François Chollet describes the empirical baseline testing conducted with 400 non-specialist human subjects on the ARC-AGI-2 benchmark suite.
Prediction Open · timeframe Mar 2031
Chollet: AGI Codebase Will Be Under 10,000 Lines on 1980s Compute
“I do believe that, you know, when you create a GI retrospectively, it will turn out that it's a code base that's less than 10,000 lines of code. And that if you had known about it back in the 19 eighties, you could have done a GI back then using the computer r…”
Prediction Not checkable as stated
Chollet: Symbolic Models Will Eventually Replicate and Outperform Deep Learning
“And so everything you're doing with machine learning today, with parametric curves, we should be able to do it. With symbolic models in the future in a way that will be much, much closer to optimality. Much closer to optimality in the sense that you're going t…”
Prediction Open · timeframe Mar 2076
Chollet: AI in 50 Years Will Not Use Today's LLM Stack
“I personally don't think that machine learning or AI in 50 years is still going to be built on this stack.”
Insight
Chollet: Gradient Descent Fails at Reasoning by Defaulting to Pattern Matching
“You could not really get Gradient descent to encode sort of like reasoning style algorithms. It was not because the models could not represent these algorithms. It was because gradient descent could not find them, right? So the problem was that it wasn't about…”
Opinion
Chollet: AI field is obsessed with idea that scale alone yields AGI
“Our field became obsessed with the idea that general intelligence would spontaneously emerge by cramming more and more data into bigger and bigger models.”
Assertion Not checkable as stated
Chollet: All inventive AI systems rely on discrete search
“All known AI systems today that are capable of some kind of invention, some kind of creativity, they rely on discrete search.”