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# Lessons from Francois Chollet
- URL: https://www.antoinebuteau.com/lessons-from-francois-chollet/
- Published: 2025-10-08T17:07:58.000Z
- Updated: 2026-10-14T18:17:44.000Z
- Description: François Chollet created Keras and writes about intelligence, generalization, and the limits of benchmark-driven AI. These lessons draw on his essays and interviews.
- Author: Antoine Buteau
- Tags: Profile, AI, AI & Machine Learning Profiles

François Chollet created Keras, introduced the ARC-AGI challenge and is a co-founder of the Ndea research lab. His writing and interviews distinguish performance on familiar tasks from the ability to acquire skills for genuinely new ones. — [Deep Learning with Python — About the Book](https://deeplearningwithpython.io/about/?ref=antoinebuteau.com).

![Infographic for "Lessons from Francois Chollet".](https://www.antoinebuteau.com/content/images/2026/07/lessons-from-francois-chollet-optimized.webp)

## On the Nature of Intelligence

1. **Skill Acquisition, Not Stored Skill:** In his 2019 framework, intelligence concerns how efficiently a system acquires new skills across a stated range of tasks, accounting for its priors, experience and the difficulty of generalization. — [On the Measure of Intelligence](https://arxiv.org/html/1911.01547?ref=antoinebuteau.com).
2. **Human Generalization:** In a 2017 essay, Chollet contrasted humans’ ability to adapt to radically unfamiliar situations with the more local generalization of the deep-learning systems he described at the time. — [The Limitations of Deep Learning](https://blog.keras.io/the-limitations-of-deep-learning.html?ref=antoinebuteau.com).
3. **Handle Novel Situations:** Chollet treats adaptation to genuinely new situations as a central test of intelligence: the learner must form a useful model for the situation rather than retrieve a rehearsed answer. — [Machine Learning Street Talk Interview](https://www.youtube.com/watch?v=JTU8Ha4Jyfc&ref=antoinebuteau.com).
4. **Separate Intelligence From Goals:** Chollet distinguishes intelligence—the capacity to acquire useful skills—from an agent’s goals or autonomy. Giving a system a goal does not by itself establish that it can generalize. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
5. **Generate New Solutions:** His account separates an intelligent process that can develop a new task-solving program from the finished program that performs a learned task; high task skill alone does not show broad generalization. — [On the Measure of Intelligence](https://arxiv.org/html/1911.01547?ref=antoinebuteau.com).
6. **Consciousness as a Gradual Process:** In a speculative discussion, Chollet proposed that consciousness develops gradually in early childhood as a child builds models of self and world. He called this his theory, not an established developmental result. — [Machine Learning Street Talk Interview](https://www.youtube.com/watch?v=JTU8Ha4Jyfc&ref=antoinebuteau.com).
7. **Degrees of Consciousness:** Chollet said he believes consciousness may exist in degrees and that at least some animals may experience it. This is his tentative philosophical view, not a measured scale. — [Machine Learning Street Talk Interview](https://www.youtube.com/watch?v=JTU8Ha4Jyfc&ref=antoinebuteau.com).

## On Large Language Models (LLMs) and AGI

1. **Useful on Familiar Tasks:** In 2024 Chollet argued that large language models can be extremely useful on tasks resembling their training experience, even while that usefulness does not settle how they handle unfamiliar problems. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
2. **Do Not Equate LLMs With AGI:** In a 2024 interview, Chollet rejected claims that then-current language models already constituted AGI or had human-like general intelligence. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
3. **Scaling Is Not the Whole Path:** Chollet argued in 2024 that merely scaling the available approaches would not, in his view, yield AGI; achieving broader intelligence would require a new idea. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
4. **Test Outside Familiar Distributions:** Chollet’s criticism of language models focuses on tasks that differ materially from what a system has encountered: strong familiar-task performance need not imply comparable performance on novel tasks. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
5. **A Database Is Not a Program Generator:** In the 2024 Mindscape interview, Chollet distinguished models that can retrieve or combine learned patterns from a system that can synthesize a new solution program for a novel task. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
6. **Stored Programs Do Not Suffice:** Chollet argued that adding more memorized task programs is different from giving a system the ability to construct a new program when it meets an unfamiliar problem. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
7. **A Scientist-Like AGI:** In a 2024 interview, Chollet described a possible AGI as a highly capable scientist—a vision of what general intelligence might do, not a capability he claimed existing systems had achieved. — [TIME100 AI — François Chollet](https://time.com/7012823/francois-chollet/?ref=antoinebuteau.com).
8. **Define What AI Measures:** Chollet argued that AI research needs an actionable definition and measure of intelligence; task-specific scores alone can mask the effects of prior knowledge and training experience. — [On the Measure of Intelligence](https://arxiv.org/html/1911.01547?ref=antoinebuteau.com).
9. **Question the Intelligence-Explosion Story:** In a signed essay, Chollet challenged simple intelligence-explosion narratives, arguing that recursive improvement is constrained by the nature of intelligence and problem solving. — [The Impossibility of Intelligence Explosion](https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec?ref=antoinebuteau.com).

## On Machine Learning and Deep Learning

1. **Learn Useful Representations:** The authors compare deep learning to unfolding a crumpled paper ball: successive transformations can make intertwined data classes easier to separate. — [Deep Learning with Python — Chapter 2](https://deeplearningwithpython.io/chapters/chapter02%5Fmathematical-building-blocks/?ref=antoinebuteau.com).
2. **Layered Representation Learning:** The authors explain deep learning as learning successive layers of useful representations from data, with each layer transforming the input for a task. — [Deep Learning with Python — Chapter 1](https://deeplearningwithpython.io/chapters/chapter01%5Fwhat-is-deep-learning/?ref=antoinebuteau.com).
3. **Check Whether Inputs Can Predict Targets:** Before fitting a model, ask whether the available inputs contain information about the intended target; examples of X and Y cannot solve a problem if X is not predictive of Y. — [Deep Learning with Python — Chapter 6](https://deeplearningwithpython.io/chapters/chapter06%5Funiversal-workflow-of-ml/?ref=antoinebuteau.com).
4. **Data Work Matters:** In his 2017 outlook, Chollet described data preparation and architecture or hyperparameter tuning as substantial parts of deep-learning practice at that time. — [The Future of Deep Learning](https://blog.keras.io/the-future-of-deep-learning.html?ref=antoinebuteau.com).
5. **Initial Hyperparameters Need Testing:** Even experienced practitioners’ first architecture and hyperparameter choices are usually not optimal; empirical validation is needed to improve them. — [Deep Learning with Python — Chapter 18](https://deeplearningwithpython.io/chapters/chapter18%5Fbest-practices-for-the-real-world/?ref=antoinebuteau.com).
6. **Automate the Search, Keep Judgment:** Systematic hyperparameter search can replace repetitive manual tweaking, but practitioners still need to design a sensible search space and guard against overfitting the validation set. — [Deep Learning with Python — Chapter 18](https://deeplearningwithpython.io/chapters/chapter18%5Fbest-practices-for-the-real-world/?ref=antoinebuteau.com).
7. **Combine Formal and Geometric Methods:** In a 2017 essay, Chollet proposed a research direction combining formal reasoning and abstraction modules with learned pattern-recognition modules; he presented it as a forecast, not an inevitable result. — [The Future of Deep Learning](https://blog.keras.io/the-future-of-deep-learning.html?ref=antoinebuteau.com).

## On Evaluation and Benchmarking

1. **Use Novel Tasks to Test Intelligence:** Chollet argues that an intelligence benchmark should require unfamiliar tasks whose solutions cannot simply have been memorized in advance. — [On the Measure of Intelligence](https://arxiv.org/html/1911.01547?ref=antoinebuteau.com).
2. **Design ARC to Resist Memorization:** ARC was designed around explicitly stated human-like priors and unfamiliar tasks so success would test adaptation rather than only rehearsed task skill; leakage or training on test items remains a separate concern. — [On the Measure of Intelligence](https://arxiv.org/html/1911.01547?ref=antoinebuteau.com).
3. **Test Research Claims Empirically:** In a direct interview, Chollet argued that strong ML research needs meaningful baselines, ablation studies, rigorous validation and significance testing rather than inflated results. — [PyImageSearch Interview](https://pyimagesearch.com/2018/07/02/an-interview-with-francois-chollet/?ref=antoinebuteau.com).
4. **Iterate with a reliable evaluation loop:** Chollet argues that successful Kaggle teams improve by testing and refining their approaches repeatedly, guided by reliable evaluation. He describes an ideation–experiment–analysis loop; he does not report a measured correlation between experiment counts and leaderboard rank. — [François Chollet — The Loop of Progress](https://fchollet.substack.com/p/the-loop-of-progress).
5. **Past Prices May Not Predict Future Returns:** The authors caution that a model trained on historical data assumes the future will behave like the past; market-price history is a case where that assumption may fail. — [Deep Learning with Python — Chapter 6](https://deeplearningwithpython.io/chapters/chapter06%5Funiversal-workflow-of-ml/?ref=antoinebuteau.com).

## On the Future of AI and its Impact

1. **Research Should Create Knowledge:** Chollet criticized PR-driven projects and publication incentives that neglect what can actually be learned; he described creating knowledge as the purpose of research. — [PyImageSearch Interview](https://pyimagesearch.com/2018/07/02/an-interview-with-francois-chollet/?ref=antoinebuteau.com).
2. **Give Users More Agency:** In a personal essay, Chollet urged developers to use AI to expand users’ agency rather than manipulate their behavior. He framed this as a concern and design preference, not a demonstrated outcome. — [What Worries Me About AI](https://medium.com/@francois.chollet/what-worries-me-about-ai-ed9df072b704?ref=antoinebuteau.com).
3. **Build Ethics Into Design:** Chollet urged software makers to consider their values during design, so ethical choices are part of what they build rather than an afterthought. — [Notes to Myself on Software Engineering](https://medium.com/@francois.chollet/notes-to-myself-on-software-engineering-c890f16f4e4d?ref=antoinebuteau.com).
4. **Locate the Intelligence in the Builder:** For a fixed hand-coded program, Chollet located the adaptive intelligence in the human programmer who designed it, rather than in the program’s already specified task behavior. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
5. **Hybrid AI Was a Research Forecast:** Chollet’s 2017 outlook envisioned learned systems that combine formal algorithmic operations with geometric pattern learning. This was a research proposal, not a prediction of a guaranteed future architecture. — [The Future of Deep Learning](https://blog.keras.io/the-future-of-deep-learning.html?ref=antoinebuteau.com).
6. **AGI Timing Is Uncertain:** Chollet said in 2024 that if broader intelligence requires a new invention, its arrival cannot be reliably forecast from scaling curves alone. — [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com).
7. **Care About API Users:** In his API design essay, Chollet argued that good developer tools begin with attention to users’ needs; practical design rules follow from that principle. — [User Experience Design for APIs](https://blog.keras.io/user-experience-design-for-apis.html?ref=antoinebuteau.com).

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Learn more:

1. [On the Measure of Intelligence](https://arxiv.org/html/1911.01547?ref=antoinebuteau.com)
2. [ARC Prize — What Is ARC-AGI?](https://arcprize.org/arc-agi?ref=antoinebuteau.com)
3. [Machine Learning Street Talk Interview](https://www.youtube.com/watch?v=JTU8Ha4Jyfc&ref=antoinebuteau.com)
4. [François Chollet — Personal Site](https://fchollet.com/?ref=antoinebuteau.com)
5. [The Limitations of Deep Learning](https://blog.keras.io/the-limitations-of-deep-learning.html?ref=antoinebuteau.com)
6. [What Worries Me About AI](https://medium.com/@francois.chollet/what-worries-me-about-ai-ed9df072b704?ref=antoinebuteau.com)
7. [The Impossibility of Intelligence Explosion](https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec?ref=antoinebuteau.com)
8. [Mindscape #280 Transcript](https://preposterousuniverse.com/podcast/2024/06/24/280-francois-chollet-on-deep-learning-and-the-meaning-of-intelligence/?ref=antoinebuteau.com)
9. [TIME100 AI — François Chollet](https://time.com/7012823/francois-chollet/?ref=antoinebuteau.com)
10. [Deep Learning with Python — Chapter 1](https://deeplearningwithpython.io/chapters/chapter01%5Fwhat-is-deep-learning/?ref=antoinebuteau.com)
11. [Deep Learning with Python — Chapter 2](https://deeplearningwithpython.io/chapters/chapter02%5Fmathematical-building-blocks/?ref=antoinebuteau.com)
12. [Deep Learning with Python — Chapter 6](https://deeplearningwithpython.io/chapters/chapter06%5Funiversal-workflow-of-ml/?ref=antoinebuteau.com)
13. [PyImageSearch Interview](https://pyimagesearch.com/2018/07/02/an-interview-with-francois-chollet/?ref=antoinebuteau.com)
14. [Notes to Myself on Software Engineering](https://medium.com/@francois.chollet/notes-to-myself-on-software-engineering-c890f16f4e4d?ref=antoinebuteau.com)
15. [The Future of Deep Learning](https://blog.keras.io/the-future-of-deep-learning.html?ref=antoinebuteau.com)
16. [User Experience Design for APIs](https://blog.keras.io/user-experience-design-for-apis.html?ref=antoinebuteau.com)
17. [Deep Learning with Python — Chapter 18](https://deeplearningwithpython.io/chapters/chapter18%5Fbest-practices-for-the-real-world/?ref=antoinebuteau.com)
18. [Deep Learning with Python — About the Book](https://deeplearningwithpython.io/about/?ref=antoinebuteau.com)
19. [Deep Learning with Python — Chapter 5](https://deeplearningwithpython.io/chapters/chapter05%5Ffundamentals-of-ml/?ref=antoinebuteau.com)