Michael Levin

The answer might be no, in which case all of the stuff that we've been saying about language models because of what they're saying, all of that could be a total red herring and not really important, and the really exciting stuff is what we never looked for.

Or...

In complex systems, maybe those things become linked.

In biology, they're linked.

In biology, evolution makes sure that the things you're capable of have a lot to do with what you've actually been selected for.

In these things, I don't know.

And so we might find out that they actually do give the language some sort of leg up, or we might find that the language is just, you know, that's not the interesting part.

We're doing all that.

We're doing all that.

We're testing, we're testing the stuff in other algorithms we're looking for.

We're developing suites of code to look for other properties.

We, you know, to some extent it's very hard because we don't know what to look for, but we do have a behaviorist handbook, which tells you what the, all kinds of things to look for.

The delay gratification, the, you know, problem solving, like we have all that.

I'll tell you an N of one of an interesting biological intrinsic motivation because people, so in like the alignment community and stuff, there's a lot of discussion about what are the intrinsic motivations going to be of AIs?

What are their goals going to be, right?

What are they going to want to do?

Just as an N of one observation, anthrobots.

The very first thing we checked for, so this is not experiment number 972 out of a thousand things.

This is the very first thing we checked for.

We put them on a plate of neurons with a big wound through them, a big scratch.