[fchollet]

The reason these tasks require fluid intelligence is because they were designed this way -- with task uniqueness/novelty as the primary goal.

ARC 1 was released long before in-context learning was identified in LLMs (and designed before Transformer-based LLMs existed), so the fact that LLMs can't do ARC was never a design consideration. It just turned out this way, which confirmed our initial assumption.

[danpalmer]

Is there any other confirmation of the assumptions, other than the LLM behaviour, because that still feels like circular reasoning.

I think a similar claim could be levelled against other benchmarks or LLM evaluation tasks. One could say that the Turing test was designed to assess human intelligence, and LLMs pass it, therefore LLMs have human intelligence. This is generally considered to be false now, because we can plainly see that LLMs do not have intelligence in the same way as humans (yet? debatable, not the point), and instead we concluded that the Turing test was not the right benchmark. That's not to diminish its importance, it was hugely important as a part of AI education and possibly even AI development for decades.

ARC does seem to be pushing the boundaries, I'm just not convinced that it's testing a provable step change.

[JFingleton]

I'm not sure that's quite correct about the Turing test. From Wikipedia:

"Turing did not explicitly state that the Turing test could be used as a measure of "intelligence", or any other human quality. He wanted to provide a clear and understandable alternative to the word "think", which he could then use to reply to criticisms of the possibility of "thinking machines" and to suggest ways that research might move forward."

[YeGoblynQueenne]

>> The reason these tasks require fluid intelligence is because they were designed this way -- with task uniqueness/novelty as the primary goal.

That's in no way different than claiming that LLMs understand language, or reason, etc, because they were designed that way.

Neural nets of all sorts have been beating benchmarks since forever, e.g. there's a ton of language understanding benchmarks pretty much all saturated by now (GLUE, SUPERGLUE ULTRASUPERAWESOMEGLUE ... OK I made that last one up) but passing them means nothing about the ability of neural net-based systems to understand language, regardless of how much their authors designed them to test language understanding.

Failing a benchmark also doesn't mean anything. A few years ago, at the first Kaggle competition, the entries were ad-hoc and amateurish. The first time a well-resourced team tried ARC (OpenAI) they ran roughshod over it and now you have to make a new one.

At some point you have to face the music: ARC is just another benchmark, destined to be beat in good time whenever anyone makes a concentrated effort at it and still prove nothing about intelligence, natural or artificial.

[szvsw]

I mostly agree with what your are saying but…

> passing them means nothing about the ability of neural net-based systems to understand language, regardless of how much their authors designed them to test language understanding.

Does this implicitly suggest that it is impossible to quantitatively assess a system’s ability to understand language? (Using the term “system” in the broadest possible sense)

Not agreeing or disagreeing or asking with skepticism. Genuinely asking what your position is here, since it seems like your comment eventually leads to the conclusion that it is unknowable whether a system external to yourself understands language, or, if it is possible, then only in a purely qualitative way, or perhaps purely in a Stewart-style-pornographic-threshold-test - you’ll know it when you see it.

I don’t have any problem if that’s your position- it might even be mine! I’m more or less of the mindset that debating whether artificial systems can have certain labels attached to them revolving around words like “understanding,” “cognition,” “sentience” etc is generally unhelpful, and it’s much more interesting to just talk about what the actual practical capabilities and functionalities of such systems are on the one hand in a very concrete, observable, hopefully quantitative sense, and how it feels to interact with them in a purely qualitative sense on the other hand. Benchmarks can be useful in the former but not the latter.

Just curious where you fall. How would you recommend we approach the desire to understand whether such systems can “understand language” or “solve problems” etc etc… or are these questions useless in your view? Or only useful in as much as they (the benchmarks/tests etc) drive the development of new methodologies/innovations/measurable capabilities, but not in assigning qualitative properties to said systems?

[fchollet]

The first time a top lab spent millions trying to beat ARC was actually in 2021, and the effort failed.

By the time OpenAI attempted ARC in 2024, a colossal amount of resources had already been expended trying to beat the benchmark. The OpenAI run itself costs several millions in inference compute alone.

ARC was the only benchmark that highlighted o3 as having qualitatively different abilities compared to all models that came before. o3 is a case of a good approach meeting an appropriate benchmark, rather than an effort to beat ARC specifically.