Built The Future of the Brain.

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…All right. Well, now we're ready for our first full on disagreement of the day. But I understand your logic there. I think it's well thought through. But it's missing the cognitive science for me. And my approach to this is more from the cognitive science. I see a set of problems that a cognitive creature must solve, many of which I wrote in my 2,001 book, The Algebraic Mind. And I don't feel like we've solved any of those problems despite the quantitative progress that we've made. And those include generalizing outside the distribution, which I think still remains a huge problem. I think the Apple paper was a good, there are actually 2 Apple papers I discovered today. But the Apple paper with the Tower Of Hanoi stuff, I think is an example of problems with distribution shift. I think we've seen many of them over the years. We see that these systems have trouble doing multiplication with large numbers unless they call on tools, and so forth. I think there's lots of evidence for that. I think that there's a problem of distinguishing types and tokens that leads to bleed through when you're representing multiple individuals from some category that leads to hallucinations. So I wrote an essay recently about the hallucinations that, I think it was ChatGPT made about my friend Harry Shearer who's a pretty well known actor. And it misnamed the roles of characters that he played in the movie Spinal Tap and said that he was British when he's American and so forth. And I think this blurring together that we see hallucinations remains a problem. And I could go on with a list of others. I think there are several having to do with reasoning, planning, etcetera. And the way I look at things, which is not to say that there isn't some value in what you're doing, is more on these cognitive tasks. And so what I say to myself is what would AI look like 2 years before we achieved AGI or ASI or something like that? Certainly 2 years before we achieved ASI we would have full solutions to all of those things. If we specified if we specified an algorithm for something, we would expect the system to be able to follow it. Current systems can't even play chess reliably according to the rules. So, you know, o 3 will not, sorry. O 3 will sometimes make illegal moves. It can't avoid illegal moves. Another thing I would expect is that current systems when we're close would be basically the equivalent of their domain specific counterparts or at least be close, right? AGI means Artificial General Intelligence. And I would say the reality is that domain specific systems are actually much better than the general ones right now. The only general ones we have are LLM based. But for example, AlphaFold is a very carefully engineered hybrid neurosymbolic system that far outperforms what you could get from a pure chatbot or something like that. Somebody just showed that an Atari 2,600 beat, I think it was o 3 in chess. y engineered hybrid neurosymbolic system that far outperforms what you could get from a pure chatbot or something like that. Somebody just showed that an Atari 2,600 beat, I think it was o 3 in chess. So even sometimes very old systems will beat the modern domain general ones. On Tower of Hanoi, Herb Simon solved it in 1957 with a classical technique that generalizes to arbitrary length whereas the LLMs do not generalize to arbitrary length and face problems. And so I could go through more but the gist of it is I don't see the qualitative problems that I think need to be solved. And I'll just go to your 10 45, 10 to the 40 fifth because it's really interesting to me. I wrote a piece once with Christoph Koch. I don't know if you guys know him, the neuroscientist. We wrote a science fiction essay. It's the only published science fiction essay I've ever written. And it was in a book that I wrote called The Future of the Brain, which I guess we wrote in 02/2015. I was the editor and we wrote the epilogue to it in, call it, 2015. And so it was said in something like 02/1955. Or I think it was 02/1945. And the notion was, there's a book about neuroscience, that by that point we would actually have created an entire simulation of the brain, but it would run slower than the human brain and would still not have taught us anything about how the brain really works. So we'd have this simulation, but we wouldn't understand the principles of We'd have a neuron by neuron simulation, maybe even a protein by protein simulation. But we could find ourselves in a place where we'd replicated the whole thing without really understanding where it worked. And I do wonder with the 10 to 45, even if you sort of train on everything, would you have solved the distribution shift and would you have abstracted principles that allow you to run efficiently and effectively and usefully in new new domains and so forth? I think it's a really interesting question. Had never thought of the 10 45 even though I read at least some of your paper. Admit I didn't get that level of detail. Well this part wasn't in ANT 20 27. This is sort of…

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