Getting 50% (SoTA) on Arc-AGI with GPT-4o

The Arc stuff just felt intuitively wrong as soon as I heard it. I don't find any of Chollet's critiques of LLMs to be convincing. It's almost as if he's being overly negative about them to make a point or something to push back against all the unbridled optimism. The problem is, the optimism really seems to be justified, and the rate of improvement of LLMs in the past 12 months has been nothing short of astonishing.

So it's not at all surprising to me to see Arc already being mostly solved using existing models, just with different prompting techniques and some tool usage. At some point, the naysayers about LLMs are going to have to confront the problem that, if they are right about LLMs not really thinking/understanding/being sentient, then a very large percentage of people living today are also not thinking/understanding/sentient!

> I don't find any of Chollet's critiques of LLMs to be convincing. It's almost as if he's being overly negative about them to make a point or something to push back against all the unbridled optimism.

Chollet published his paper On the measure of intelligence in 2019. In Internet time that is a lifetime before the LLM hype started.

Einstein, infamously, couldn't really make much progress with quantum physics, even though he invented the precursors (ex. Brownian motion). Your world model is hard to update.

A bit of a stretch given that Chollet is a researcher in deep learning and transformers and his criticism is that memorization (training LLMs on lots and lots of problems) doesn't equate to AGI.

> A bit of a stretch

Is that true?

C.f. what we're discussing

He's actively encouraging using LLMs to solve his benchmark, called ARC AGI.

8 hours ago, from Chollet, re: TFA

"The best solution to fight combinatorial explosion is to leverage intuition over the structure of program space, provided by a deep learning model. For instance, you can use a LLM to sample a program..."

Source: https://x.com/fchollet/status/1802801425514410275

The stretch was in reference to comparing Chollet to Einstein. Chollet clearly understands LLMs (and transformers and deep learning), he simply doesn't believe they are sufficient for AGI.

I don't know what you mean, it's a straightforward analogy, but yes, that's right, except for the part where he's heralding this news by telling people the LLM is an underexplored solution space for a possible solution to his AGI benchmark he made to disprove LLMs are AGI.

I don't mean to offend, but to be really straightforward: he's the one saying it's possible they might be AGI now. I'm as flummoxed as you, but I think its hiding the ball to file it under "he doesn't mean what he's saying, because he doesn't believe LLMs can ever be AGI." The only steelman for that is playing at: AGI-my-benchmark, which I say is for AGI, is not the AGI I mean

You're reading a whole lot into a tweet, in his interview with Dwarkesh Patel he says, about 20 different times, that scaling LLMs (as they are currently conceived) won't lead to AGI.