> I still expect decent results (>50%) on ARC benchmark soon (this year)

What gives you this confidence? What is your expertise in ML? Have you trained systems? Developed architectures? Do you know why the systems currently fail?

> now that the AI community has noticed it.

Which community? The researchers or public? The researchers have known if for quite some time. The previous contest as famous and so is Francis. Big labs have tried to tackle ARC for quite some time. You just don't see negative results.

> I don't really see what ARC benchmark has to do with AGI

ARC is a reasoning test. Which is quite different from all the LLM tests you likely have seen, which are memory tests. The problem is most people are not aware of what the models have been trained on. GI involves memory, it involves reasoning, it involves a lot of things.

> I think they might still get to 50% with some finetuning and other tricks, but I would not even try any lesser models.

And how do you have this confidence? Are you guessing? Have you tried? Because I can tell you that others have. Even before the prize was announced. And I hope you realize there's a lot of models that do in fact do next frame prediction. People have trained multimodal models on ARC.

There's quite a lot of assumptions by many that it just hasn't been tried. But it's a baseless assumption with evidence to the contrary. Look into it yourself before making such claims.

> I've heard some very smart people proposing complex approaches towards building AGI (Lecun, Bengio, Jeff Hawkins, etc), yet scaling up deep learning models is still the best one we have today.

These are not in contention so I'm not sure what your argument is.

> If Chollet believes in his hybrid, whatever it is, he should build some sort of a prototype/PoC. Why hasn't he?

I'm sorry, but I'm going to say this is a dumb question. He's trying. A lot of us are. But clearly there's unsolved problems. The logic doesn't follow from your question. We still don't know how to conceptually build a brain. But there's many things we conceptually know how to build but still can't. We conceptually know how to build space elevators but we don't know how to build all the pieces to actually make them even if we had infinite money.

And I'll ask you a similar question: if scale is all you need then why don't we have AGI now?

There may be parts to this question you don't know. We don't train multiple epochs for LLMs. LLM architecture has been rapidly changing despite maintaining the general structure of transformers (but they aren't your standard transformers and reading the AIAYN paper won't get you there). And if scale was all you needed then shouldn't Google be leading the way? Certainly they have more data and compute than anyone else. In fact, I'd argue that this is why they do so poorly and why LLMs are getting worse at the same time they're getting better.

> the good news is most of academic AI labs today don't have the money to scale up transformers, so they are probably trying out all these other ideas.

The unfortunate news is when you propose some other architecture it gets lambasted in review because they do not perform state of the art and I've had SOTA papers get rejected due to "lack of experiments" which is equivalent to lack of compute. There's a railroad and lots of academic funding comes from big tech, not universities or government. Go look at the affiliations of academic authors. Go to the papers and you'll see.

> So you're not worried about impending mass unemployment, ok

Oh, I'm worried. More worried about displacement. You know how things sucked when everything got outsourced? Because they just cut corners, do the absolute bare minimum, and how they won't consider anything that makes any sense just because there's rules in place that were not correctly created but are strictly followed? Get ready for that to be much worse.