Michael Levin

They're in spite of the algorithm.

They are filling up the spaces in between.

There's what the algorithm is forcing you to do, and then there's the other cool stuff it's doing, which is nowhere in the algorithm.

And if that's true, and we think it's true even of very minimal systems, then this whole business of language models and AIs in general

Watching the language part may be a total red herring because the language is what we force them to do.

The question is, what else are they doing that we are not good at noticing?

And this is something that we are, I think, as a kind of existential step for humanity is to become better at this because we are not good at recognizing these things now.

Sure.

First, just the goal of the study.

There are two things that people generally assume.

One is that we have a pretty good intuition about what kind of systems are going to have competencies.

So from observing biologicals, we're not terribly surprised when biology does interesting things.

Everybody always says, well, it's biology.

Of course, it does all this cool stuff.

But we have these machines and the whole point of having machines and algorithms and so on is they do exactly what you tell them to do, right?

And people feel pretty strongly that that's a binary distinction and that that's what...

that we can carve up the world in that way.

So I wanted to do two things.

I wanted to, first of all, explore that and hopefully break the assumption that we're good at seeing this, because I think we're not.

And I think it's extremely important that we understand very soon that we need to get much better at knowing when to expect these things.