6 ms·

Looking at the training data I don't think it will know anything.[0] Doubt On the Connexion of the Physical Sciences (1834) is going to have much about QM. Whil

by tokai 10mo ago

Looking at the training data I don't think it will know anything.[0] Doubt On the Connexion of the Physical Sciences (1834) is going to have much about QM. While the cut-off is 1900, it seems much of the texts a much closer to 1800 than 1900. [0] https://github.com/haykgrigo3/TimeCapsuleLLM/blob/main/Copy%20of%20London%20Documents%20for%20Time%20Capsule%20LLM.txt https://github.com/haykgrigo3/TimeCapsuleLLM/blob/main/Copy%...

dogma1138 10mo ago
It doesn’t need to know about QM or reactivity just about the building blocks that led to them. Which were more than around in the year 1900. In fact you don’t want it to know about them explicitly just have enough background knowledge that you can manage the rest via context.
- tokai 10mo ago
  I was vague. My point is that I don't think the building blocks are in the data. Its mainly tertiary and popular sources. Maybe if you had the writings of Victorian scientists, both public and private correspondence.
  - pegasus 10mo ago
    Probably a lot of it exists but in archives, private collections etc. Would be great if it will all end up digitized as well.
- viccis 10mo ago
  LLMs are models that predict tokens. They don't think, they don't build with blocks. They would never be able to synthesize knowledge about QM.
  - strbean 10mo ago
    You realize parent said "This would be an interesting way to test proposition X" and you responded with "X is false because I say say", right?
    - anonymous908213 10mo ago
      "Proposition X" does not need testing. We already know X is categorically false because we know how LLMs are programmed, and not a single line of that programming pertains to thinking (thinking in the human sense, not "thinking" in the LLM sense which merely uses an anthromorphized analogy to describe a script that feeds back multiple prompts before getting the final prompt output to present to the user). In the same way that we can reason about the correctness of an IsEven program without writing a unit test that inputs every possible int32 to "prove" it, we can reason about the fundamental principles of an LLM's programming without coming up with ridiculous tests. In fact the proposed test itself is less eminently verifiable than reasoning about correctness; it could be easily corrupted by, for instance, incorrectly labelled data in the training dataset, which could only be determined by meticulously reviewing the entirety of the dataset. The only people who are serious about suggesting that LLMs could possibly 'think' are the people who are committing fraud on the scale of hundreds of billions of dollars (good for them on finding the all-time grift!) and people who don't understand how they're programmed, and thusly are the target of the grift. Granted, given that the vast majority of humanity are not programmers, and even fewer are programmers educated on the intricacies of ML, the grift target pool numbers in the billions.
      - strbean 10mo ago
        > We already know X is categorically false because we know how LLMs are programmed, and not a single line of that programming pertains to thinking (thinking in the human sense, not "thinking" in the LLM sense which merely uses an anthromorphized analogy to describe a script that feeds back multiple prompts before getting the final prompt output to present to the user). Could you elucidate me on the process of human thought, and point out the differences between that and a probabilistic prediction engine? I see this argument all over the place, but "how do humans think" is never described. It is always left as a black box with something magical (presumably a soul or some other metaphysical substance) inside.
        anonymous908213 10mo ago
        There is no need to involve souls or magic. I am not making the argument that it is impossible to create a machine that is capable of doing the same computations as the brain. The argument is that whether or not such a machine is possible, an LLM is not such a machine. If you'd like to think of our brains as squishy computers, then the principle is simple: we run code that is more complex than a token prediction engine. The fact that our code is more complex than a token prediction engine is easily verified by our capability to address problems that a token prediction engine cannot. This is because our brain-code is capable of reasoning from deterministic logical principles rather than only probabilities. We also likely have something akin to token prediction code, but that is not the only thing our brain is programmed to do, whereas it is the only thing LLMs are programmed to do.
  - PaulDavisThe1st 10mo ago
    I am a deep LLM skeptic. But I think there are also some questions about the role of language in human thought that leave the door just slightly ajar on the issue of whether or not manipulating the tokens of language might be more central to human cognition than we've tended to think. If it turned out that this was true, then it is possible that "a model predicting tokens" has more power than that description would suggest. I doubt it, and I doubt it quite a lot. But I don't think it is impossible that something at least a little bit along these lines turns out to be true.
    - pegasus 10mo ago
      > manipulating the tokens of language might be more central to human cognition than we've tended to think I'm convinced of this. I think it's because we've always looked at the most advanced forms of human languaging (like philosophy) to understand ourselves. But human language must have evolved from forms of communication found in other species, especially highly intelligent ones. It's to be expected that the building blocks of it is based on things like imitation, playful variation, pattern-matching, harnessing capabilities brains have been developing long before language, only now in the emerging world of sounds, calls, vocalizations. Ironically, the other crucial ingredient for AGI which LLMs don't have, but we do, is exactly that animal nature which we always try to shove under the rug, over-attributing our success to the stochastic parrot part of us, and ignoring the gut instinct, the intuitive, spontaneous insight into things which a lot of the great scientists and artists of the past have talked about.
      - viccis 10mo ago
        >Ironically, the other crucial ingredient for AGI which LLMs don't have, but we do, is exactly that animal nature which we always try to shove under the rug, over-attributing our success to the stochastic parrot part of us, and ignoring the gut instinct, the intuitive, spontaneous insight into things which a lot of the great scientists and artists of the past have talked about. Are you familiar with the major works in epistemology that were written, even before the 20th century, on this exact topic?
      - catlifeonmars 10mo ago

by tokai 10mo ago

dogma1138 10mo ago

It doesn’t need to know about QM or reactivity just about the building blocks that led to them. Which were more than around in the year 1900. In fact you don’t want it to know about them explicitly just have enough background knowledge that you can manage the rest via context.

tokai 10mo ago
I was vague. My point is that I don't think the building blocks are in the data. Its mainly tertiary and popular sources. Maybe if you had the writings of Victorian scientists, both public and private correspondence.
- pegasus 10mo ago
  Probably a lot of it exists but in archives, private collections etc. Would be great if it will all end up digitized as well.
viccis 10mo ago
LLMs are models that predict tokens. They don't think, they don't build with blocks. They would never be able to synthesize knowledge about QM.
- strbean 10mo ago
  You realize parent said "This would be an interesting way to test proposition X" and you responded with "X is false because I say say", right?
  - anonymous908213 10mo ago
    "Proposition X" does not need testing. We already know X is categorically false because we know how LLMs are programmed, and not a single line of that programming pertains to thinking (thinking in the human sense, not "thinking" in the LLM sense which merely uses an anthromorphized analogy to describe a script that feeds back multiple prompts before getting the final prompt output to present to the user). In the same way that we can reason about the correctness of an IsEven program without writing a unit test that inputs every possible int32 to "prove" it, we can reason about the fundamental principles of an LLM's programming without coming up with ridiculous tests. In fact the proposed test itself is less eminently verifiable than reasoning about correctness; it could be easily corrupted by, for instance, incorrectly labelled data in the training dataset, which could only be determined by meticulously reviewing the entirety of the dataset. The only people who are serious about suggesting that LLMs could possibly 'think' are the people who are committing fraud on the scale of hundreds of billions of dollars (good for them on finding the all-time grift!) and people who don't understand how they're programmed, and thusly are the target of the grift. Granted, given that the vast majority of humanity are not programmers, and even fewer are programmers educated on the intricacies of ML, the grift target pool numbers in the billions.
    - strbean 10mo ago
      > We already know X is categorically false because we know how LLMs are programmed, and not a single line of that programming pertains to thinking (thinking in the human sense, not "thinking" in the LLM sense which merely uses an anthromorphized analogy to describe a script that feeds back multiple prompts before getting the final prompt output to present to the user). Could you elucidate me on the process of human thought, and point out the differences between that and a probabilistic prediction engine? I see this argument all over the place, but "how do humans think" is never described. It is always left as a black box with something magical (presumably a soul or some other metaphysical substance) inside.
      - anonymous908213 10mo ago
        There is no need to involve souls or magic. I am not making the argument that it is impossible to create a machine that is capable of doing the same computations as the brain. The argument is that whether or not such a machine is possible, an LLM is not such a machine. If you'd like to think of our brains as squishy computers, then the principle is simple: we run code that is more complex than a token prediction engine. The fact that our code is more complex than a token prediction engine is easily verified by our capability to address problems that a token prediction engine cannot. This is because our brain-code is capable of reasoning from deterministic logical principles rather than only probabilities. We also likely have something akin to token prediction code, but that is not the only thing our brain is programmed to do, whereas it is the only thing LLMs are programmed to do.
- PaulDavisThe1st 10mo ago
  I am a deep LLM skeptic. But I think there are also some questions about the role of language in human thought that leave the door just slightly ajar on the issue of whether or not manipulating the tokens of language might be more central to human cognition than we've tended to think. If it turned out that this was true, then it is possible that "a model predicting tokens" has more power than that description would suggest. I doubt it, and I doubt it quite a lot. But I don't think it is impossible that something at least a little bit along these lines turns out to be true.
  - pegasus 10mo ago
    > manipulating the tokens of language might be more central to human cognition than we've tended to think I'm convinced of this. I think it's because we've always looked at the most advanced forms of human languaging (like philosophy) to understand ourselves. But human language must have evolved from forms of communication found in other species, especially highly intelligent ones. It's to be expected that the building blocks of it is based on things like imitation, playful variation, pattern-matching, harnessing capabilities brains have been developing long before language, only now in the emerging world of sounds, calls, vocalizations. Ironically, the other crucial ingredient for AGI which LLMs don't have, but we do, is exactly that animal nature which we always try to shove under the rug, over-attributing our success to the stochastic parrot part of us, and ignoring the gut instinct, the intuitive, spontaneous insight into things which a lot of the great scientists and artists of the past have talked about.
    - viccis 10mo ago
      >Ironically, the other crucial ingredient for AGI which LLMs don't have, but we do, is exactly that animal nature which we always try to shove under the rug, over-attributing our success to the stochastic parrot part of us, and ignoring the gut instinct, the intuitive, spontaneous insight into things which a lot of the great scientists and artists of the past have talked about. Are you familiar with the major works in epistemology that were written, even before the 20th century, on this exact topic?
    - catlifeonmars 10mo ago