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A new intelligence science lab founded by @fchollet & @mikeknoop. Deep Learning-guided Program Synthesis. We're hiring.

Joined October 2024
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ARC Prize Research Summit 2026 October 23, 2026 - Boston, MA The ARC Prize community comes together to share new work, challenge ideas, and advance the frontier of machine reasoning towards general intelligence
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Francois Chollet + Sam Altman Fireside @fchollet and @sama fireside during ARC-AGI-3 Launch Party moderated by @deedydas They discuss: - Social contracts evolving - AGI views as a parent - When will labs score >85% on ARC-AGI-3?
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François Chollet (@fchollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the @LightconePod, he traces that path from his early work on deep learning to the creation of the @arcprize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI
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The field of program synthesis is growing but is still under-producing the amount of talent I think we'll need to craft future AI systems. Our goal with Abstract Synthesis is to steer interest by highlighting frontier symbolic research and useful open problems.
Introducing the Ndea podcast - Abstract Synthesis. Hear the stories behind interesting academic papers in the world of program synthesis. Episode 1 features @MarkSantolucito, @BarnardCollege/@Columbia, discussing his paper "Grammar Filtering for Syntax-Guided Synthesis".
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.@fchollet + @mikeknoop fireside chat @ MIT Listen to ARC Prize Co-Foundres, Francois Chollet + Mike Knoop talk about ARC-AGI-3, game development, and measuring intelligence with Interactive Benchmarks youtu.be/1u2DkoqEfhk
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The way to think about AGI is as a scalable, efficient formalization & implementation of the scientific method. Not a brain in a jar.
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Small teams are shaping the future of AI. Here's why @mikeknoop, co-founder of @ndea, @arcprize, and @zapier believes AGI will be defined by teams all around the world, not just the big labs.
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Worth noting that the output artifacts of science -- the models it produces -- are symbolic in nature. Most commonly expressed in mathematical form, sometimes in code. Science is a program synthesis process.
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Excited to host our first-ever AI Startup School in San Francisco next month! We're bringing together an incredible speaker lineup of the world's top AI founders and experts: @sama · @fchollet · @chelseabfinn · @JohnJumperSci · Jared Kaplan · @karpathy · @drfeifei · @elonmusk · @satyanadella · @AndrewYNg · @AravSrinivas and more!
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Beyond Brute Force: my conversation with star AI researcher @fchollet and Zapier’s @mikeknoop * ARC AGI 2 and why it breaks base LLMs * test-time adaptation * the search for true machine intelligence All links in reply to this post below
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We just dropped a fun panel discussion with @fchollet @ZennaTavares and @ellisk_kellis on program synthesis. This was a powerhouse conversation with three legends in the field, enjoy.
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We spoke with @ClementBonnet16 at NeurIPS about his extremely innovative approach to the @arcprize using a form of test time inference where you search a latent space of a VAE before making an optimal prediction. @fchollet was so impressed, he hired Clem shortly after! 😃 - Dropping on MLST tomorrow.
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