Francois Chollet

And I see LLMs as more of an off-ramp on the path to a GR actually.

And all these new resources, they're actually going to LLMs instead of everything else they could be going to.

And, you know, if you look further into the past to like 2015, 2016, there were like a thousand times fewer people doing AI back then.

And yet I feel like the rate of progress was higher because people are exploring more directions.

the world felt more open-ended.

Like you could just go and try, like have a cool idea of a launch and try it and get some interesting results.

So there was this energy.

And now everyone is very much doing some variation of the same thing.

And the big labs also tried their hand on ARC, but because they got bad results, they didn't publish anything.

Like, you know, people only publish positive results.

I wonder how much...

That's an empirical question.

So we'll see in practice what happens.

But what Jack Cole is doing is actually very unique.

It's not just pre-training an LLM and then prompting it.

He's actually trying to do active inference.

He's doing test time, right?

He's doing test time fine-tuning.

Test time fine-tuning.

And this is actually trying to lift one of the key limitations of LLMs, which is that at inference time, they cannot learn anything new.