Daniel Kokotajlo: belief

Transcript context

…LM That's assuming your conclusion, the last little piece. Well, me tell you more. Let me tell you why I think this. So I think that, yeah, maybe the transformer by itself might have trouble doing a lot of this. But you should think about the transformer plus the system of tools and plug ins that you can build around it. Claude plus code interpreter and things like that. And I think that that system could solve tariff and things like that because Claude might be smart enough to look up the algorithm and then implement the algorithm and then do it. So actually I'm curious for your immediate reaction to that point. I mean, I've always advocated neurosymbolic AI. And I think that that is actually a species of neurosymbolic AI. I don't think it's the right 1. But the point of neurosymbolic AI, the arguments that I made going back to 2001 was that you need symbols in the system to do abstract operations over variables. There were several other arguments, but that was the core argument. And what you're doing when you have Claude call an interpreter is you are doing operations over variables in the Python that it creates. And so you're, you know, the you're moving it to a different part of the system. So the pure neural networks don't have operations over variables and fail on all of these things. Symbolic systems can do them fine. And here you're using the neural network to create the symbolic system that you need in order to solve that particular problem. So it's absolutely a neurosymbolic solution. I think that the rate limiting step is that they don't always call the right code that they need. If if you could make that solid enough, that would be great.…

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