Building AI reasoning systems for software
Our research is rooted in leveraging neurosymbolic methods and reinforcement learning in verifiable domains to build intelligent systems able to solve human-level problems.
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Using neurosymbolic reasoning, reinforcement learning and process-guided search to...
ARC-AGI
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Reinforcement Learning from Machine Feedback
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Further Reading
What Our Team is Saying
I believe that anyone with new ideas and ability to accelerate AGI should be working on it. The world deserves it. Chollet and I have tried to support this goal indirectly. We’ve both invested in AGI startups, created @arcprize written papers, and given talks to inspire open progress.
Neurosymbolic methods are the key to building systems that can reason. We are actively researching and applying methods that can synthesize digital solutions from human language at scale, learn from feedback and in a cost-effective way. Similar to AlphaGo for the game of Go.
I believe that anyone with new ideas and ability to accelerate AGI should be working on it. The world deserves it. Chollet and I have tried to support this goal indirectly. We’ve both invested in AGI startups, created @arcprize written papers, and given talks to inspire open progress.
Neurosymbolic methods are the key to building systems that can reason. We are actively researching and applying methods that can synthesize digital solutions from human language at scale, learn from feedback and in a cost-effective way. Similar to AlphaGo for the game of Go.
Scientific Advisors
Head of Open-Endedness Team at Google Deepmind
Staff Research Scientist at Google Deepmind
AI Research Scientist at Meta
The future cannot wait, come build with us.
If working on complex problems with a world class team and the questions below resonate with you, we want to hear from you.
How do we build a Turing-complete system and unlock reasoning for software?
How does a higher abstraction-level programming interface look like?
How does a world where billions of people can create software look like?