Keith Duggar: belief

Transcript context

…I mean, I totally agree that in terms of creativity and the amplification of what humans already can do, AI is hugely valuable in its present form. Like, can amplify things in really interesting ways. But it's still ultimately the germ of the ideas is coming out of the human mind. And so you could be satisfied with that or not. You could say, this is good enough, I'm happy. But I think in terms of what we can't do because of that, that we might someday in the future be able to do, The things we can't do are the things where the human mind isn't having the ideas. We're worried about things from a scientific perspective, for example, and the kinds of things like the new place that I work now, Lila Science is thinking about automating the wheel of science, where we would like these ideas to come faster to solve the problems of the world. But it also applies to art as well. It's like when are we going to get the next big idea in music that's going to really shake things up? It's taking a long time here. And so of course, we can just wait for humans to come up with these things and wait long enough and it might happen. But the question is can AI accelerate that process both in the sciences and the humanities? This is a separate question, is that a good thing? But it's just an interesting question, can it be done? And if it can be done, then that's not what's happening with current models yet. And so, I think when we talk about representation, it starts pointing us in the direction of how that will ultimately be done, is by taking seriously the underlying representations and understanding that to actually see something novel in the world in a genuinely interesting and deep way requires you to decompose the world in a special way that's highly organized, and unique. And, that's just not what this fractured and tangled representation seems likely to do, at least as far as we've understand it right now. And the the actually, this question came up, Ken, in your doom debate, which is, you know, is survival an objective or a constraint? And I think, you know, Ken, I think and and by the way, I I wanna say that was a brilliant interview. I thought your performance was excellent. I really enjoyed watching that debate. I saw it from start to finish. I think, you know, your communication of open ended search and and kind of these this take on it has has been sharpened, you know, significantly since like 4 years ago when we talked. I found it like really compelling. So I thought that was a great a great conversation. And in there you say, look, you know, survival of the fittest is is not an objective. It's a constraint. It's like it's a constraint on the system. You have to survive if you're gonna propagate but subject to that binary, you know, survive or not survive, it's not part of any objective. You know, I think is the way you kinda communicated that. Yeah. Yeah. I I think that's an important point here. Like, I don't think that the right mental conception of evolution, biological evolution is as an optimizer. And this causes endless confusion, you know, because like, in the field of AI it causes a lot of confusion. Because unfortunately early genetic algorithms essentially were using selection for explicit optimization. The metaphor was broken in my view. Basically we had the metaphor for selection, but the metaphor is still wrong because that's not overall what evolution is doing. It's not selecting just to get to a single target. A single target in the search space that we're trying to get to, like the solution to 1 problem. But the problem is that in the field of AI, people think of genetic algorithms as effectively an appropriate metaphor for what evolution is. And it's sort of a bad optimization algorithm. People think of it as the poor man's optimization algorithm. And so get really dismissive of evolution as a useful metaphor. It's like, we played with that in the '80s, but it is nothing really down that path. But I think that's why it's been damaging. It actually is a very deep metaphor if you think of it, like you said, as a constraint. Like you think of survival as a constraint, as an object. There are many, many things that we could have thought of as objectives like flight or like photosynthesis. These could have been objectives for machines. They would have been single runs where if they succeeded, we would have celebrated and said, is amazing achievement. We discovered the ability to fly, the ability to process sunlight. Like these would have been achievements that were objective, but they're not the objective of the system in the usual sense. And the reason that the system is discovering is not because survive obviously leads to these things. It doesn't follow at all directly and say, well, we have an objective and we're optimizing it. It's called survive. So of course we're going to get photosynthesis. Obvious, I mean, no 1 could predict this at the beginning. It makes absolutely no sense at all. It's an orthogonal issue. It's sort of like a side effect. The point is that the things that we care about are actually the side effects of the constraint. It's not directly a consequence of the constraint, but we have to understand it as like the side effect is actually the main event. Like we care about the side effect. And so what that there's a really important, so when we look at it in that light, like if we change the metaphor, and we think of it as evolution is actually an open ended divergent process without a final objective. Then we can see that there are huge consequences for again, things like representation.…

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