Michael Levin: belief

Transcript context

…So this goes back to the very first thing you said about physicists thinking that physics is enough. You’re 100% correct that somebody could look at this and say, “Well, I see exactly why this is happening. We can track through the algorithm.” Yeah, you can. There’s no miracle going on here, right? I mean, the hardware isn’t doing some crazy thing that it wasn’t supposed to do. The point is that despite following the algorithm to do one thing, it is also at the same time doing other things that are neither prescribed nor forbidden by the algorithm. It’s the space between chance and necessity, which is how a lot of people, you know, see these things. It’s that free space, we don’t really have a good vocabulary for it, where the interesting things happen. And to whatever extent it’s doing other things that are useful, that stuff is computationally without extra cost. Now, there’s one other cool thing about this. And this is the beginning of a lot of thinking that I’ve done about this—this relates to AI and stuff like that: intrinsic motivations. The sorting of the digits is what we forced it to do. The clustering is an intrinsic motivation. We didn’t ask for it. We didn’t expect it to happen. We didn’t explicitly forbid it, but we didn’t, you know, we didn’t know. This is a great definition of the intrinsic motivation of a system. So when people say, “Oh, that’s a machine, it only does what you programmed it to do.” I, you know, I as a human have intrinsic motivation, you know I’m creative and I have intrinsic motivation. Machines don’t do that. Even this minimal thing has a minimal kind of intrinsic motivation, which is something that is not forbidden by the algorithm, but isn’t prescribed by the algorithm either. And I think that’s an important, you know, third thing besides chance and necessity. Something else that’s fun about this is when you think about intrinsic motivations, think about a child. If you make him sit in math class all day, you’re never going to know what the other intrinsic motivations are that he might be doing, right? Like who knows what else he might be interested in. So I wanted to ask this question. I want to say, if we let off the pressure on the sorting, what would happen? Now, that’s hard because if you mess with the algorithm, now it’s no longer the same algorithm, so you don’t want to do that. So we did something that I think was kind of clever. We allowed repeat digits. that’s hard because if you mess with the algorithm, now it’s no longer the same algorithm, so you don’t want to do that. So we did something that I think was kind of clever. We allowed repeat digits. So if you allow repeat digits in your array, you can still have all the fives, can still be after all the fours and after all the sixes, but you can keep them as clustered as you want. So this thing at the end where they have to get de-clustered in order for the sorting to happen, we thought maybe we could let off the pressure a little bit. If you do that, all you do is allow some extra repeat digits, the clustering gets bigger. It will cluster as much as you let it. The clustering is what it wants to do. The sorting is what we’re forcing it to do. And my only point is if the bubble sort, which has been gone over and gone over, how many times has these kinds of things that we didn’t see coming, what about the AIs, the language model, everything else? Not because they talk, not because they say that they’re, you know, have an inner perspective or any of that, but just from the fact that this thing is even the most minimal system surprises with what happens. And frankly, when I see this, tell me if this doesn’t sound like all of our existential story. For the brief time that we’re here, the universe is going to grind us into dust eventually, but until then, we get to do some cool stuff that is intrinsically motivating to us, that is neither forbidden by the laws of physics nor determined by the laws of physics, but eventually, it kind of comes to an end. So I think that aspect of it, right, that there are spaces… Even in algorithms, there are spaces in which you can do other new things, not just random stuff, not just complex stuff, but things that are easily recognizable to a behavior scientist. You see, that’s the point here. And I think that kind of intrinsic motivation is what’s telling us that this idea that we can carve up the world, we can say, “Okay, look, biology is complex. Cognition, who knows what’s responsible for that, but at least we can take a chunk of the world aside and we can cut it off and we can say, these are the dumb machines.” These are just these algorithms… Whereas we know the rules of biochemistry don’t explain everything we want to know about how psychology is going to go, but at least the rules of algorithms tell us exactly what the machines are going to do, right? We have some hope that we’ve carved off a little part of the world and everything is nice and simple, and it is exactly what we said it was going to be. I think that failed. I think it was a good try. I think we have good theories of interfaces, but even the simplest algorithms have these kinds of things going on. And so that’s why I think something like this is significant. Do you think that there is going to be in all kinds of systems of varying complexity things that the system wants to do and things that it’s forced to do? So, are there these unexpected competencies to be discovered in basically all algorithms and all systems?…

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