Cristopher Moore: evaluation

Transcript context

…of those challenges is the meaning of the real world. Like, look at this cool thing or, you know, this essay is actually about real things. Think about those real things. Don't just look at the text. And, you know, of course, these are again, like I I promised you that I I would say things that other people have said better. So Right. Yeah. But this this is the this seems like the debate. And on the other hand, I do think like I said, just just as I think that once these things, as they're already doing, cannot just write code but run it and see whether it works, and then debug the code if it doesn't work, or if it is a question about a three-dimensional object, they could fire up a three-dimensional workspace or a 7 dimensional workspace where they can doodle and then kind of perceive the way we perceive. Right? I am a bit of a Platonist because if you and I close our eyes and we each think of a cube admittedly, we live in a society with a lot of right angles, and we've seen wireframes rotating on screen, so we've had a lot of practice with this. But both of us can see in our minds a cube, and we can count the fact we can just by counting, just by perception, see that it has 8 corners and see that it has 12 edges. And if 1 of us thought it had 14 edges, the other would say, no, it's 12, and the other 1 would look again at the cube in their mind and say, oh, yeah. You're right. It's 12. Right? So we're really perceiving something there. And the fact that we can have that shared perception gives me and a lot of other mathematicians a sense that there is some reality to these things. These are not just subjective objects. And so I do think that once these systems can switch on the fly what kind of workspaces they have and what kind of reasoning they do, then I think that they'll I think they'll they'll be much closer to what we do. Right? Yeah. I mean, even even if you ask them to do proofs, of course, there are proof finding systems that are very formalized systems. If you ask an LLM to construct a proof, it will often construct some BS. It will be stylistically similar to proofs it's read. But so far, it doesn't seem to be able to do that reflection process and really check the steps in the proof and see if it works. But of course, that is also a very specific thing that humans don't do very often. Right? Specific humans in specific cultures do this and have tools for doing this. And we might whip out a sheet of paper and we might start writing things with formal symbols and formal logic to see if our informal proof written in English or whatever actually holds. But when we do that, we're firing up some special mental models, and we're using some external tools, paper, pencil, blackboards, computers, to help us with them. Because, actually, formal logic is not something that we're built to do. p some special mental models, and we're using some external tools, paper, pencil, blackboards, computers, to help us with them. Because, actually, formal logic is not something that we're built to do. So I guess I I expect I expect these systems to once they can really play with all of these modules, including ones that we don't have, like visualizing things in 7 dimensions, they'll be able to do a lot just as when they start I'm not sure if we should do this, but when we give them access to 3 d printers and fab labs so that they can start building things and seeing whether they work, well, maybe we should solve the alignment problem first. Yes. What what what you were saying about the pastiche, you know, like the kind of GPT generated text was was very interesting to me. So so they they model this statistical distribution, and and they they they greedily sampled, and they just give you tokens from the bulk. And you know, 1 school of thought is, well, we'll make them more creative. We'll just turn up the temperature and we'll just sample tokens from the tail. And and you you really get garbage there because you're kind of, you know, you're you're a little bit out of distribution now. And 1 thing that would lead me to think they were learning these kind of, you know, factored representations of the world is when when you did sample the tail, it actually gave you something creative and and useful. Right. But I did I did wanna kind of like say that I'm not entirely sure whether it's that, whether it is about learning meaningful structured representations about things which are grounded in the world. Or I'm a creative professional, so I've learned about video editing and a higher script writers and so on. And and I've noticed that there might be something else at play. I've noticed, you know, you were saying you can add noise to problems, and and that actually makes them more tractable. And in in audio and video, if you add noise, then you're actually training, you know, just textures, high frequency patterns. You're training human perception to to look away. So you could blur, you could add a texture, and and so on. And it's the same in writing, that there are so many creative motifs just using slightly different language, that deliberately taking it away from the head of the distribution, but such that it respects the epistemic phylogeny. So it needs to be meaningful, but still creative. And it doesn't necessarily have to have any grand meaning or be grounded in the world. So I guess the question, just is kind of aesthetic creativity? Or does it really need to respect the rules?…

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