
3 segments available
Full Episode: https://youtu.be/UakqL6Pj9xo Transcript: https://www.dwarkeshpatel.com/p/francois-chollet Apple Podcasts: https://podcasts.apple.com/us/podcast/francois-chollet-mike-knoop-llms-wont-lead-to-agi-%241/id1516093381?i=1000658672649 Spotify: https://open.spotify.com/episode/7bmeJQOvXGy4LYl6YoiYYP?si=obUSUEwjSA6tkB8EBcb18w Follow me on Twitter: https://x.com/dwarkesh_sp
Francois Chollet discusses the implications of a multimodal model achieving 80% on the ARC benchmark, suggesting that this could indicate a step towards Artificial General Intelligence (AGI). He emphasizes the challenge of creating a perfect benchmark that cannot be anticipated, highlighting the importance of genuine intelligence over brute-force memorization.
"so suppose that it's the case that in a year a multimodal model can solve Arc let's say get 80% whatever the average human would get then AGI quite possibly yes I think if you if you start so honestly..."
Chollet elaborates on the concept of intelligence as a pathfinding algorithm in uncertain future situations. He draws parallels to game development, explaining how intelligence relies on past experiences and partial information to navigate unknown territories, contrasting this with the limitations of pure memorization.
"know with enough scale you can always cheat if you can do this for every single thing that supposedly requires intelligence then what good is intelligence apparently you can just Brute Force intellige..."
In this segment, Chollet critiques the notion of memorization in both human learning and LLMs. He argues that while humans learn through a combination of memorization and reasoning, LLMs operate similarly, raising questions about the nature of intelligence and the potential for automation in a world where skills can be synthesized from data.
"simply memorizing every possible path every mapping from uh point A to point B uh you could you could solve the problem with pure memory but where the reason you cannot do that in real life is because..."