François Chollet: ARC-AGI-3, Beyond Deep Learning & A New Approach To ML
In a Nutshell
François Chollet launches ARC-AGI V3, a benchmark testing agentic intelligence via interactive mini-games that demand efficient exploration, goal-setting, and planning in novel environments, resisting brute-force scaling seen in V1/V2 saturation by coding agents. Through NDIA, he pursues symbolic descent—replacing gradient descent with program synthesis for concise, optimal symbolic models that need far less data, generalize better, and emulate human-like skill acquisition efficiency toward AGI by 2030. Deep learning excels in verifiable domains like code but falls short on true fluid intelligence; future AGI will be a compact, self-improving codebase leapfrogging current stacks, with advice to explore discarded 1970s/80s AI ideas and build scalable open-source projects with intuitive APIs.
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AGI around 2030, coinciding with ARC 6 or 7 release. AI progress cannot be stopped and will accelerate. Question is how to make use of it and ride the wave.
François Chollet, founder of ARC Prize, global competition to solve ARC-AGI benchmark. Latest project: NDIA, lab exploring new paradigm in frontier AI research. Congrats on ARC-AGI V3 launch.
Thanks so much for having me. I'm super excited to be here. Super exciting time to talk about AI.
NDIA is new AGI research lab trying different ideas to build new branch of machine learning much closer to optimal, unlike deep learning.
Current focus on coding agents; speaker's GStack open source project reached 40,000 stars with over 100 PRs from contributors.
NDIA does program synthesis research at much lower level than codegen or coding agents. Building alternative to deep learning itself, rebuilding the whole stack on different foundations: new learning substrate different from parametric deep learning.
Machine learning: input data to target data, find function mapping inputs to targets that generalizes.
Deep learning: parametric curve fit via gradient descent.
NDIA replaces parametric curve with symbolic model as small as possible (simplest model explaining data). Cannot use gradient descent; instead, symbolic descent (symbolic equivalent of gradient descent).
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