • > The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, concerning priors, experience, and generalization difficulty. (Chollet, 2019, https://arxiv.org/pdf/1911.01547.pdf) Priors here means how targeted is the model design to the…

      visargaView on HNParent context

    • A little surprised that neither the paper nor the discussion here cite Francois Chollet's 2019 paper on defining intelligence for machines [1], which talks explicitly about visual reasoning and proposed an RPM-like visual task as a benchmark. I found it very useful for thinking…

      levocardiaView on HNParent context

    • > IQ tests for models seem...somewhat flawed. They are for all the reasons you explain. Here's a wonderful paper (which I probably found here on HN) on the subject: On the Measure of Intelligence, by Francois Chollet (working for Google), from 2019…

      TacticalCoderView on HNParent context

  4. The IQ Test That AI Can't Pass 1 comment
    • A paper linked in TFA: https://arxiv.org/pdf/1911.01547.pdf Start at page 48 from this paper to see lots of cool tests, totally trivial for us humans to solve, that apparently AI have issues with. The paper is from late 2019: I wonder how multi-modal current AIs do on these…

      TacticalCoderView on HNParent context

    • You know, François Chollet literally did this, and people don't listen. People should listen to Chollet more. https://twitter.com/fchollet/status/1638643323748618240 https://arxiv.org/abs/1911.01547

      sanxiynView on HNParent context

  5. What Is AGI-Hard 1 comment
    • To add to it: a recent attempt at designing a psychometric test encoding human cognitive priors: https://arxiv.org/abs/1911.01547 - the ARC dataset. https://github.com/google/BIG-bench - the most recent kitchen sink of a cognitive test, which includes the previous test as a…

      sinenomineView on HNParent context

    • > I don't think we are near general AI Nor do I. But I don't agree with your definition of general AI at all. I think by your definition we are well on the way towards it. I'd note that you didn't address the idea that compression of input concepts into a lower dimensional space…

      nlView on HNParent context

  6. The Problem with Intelligence 1 comment
    • > makes the best possible decision about the future with the information that it has available So would a good thermostat be considered intelligent, assuming it takes great decisions? Intelligence needs to be defined with respect to a range of tasks and a range of priors. It's…

      visargaView on HNParent context

    • > Intelligence is the ability to achieve goals A more precise definition by François Chollet: The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty…

      visargaView on HNParent context

    • Ability to solve novel problems with little experience. Skill acquisition efficiency. On the Measure of Intelligence, François Chollet https://arxiv.org/abs/1911.01547

      visargaView on HNParent context

    • Very Interesting. This reminds me of The Abstraction and Reasoning Corpus [1] by François Chollet, accompanying his paper On the Measure of Intelligence [2]... Edit: Found a recent article mentioning both and discussing a NeurIPS paper on using Bongard problems to test AI…

      yamrzouView on HNParent context

  7. Why AI is harder than we think 1 comment
    • In the HANS paper I'd point out this bit: "we retrained each model on the MNLI training set augmented with a dataset structured exactly like HANS (i.e. using the same thirty subcases) but containing no specific examples that appeared in HANS.... In general, the models trained on…

      nlView on HNParent context

    • On the Measure of Intelligence, François Chollet [1] Fellow HNer seems to have liked a lot of ML paper, this is not breaking the trend. This is a great meta paper questioning the goal of the field itself, and proposing ways to formally evaluate intelligence in a computational…

      m3atView on HNParent context

    • I think GPT-3 is amazing, and the equal of humans in many cases. But I think that this is because in many cases humans speak based on reflex and without thought. GPT-3 is incapable of abstract thought. In terms of actual measurable tests it doesn't pass the kinds of tests…

      nlView on HNParent context

  8. Neural Abstract Reasoner 1 comment
    • Claims 78.8% on the Abstract Reasoning Corpus [1] (ARC) in the abstract, but clarifies that this is only on 100 out of the 400 evaluation tasks, all on 10 x 10 grids. The 10x10 grids are hard-coded in the network architecture so it's not possible for the system to perform any…

      YeGoblynQueenneView on HNParent context

  9. AI Expert Roadmap 1 comment
    • Deep neural nets do not learn only from examples! They encode strong inductive biases in their carefully hand-engineered and hand-tuned architectures, hence for example CNNs are used for image recognition and LSTMs for sequence learning etc. Without these biases deep neural nets…

      YeGoblynQueenneView on HNParent context

    • Francois Chollet tries to address some of these problems by tackling how intelligence is measured. Most AI systems are measured by task performance, but he argues that intelligence is about "skill-acquisition efficiency". https://arxiv.org/abs/1911.01547

      andra1View on HNParent context

    • > There is a lot of debate over the definition of intelligence but one thing most definitions have in common is that it is general. A human being can play the piano, solve an integral, write a novel, compose a photograph, and many many more things. There is a nice paper on this…

      visargaView on HNParent context

    • If someone wants to go as far as to claim that any computation is just curve-fitting then your statement is equivalent to Church–Turing thesis. There are no formal arguments against Church–Turing thesis. From that perspective intelligence is indeed just a curve fitting.…

      IsinlorView on HNParent context

    • I'm not sure this is necessarily true. At a smaller scale, we didn't need to understand how humans play chess in order to build a good chess AI. I do think we need a better understanding of what we mean when we talk about intelligence, if we expect to recreate it. Francois…

      glialView on HNParent context

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