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>No, I said "computers aren't brains, and they don't do the things that brains do". >and also be rejecting the very premise that AI is possible? >>Who ever sa

>No, I said "computers aren't brains, and they don't do the things that brains do". >and also be rejecting the very premise that AI is possible? >>Who ever said anything like that? They certainly do a lot of the things brains do. Lest we forget, "calculator" was once a job title. My understanding of AI as field is that the point of it all is exactly to figure out how to do on the machine the stuff brains can do that still eludes us. The case you've been making, particularly "because a computer is not human, and it can't see the things that humans can see, it can only compute" is the anthesis of this premise. It implies that there is something about our conscious existence that cannot be understood and implemented as a computer algorithm (as we've done for prior things that were once strictly the domain of man). It implies that questions of emergent properties and the relevance of recursion and so on are futile. Maybe we just have superficial agreement. On the surface we are both "interested in AI", but dig into our reasons why and maybe there's a sea of difference. I don't think its terribly important that "the model can't do any computation itself, because it's a model and not a computational device." I'm not interested in...whatever that argues against. I am interested in if giving chatGPT the ability to prompt itself recursively results in any surprising emergent reasoning behaviors. With respect to that, it really doesn't matter the details of how it was piped back into itself.

YeGoblynQueenne 4y ago
>> They certainly do a lot of the things brains do. Lest we forget, "calculator" was once a job title. My understanding of AI as field is that the point of it all is exactly to figure out how to do on the machine the stuff brains can do that still eludes us. There isn't really any one "point of it all" for AI. If you ask different people, they'll tell you different things. Some want to replicate human intelligence, which may mean replicating the function of the brain; or not, because maybe we can make a machine behave like a human without it functioning like a brain. Some just want to make computers not stupid. Others want to get computers to exhibit human-like behaviour on a computer in order to understand human-like behaviour in humans. For example, many of the pioneers of AI (Turing, Shannon, McCarthy, Michie, etc) were interested in chess and board game-playing AI because they thought that a computer playing chess like a human, would tell us something about how humans play chess. And that, in turn would tell us something about how humans think, because it's obvious that humans play chess by thinking about chess (and who knows what else). It turns out that it's not necessary to think like a human, or to think at all, to play a game or chess, or at least to calculate the best move given a board position. We now have systems that can do it, and that can beat any human in chess. Yet those systems are based on specialised algorithms for board-game playing, that work nothing like humans do with their minds when we play chess, because no human plays chess by "running" alpha-beta minimax and Monte Carlo Tree Search in their head. And so those systems still tell us nothing about how humans play chess, or how humans think (McCarthy was really pissed off about that and he wrote an article blasting the state of AI chess research when Deep Blue beat Kasparov). And that's because digital computers, and human brains, or human minds, are nothing like each other. So being able to do one thing with a computer tells us nothing about doing that thing with a human brain or mind, and vice-versa. Which btw also means that we can't really look at human behaviour, and predict from it computer behaviour, just because we see some behaviour in a computer that looks superficially like human behaviour. There is always the question of what the computer is really doing, and whether it is at all like what the human is doing, and the answer to that is, so far, a resounding: no. And because of all of the above, we learn nothing by simply trying to match ideas and concepts, and reuse terms, that we use to talk about computers, to talk about humans, and the other way around. In turn, when terminology is used in such a free-wheeling manner as in the article above, we learn nothing, because it means nothing. "Recursion" is a thing in computers. It's a different thing in humans. It's clear that the author is trying to do the computer-thing of recursion, but it's also clear they're not doing that, at all. And if they were trying to do "recursion" as in humans, then it's clear they're not doing that, either, because they're trying to do it like we do it in computers. So all the author's done is fudge some terminology, bodge together some code and call it "recursion", and achieve nothing but brief internet fame. But I suspect that was the only motivation. >> I don't think its terribly important that "the model can't do any computation itself, because it's a model and not a computational device." For me that's the whole point because the only model we have of what minds do is computational. And the only justification anyone can really give for AI, whatever they are trying to achieve with it, is that brains are like computers, minds are like programs, and a Universal Turing Machine can run any program that can be run on any other computing machine, so it should be possible to run the mind-program on a computer-brain. Which we still don't know how to do in practice. We might not have the right theory of computation. Or we may not have the right kind of computer. Or we may even not have the right kind of mind, or brain. We'll know when we know, if we ever do.
- IIAOPSW 4y ago
  alright you mostly circled back to things I agree with. It sure was a circuitous garden path. >specialised algorithms for board-game playing, that work nothing like humans do with their minds when we play chess, because no human plays chess by "running" alpha-beta minimax and Monte Carlo Tree Search in their head. And so those systems still tell us nothing about how humans play chess True, but lets not imply that we are true all-domain generalists. If I made a platformer with a quirky physics violating gimmick, you could probably fill in the blanks and figure out how to play it. Now if I handed you a platformer where I have shoved the screen through a 2d fourier transform and you have to play in frequency space, you probably can't do that. The point is we are specialized by evolution for this world with its Newtonian physics and euclidean dimensions and retinal data projected down from 3d to 2d. No one is showing us the games we would suck at. An uncomfortable possibility is that maybe an ad hoc hodge podge of specialised algorithms might actually be the right path. There is no general theory, its all just kludge and patch work. >digital computers, and human brains, or human minds, are nothing like each other. So being able to do one thing with a computer tells us nothing about doing that thing with a human brain or mind, and vice-versa. Sure it does. It tells you exactly how its not done on the other! It can be a model that fails in interesting ways. ChatGPT forces you to put your finger on what exactly differentiates us from a statistical word generator. And, sometimes, we really do learn one off the other, gaining a biologically inspired algorithm or interpreting some brain scans as a known sort of classifier. Let's not undersell this here either. >We'll know when we know, if we ever do. Will we? Or will we rationalize refusing to accept it? From how most people talk, the latter.
  - YeGoblynQueenne 4y ago
    >> Sure it does. It tells you exactly how its not done on the other! I agree. We sure have learned a lot about how our mind doesn't work, with recent AI results. But that's a very long way to get to how it does work. Still, I do think it's interesting how every once in a while we figure out that it doesn't quite take a human mind to do X, but then we also learn that human minds do something else than X, after all. So we do learn something. I once commented to Ivan Bratko, great dean of Prolog programming for AI, that maybe humans are not that good at chess, after all, seeing how a dumb machine can beat us. He's done plenty of work on computer chess and is an inveterate chess player, so he really didn't like my comment. >> True, but lets not imply that we are true all-domain generalists (...) Yan Lecun has said something like that. I don't remember where I saw this, it might have been a Lex Fridman podcast. Anyway he pointed out hat if you could somehow take out someone's visual system, like all their neurons involved in vision, cut it up and stitched it back up in some random new way, there's no way that person would still be able to understand what they were looking at. I don't disagree, but then, we don't just look at the world with our vision, or our senses in general. One absolutely mad thing we do is that we come up with formal systems that abstract the model concepts we can't directly experience, like, say, multi-dimensional spaces. Or, really, all of maths. Those formal systems become interpretations that our mind can then access, and manipulate. So we are not restricted in generality to "this world with Newtonian physics". We may have an "ad hoc hodge podge of specialised algorithms", because that sounds useful and efficient; but it seems to me we have something else, beyond that, and that's what lets us hope we can one day rebuild our mind up from scratch, on a different substrate, in the first place. Because if a bunch of heuristics were all we really are, I don't think we could hope to ever create artificial-us. Because were would the heuristic for that come from? Not evolution, surely. >> alright you mostly circled back to things I agree with. It sure was a circuitous garden path. Yeah, like when the horse raced passed the barn fell. I sure do like to go on :P

YeGoblynQueenne 4y ago

>> They certainly do a lot of the things brains do. Lest we forget, "calculator" was once a job title. My understanding of AI as field is that the point of it all is exactly to figure out how to do on the machine the stuff brains can do that still eludes us. There isn't really any one "point of it all" for AI. If you ask different people, they'll tell you different things. Some want to replicate human intelligence, which may mean replicating the function of the brain; or not, because maybe we can make a machine behave like a human without it functioning like a brain. Some just want to make computers not stupid. Others want to get computers to exhibit human-like behaviour on a computer in order to understand human-like behaviour in humans. For example, many of the pioneers of AI (Turing, Shannon, McCarthy, Michie, etc) were interested in chess and board game-playing AI because they thought that a computer playing chess like a human, would tell us something about how humans play chess. And that, in turn would tell us something about how humans think, because it's obvious that humans play chess by thinking about chess (and who knows what else). It turns out that it's not necessary to think like a human, or to think at all, to play a game or chess, or at least to calculate the best move given a board position. We now have systems that can do it, and that can beat any human in chess. Yet those systems are based on specialised algorithms for board-game playing, that work nothing like humans do with their minds when we play chess, because no human plays chess by "running" alpha-beta minimax and Monte Carlo Tree Search in their head. And so those systems still tell us nothing about how humans play chess, or how humans think (McCarthy was really pissed off about that and he wrote an article blasting the state of AI chess research when Deep Blue beat Kasparov). And that's because digital computers, and human brains, or human minds, are nothing like each other. So being able to do one thing with a computer tells us nothing about doing that thing with a human brain or mind, and vice-versa. Which btw also means that we can't really look at human behaviour, and predict from it computer behaviour, just because we see some behaviour in a computer that looks superficially like human behaviour. There is always the question of what the computer is really doing, and whether it is at all like what the human is doing, and the answer to that is, so far, a resounding: no. And because of all of the above, we learn nothing by simply trying to match ideas and concepts, and reuse terms, that we use to talk about computers, to talk about humans, and the other way around. In turn, when terminology is used in such a free-wheeling manner as in the article above, we learn nothing, because it means nothing. "Recursion" is a thing in computers. It's a different thing in humans. It's clear that the author is trying to do the computer-thing of recursion, but it's also clear they're not doing that, at all. And if they were trying to do "recursion" as in humans, then it's clear they're not doing that, either, because they're trying to do it like we do it in computers. So all the author's done is fudge some terminology, bodge together some code and call it "recursion", and achieve nothing but brief internet fame. But I suspect that was the only motivation. >> I don't think its terribly important that "the model can't do any computation itself, because it's a model and not a computational device." For me that's the whole point because the only model we have of what minds do is computational. And the only justification anyone can really give for AI, whatever they are trying to achieve with it, is that brains are like computers, minds are like programs, and a Universal Turing Machine can run any program that can be run on any other computing machine, so it should be possible to run the mind-program on a computer-brain. Which we still don't know how to do in practice. We might not have the right theory of computation. Or we may not have the right kind of computer. Or we may even not have the right kind of mind, or brain. We'll know when we know, if we ever do.

IIAOPSW 4y ago
alright you mostly circled back to things I agree with. It sure was a circuitous garden path. >specialised algorithms for board-game playing, that work nothing like humans do with their minds when we play chess, because no human plays chess by "running" alpha-beta minimax and Monte Carlo Tree Search in their head. And so those systems still tell us nothing about how humans play chess True, but lets not imply that we are true all-domain generalists. If I made a platformer with a quirky physics violating gimmick, you could probably fill in the blanks and figure out how to play it. Now if I handed you a platformer where I have shoved the screen through a 2d fourier transform and you have to play in frequency space, you probably can't do that. The point is we are specialized by evolution for this world with its Newtonian physics and euclidean dimensions and retinal data projected down from 3d to 2d. No one is showing us the games we would suck at. An uncomfortable possibility is that maybe an ad hoc hodge podge of specialised algorithms might actually be the right path. There is no general theory, its all just kludge and patch work. >digital computers, and human brains, or human minds, are nothing like each other. So being able to do one thing with a computer tells us nothing about doing that thing with a human brain or mind, and vice-versa. Sure it does. It tells you exactly how its not done on the other! It can be a model that fails in interesting ways. ChatGPT forces you to put your finger on what exactly differentiates us from a statistical word generator. And, sometimes, we really do learn one off the other, gaining a biologically inspired algorithm or interpreting some brain scans as a known sort of classifier. Let's not undersell this here either. >We'll know when we know, if we ever do. Will we? Or will we rationalize refusing to accept it? From how most people talk, the latter.
- YeGoblynQueenne 4y ago
  >> Sure it does. It tells you exactly how its not done on the other! I agree. We sure have learned a lot about how our mind doesn't work, with recent AI results. But that's a very long way to get to how it does work. Still, I do think it's interesting how every once in a while we figure out that it doesn't quite take a human mind to do X, but then we also learn that human minds do something else than X, after all. So we do learn something. I once commented to Ivan Bratko, great dean of Prolog programming for AI, that maybe humans are not that good at chess, after all, seeing how a dumb machine can beat us. He's done plenty of work on computer chess and is an inveterate chess player, so he really didn't like my comment. >> True, but lets not imply that we are true all-domain generalists (...) Yan Lecun has said something like that. I don't remember where I saw this, it might have been a Lex Fridman podcast. Anyway he pointed out hat if you could somehow take out someone's visual system, like all their neurons involved in vision, cut it up and stitched it back up in some random new way, there's no way that person would still be able to understand what they were looking at. I don't disagree, but then, we don't just look at the world with our vision, or our senses in general. One absolutely mad thing we do is that we come up with formal systems that abstract the model concepts we can't directly experience, like, say, multi-dimensional spaces. Or, really, all of maths. Those formal systems become interpretations that our mind can then access, and manipulate. So we are not restricted in generality to "this world with Newtonian physics". We may have an "ad hoc hodge podge of specialised algorithms", because that sounds useful and efficient; but it seems to me we have something else, beyond that, and that's what lets us hope we can one day rebuild our mind up from scratch, on a different substrate, in the first place. Because if a bunch of heuristics were all we really are, I don't think we could hope to ever create artificial-us. Because were would the heuristic for that come from? Not evolution, surely. >> alright you mostly circled back to things I agree with. It sure was a circuitous garden path. Yeah, like when the horse raced passed the barn fell. I sure do like to go on :P