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

(ARC Prize co-founder here).

Ryan's work is legitimately interesting and novel "LLM reasoning" research! The core idea:

> get GPT-4o to generate around 8,000 python programs which attempt to implement the transformation, select a program which is right on all the examples (usually there are 3 examples), and then submit the output this function produces when applied to the additional test input(s)

Roughly, he's implemented an outer loop and using 4o to sample reasoning traces/programs from training data and test. Hybrid DL + program synthesis approaches are solutions we'd love to see more of.

A couple important notes:

1. this result is on the public eval set vs private set (ARC Prize $).

2. the current private set SOTA ~35% solution also performed ~50% on the public set. so this new result might be SOTA but hasn't been validated or scrutinized yet.

All said, I do expect verified public set results to flow down to the private set over time. We'll be publishing all the SOTA scores and open source reproductions here once available: https://arcprize.org/leaderboard

EDIT: also, congrats and kudos to Ryan for achieving this and putting the effort in to document and share his approach. we hope to inspire more frontier AI research sharing like this

Ah, give it a rest. That's not "frontier AI research", neither is it any kind of reasoning. It's the dumbest of the dumb possible generate-and-test approach that spams a fire hose of Python programs until it hits one that works. And still it gets only 50% on the public eval.

How many thousands of Python programs does a human need to solve a single ARC task? That's what you get with reasoning: you don't need oodles of compute and boodles of sampling.

And I'm sorry to be so mean, but ARC is a farce. It's supposed to be a test for AGI but its only defense from a big data approach (what Francois calls "memorisation") is that there are few examples provided. That doesn't make the tasks hard to solve with memorisation it just makes it hard for a human researcher to find enough examples to solve with memorisation. Like almost every other AI-IQ test before it, ARC is testing for the wrong thing, with the wrong assumptions. See the Winograd Schema Challenge (but not yet the Bongard problems).

Do you have any suggestions for a better approach of testing artificial intelligence? I mean, in a way that allows comparing different approaches and being a reasonable metric of progress.

I don't. I'm guessing -and it's nothing but a guess- that for every problem that can be solved with intelligence there exists a solution that does not require intelligence. I'm guessing in other words that intelligence is the ability to come up with solutions to arbitrary problems. If that's true then there's no way to test for intelligence by looking at the performance of a system at any particular task, or any finite set of tasks, and so there's no way to create a "test for intelligence".

My guess is supported by the experience that, in AI research, every time someone came up with a plausible test for intelligence, an AI system eventually passed the test only to make it clear that the test was not really testing intelligence after all (edit: I don't just mean formal tests; e.g. see how chess used to "require intelligence" right up until Deep Blue vs Kasparov).

Some people see that as "moving the goalposts" and it's certainly frustrating but the point is that we don't know what intelligence is, exactly, so it's very hard to test for its existence or not, or to measure it.

My preference would be for everyone in AI research to either stop what they're doing and try to understand what the hell intelligence is in the first place, to create a theory of intelligence so that AI can be a scientific subject again, or to at least admit they're not interested in creating artificial intelligence. I, for example, am not, but all my background is in subjects that are traditionally labelled "AI" so I have to suck it up, I guess.

You're basically paraphrasing fchollet's paper on intelligence and what he talked about in his most recent podcast appearance with dwarkesh.

I watched the podcast you're referencing, on youtube, but I don't remember Chollet saying anything like what I say above.

Quite the contrary, Chollet seems convinced that a test for artificial intelligence, like an IQ test for AI, can be created and he has not only created one but also organised a Kaggle competition on it, and now is offering a $1 million prize to solve it. So how is anything he says or does compatible with what I say above, that it's likely there can't be a test for artificial intelligence?

You clearly didn't listen much since Chollet's point is exactly that there is no one task that can test for AI. The test he created is supposed to take that into account.