Michael Levin

Self.

That's it.

And so that's the thing to remember is that, and we know this from history, is that just being a collective isn't enough because what the goals of that collective will be relative to the welfare of the individual parts is a massively open question.

Yeah.

I mean, every system has some degree of intelligence in its own problem domain.

So cells will have problems they're trying to solve in physiological space and transcriptional space, and then I can give you some cool examples of that.

But the collective is trying to solve problems in anatomical space, right, and forming a creature and growing your blood vessels and so on.

And then the whole body is solving yet other problems.

They may be in social space and linguistic space and three-dimensional space.

And who knows, the group might be solving problems in, I don't know, some sort of financial space or something.

So one of the major differences with most AIs today is A, the kind of flatness of the architecture, but also of the fact that they're constructed...

from outside their their borders and their you know so so a few for so to a large extent and of course there are counter examples now but but to a large extent our technology has been such that you create a machine or a robot it knows what its sensors are it knows what its effectors are it knows the boundary between it and the outside world all this is given from the outside biology constructs this from scratch now the best example of this that that originally

in robotics was actually Josh Vanguard's work in 2006, where he made these robots that did not know their shape to start with.

So like a baby, they sort of floundered around.

They made some hypotheses.

Well, I did this and I moved in this way.

Well, maybe I'm a whatever.

Maybe I have wheels or maybe I have six legs or whatever.

And they would make a model and eventually would crawl around.

So that's I mean, that's really good.