5 ms·

It's pretty obvious that LLMs are not human like intelligent but are just statistical models. They can't produce anything novel in the sense of finding a cure f

It's pretty obvious that LLMs are not human like intelligent but are just statistical models. They can't produce anything novel in the sense of finding a cure for cancer or solving millennium problems, even though they have embedded knowledge of all human knowledge. The easiest way to test this is by trying to get them to generate a novel idea that doesn't yet exist but will exist in a year or a few years. This idea shouldn't require experimentation in the real world, which LLMs don't have access to, but should involve interpreting and reasoning about the knowledge we already have in a novel way

viraptor 3y ago
> The easiest way to test this is by trying to get them to generate a novel idea that doesn't yet exist but will exist in a year or a few years. Counterexample: See Tom Scott playing with ChatGPT and asking for ideas for the kind of videos he would do. One of the results was almost exactly a video which was already planned but not released.
- HAL3000 3y ago
  I don't see anything novel there. With millions of videos and content creators on the platform, it was statistically highly probable that one of these videos would match what he should create. It's like advanced collaborative filtering. If I tell you that you sometimes miss opportunities, it's not because I'm clairvoyant, but because statistically, most people do.
  - cratermoon 3y ago
    > If I tell you that you sometimes miss opportunities, it's not because I'm clairvoyant, but because statistically, most people do. Your choice of the word "clairvoyant" is revealing. So-called clairvoyants, palm readers, horoscopes, fortune-tellers, and so forth, lean on cold-reading skills. As it happens, the kind of information LLMs have about things like what people on the internet do for content creation is exactly the sort of thing that allows a high degree of Barnum effect to influence the person.
  - viraptor 3y ago
    You seem to imply that novel things are not statistically highly probable as a chain of thought in a specific context. That's a significant claim and I'm not sure we'd all agree on.
    - HAL3000 3y ago
      I believe one of the main problems with the current generation of LLMs is that they are constrained by statistics. Unless there exists a universal pattern that can address all problems, and we require an enormous amount of parameters and training for it to emerge, these models may never effectively solve the problems that matter most to us and that we find challenging to address on our own.
astrange 3y ago
> They can't produce anything novel in the sense of finding a cure for cancer or solving millennium problems, even though they have embedded knowledge of all human knowledge. Humans can't do this by thinking about it either. Humans would find a cure for cancer by performing experiments and seeing which one of them worked.
- lossolo 3y ago
  Sure, in case of cure for cancer in context of current models it's totally true, because they don't have access to outside world. I addressed your argument in next paragraph, in which I acknowledged exactly this and proposed an experiment that would avoid that. You don't need to perform experiments in outside world to solve some of the mathematical problems, that are still unsolved. You only need brain, prior knowledge about the problem and a piece of paper for that.
  - astrange 3y ago
    The piece of paper is an experiment in the outside world; if you have external memory you're limited by the speed of using it. Of course, any known AIs aren't nearly (or at all) as capable of using them as people are.
    - lossolo 3y ago
      Piece of paper is here used as metaphor for memory or stack on which you store results of your computation, not as outside world physical object.
cratermoon 3y ago
> they have embedded knowledge of all human knowledge. They do not have "all human knowledge". This is important: they have been trained with a very limited subset of human-generated content, partly scraped from the world wide web. What we don't know, because AI purveyors generally don't tell us, is the full contents of their training set. We do know, however, they reproduce the biases in their data sets.

viraptor 3y ago

> The easiest way to test this is by trying to get them to generate a novel idea that doesn't yet exist but will exist in a year or a few years. Counterexample: See Tom Scott playing with ChatGPT and asking for ideas for the kind of videos he would do. One of the results was almost exactly a video which was already planned but not released.

HAL3000 3y ago
I don't see anything novel there. With millions of videos and content creators on the platform, it was statistically highly probable that one of these videos would match what he should create. It's like advanced collaborative filtering. If I tell you that you sometimes miss opportunities, it's not because I'm clairvoyant, but because statistically, most people do.
- cratermoon 3y ago
  > If I tell you that you sometimes miss opportunities, it's not because I'm clairvoyant, but because statistically, most people do. Your choice of the word "clairvoyant" is revealing. So-called clairvoyants, palm readers, horoscopes, fortune-tellers, and so forth, lean on cold-reading skills. As it happens, the kind of information LLMs have about things like what people on the internet do for content creation is exactly the sort of thing that allows a high degree of Barnum effect to influence the person.
- viraptor 3y ago
  You seem to imply that novel things are not statistically highly probable as a chain of thought in a specific context. That's a significant claim and I'm not sure we'd all agree on.
  - HAL3000 3y ago
    I believe one of the main problems with the current generation of LLMs is that they are constrained by statistics. Unless there exists a universal pattern that can address all problems, and we require an enormous amount of parameters and training for it to emerge, these models may never effectively solve the problems that matter most to us and that we find challenging to address on our own.

astrange 3y ago

> They can't produce anything novel in the sense of finding a cure for cancer or solving millennium problems, even though they have embedded knowledge of all human knowledge. Humans can't do this by thinking about it either. Humans would find a cure for cancer by performing experiments and seeing which one of them worked.

lossolo 3y ago
Sure, in case of cure for cancer in context of current models it's totally true, because they don't have access to outside world. I addressed your argument in next paragraph, in which I acknowledged exactly this and proposed an experiment that would avoid that. You don't need to perform experiments in outside world to solve some of the mathematical problems, that are still unsolved. You only need brain, prior knowledge about the problem and a piece of paper for that.
- astrange 3y ago
  The piece of paper is an experiment in the outside world; if you have external memory you're limited by the speed of using it. Of course, any known AIs aren't nearly (or at all) as capable of using them as people are.
  - lossolo 3y ago
    Piece of paper is here used as metaphor for memory or stack on which you store results of your computation, not as outside world physical object.

cratermoon 3y ago

> they have embedded knowledge of all human knowledge. They do not have "all human knowledge". This is important: they have been trained with a very limited subset of human-generated content, partly scraped from the world wide web. What we don't know, because AI purveyors generally don't tell us, is the full contents of their training set. We do know, however, they reproduce the biases in their data sets.