Francois Chollet is leaving Google

I wonder what he will be working on?

Maybe he figured out a model that beats ARC-AGI by 85%?

> Maybe he figured out a model that beats ARC-AGI by 85%?

People have, I think.

One of the published approaches (BARC) uses GPT-4o to generate a lot more training data.

The approach is scaling really well so far [1], and whether you expect linear scaling or exponential one [2], the 85% threshold can be reached, using the "transduction" model alone, after generating under 2 million tasks ($20K in OpenAI credits).

Perhaps for 2025, the organizers will redesign ARC-AGI to be more resistant to this sort of approach, somehow.

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[1] https://www.kaggle.com/competitions/arc-prize-2024/discussio...

[2] If you are "throwing darts at a board", you get exponential scaling (the probability of not hitting bullseye at least once reduces exponentially with the number of throws). If you deliberately design your synthetic dataset to be non-redundant, you might get something akin to linear scaling (until you hit perfect accuracy, of course).

I like the idea of ARC-AGI and think it was worth a shot. But if someone has already hit the human-level threshold, I think the entire idea can be thrown out.

If the ARC-AGI challenge did not actually follow their expected graph[1], I see no reason to believe that any benchmark can be designed in a way where it cannot be gamed. Rather, it seems that the existing SOTA models just weren't well-optimized for that one task.

The only way to measure "AGI" is in however you define the "G". If your model can only do one thing, it is not AGI and doesn't really indicate you are closer, even if you very carefully designed your challenge.

[1] https://static.supernotes.app/ai-benchmarks-2.png

What you're calling "gamed" could actually be research and progress in general problem solving.

Almost by definition it is not. If you are "gaming" a specific benchmark, what you have is not progress in general intelligence. The entire premise of the ARC-AGI challenge was that general problem solving would be required. As noted by the GP, one of the top contenders is BARC which performs well by generating a huge amount of training data for this particular problem. That's not general intelligence, that's gaming.

There is no reason to believe that technique would not work for any particular problem. After all, this problem was the best attempt the (very intelligent) challenge designers could come up with, as evidenced by putting $1m on the line.