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People have done experiments trying to get GPT-4 to come up with viable conjectures. So far it does such a woefully bad job that it isn't worth even trying. Un

by wbhart 3y ago

People have done experiments trying to get GPT-4 to come up with viable conjectures. So far it does such a woefully bad job that it isn't worth even trying. Unfortunately there are rather a lot of issues which are difficult to describe concisely, so here is probably not the best place. Primary amongst them is the fact that an LLM would be a horribly inefficient way to do this. There are much, much better ways, which have been tried, with limited success.

gmerc 3y ago
After a year the entire argument you make boils down to “so far”.
- ra 3y ago
  Indeed. LLM is an application on a transformer trained with backpropagation. What stops you from adding a logic/mathematic "application" on the same transformer?
  - seanhunter 3y ago
    Nothing, and there are methods which allow these types of models to learn to use special purpose tools of this kind[1]. [1] https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 Toolformer: Language Models Can Teach Themselves to Use Tools
- Terr_ 3y ago
  Whereas your post sounds like "Just give the approach more time, it shall continue to incrementally improve until it finally works someday, cuz reasons." Early attempts at human flight approached it by strapping wings to people's arms and flapping: Do you think that would have eventually worked too, if only we had just given it a bit more time and faith?
  - xcv123 3y ago
    > Just give the approach more time, it shall continue to incrementally improve until it finally works someday, cuz reasons Yes. Because we haven't yet reached the limit of deep learning models. GPT-3.5 has 175 billion parameters. GPT-4 has an estimated 1.8 trillion parameters. That was nearly a year ago. Wait until you see what's next.
    - meheleventyone 3y ago
      Why would adding more parameters suddenly make it better at this sort of reasoning? It feels a bit of a “god of the gaps” where it’ll just stop being a stochastic parrot in just a few more million parameters.
      - Al-Khwarizmi 3y ago
        I don't think it's guaranteed, but I do think it's very plausible because we've seen these models gain emerging abilities at every iteration, just from sheer scaling. So extrapolation tells us that they may keep gaining more capabilities (we don't know how exactly it does it, though, so of course it's all speculation). I don't think many people would describe GPT-4 as a stochastic parrot already... when the paper that coined (or at least popularized) the term came up in early 2021, the term made a lot of sense. In late 2023, with models that at the very least show clear signs of creativity (I'm sticking to that because "reasoning" or not is more controversial), it's relegated to reductionistic philosophical arguments, but not really a practical description anymore.
        meheleventyone 3y ago
        I don’t think we should throw out the stochastic parrot so easily. As you say there are “clear signs of creativity” but that could be it getting significantly better as a stochastic parrot. We have no real test to tell mimicry apart from reasoning and as you note we also can only speculate about how any of it works. I don’t think it’s reductionist in light of that, maybe cautious or pessimistic.
        3y ago
  - londons_explore 3y ago
    > Early attempts at human flight approached it by strapping wings to people's arms and flapping: Do you think that would have eventually worked too, if only we had just given it a bit more time and faith? Interestingly, we how have human powered aircraft... We have flown ~60km with human leg power alone. We've also got human powered ornithopters (flapping wing designs) which can fly but only for very short times before the pilot is exhausted. I expect that another 100 years from now, both records will be exceeded, altough probably for scientific curiosity more than because human powered flight is actually useful.
    - ben_w 3y ago
      I knew about the legs (there was a model in the London Science Museum when I was a kid), but I didn't know about the ornithopter. https://en.wikipedia.org/wiki/UTIAS_Snowbird https://en.wikipedia.org/wiki/UTIAS_Snowbird 13 years ago! Wow, how did I miss that?

by wbhart 3y ago

gmerc 3y ago

After a year the entire argument you make boils down to “so far”.

ra 3y ago
Indeed. LLM is an application on a transformer trained with backpropagation. What stops you from adding a logic/mathematic "application" on the same transformer?
- seanhunter 3y ago
  Nothing, and there are methods which allow these types of models to learn to use special purpose tools of this kind[1]. [1] https://arxiv.org/abs/2302.04761 https://arxiv.org/abs/2302.04761 Toolformer: Language Models Can Teach Themselves to Use Tools
Terr_ 3y ago
Whereas your post sounds like "Just give the approach more time, it shall continue to incrementally improve until it finally works someday, cuz reasons." Early attempts at human flight approached it by strapping wings to people's arms and flapping: Do you think that would have eventually worked too, if only we had just given it a bit more time and faith?
- xcv123 3y ago
  > Just give the approach more time, it shall continue to incrementally improve until it finally works someday, cuz reasons Yes. Because we haven't yet reached the limit of deep learning models. GPT-3.5 has 175 billion parameters. GPT-4 has an estimated 1.8 trillion parameters. That was nearly a year ago. Wait until you see what's next.
  - meheleventyone 3y ago
    Why would adding more parameters suddenly make it better at this sort of reasoning? It feels a bit of a “god of the gaps” where it’ll just stop being a stochastic parrot in just a few more million parameters.
    - Al-Khwarizmi 3y ago
      I don't think it's guaranteed, but I do think it's very plausible because we've seen these models gain emerging abilities at every iteration, just from sheer scaling. So extrapolation tells us that they may keep gaining more capabilities (we don't know how exactly it does it, though, so of course it's all speculation). I don't think many people would describe GPT-4 as a stochastic parrot already... when the paper that coined (or at least popularized) the term came up in early 2021, the term made a lot of sense. In late 2023, with models that at the very least show clear signs of creativity (I'm sticking to that because "reasoning" or not is more controversial), it's relegated to reductionistic philosophical arguments, but not really a practical description anymore.
      - meheleventyone 3y ago
        I don’t think we should throw out the stochastic parrot so easily. As you say there are “clear signs of creativity” but that could be it getting significantly better as a stochastic parrot. We have no real test to tell mimicry apart from reasoning and as you note we also can only speculate about how any of it works. I don’t think it’s reductionist in light of that, maybe cautious or pessimistic.
        3y ago
- londons_explore 3y ago
  > Early attempts at human flight approached it by strapping wings to people's arms and flapping: Do you think that would have eventually worked too, if only we had just given it a bit more time and faith? Interestingly, we how have human powered aircraft... We have flown ~60km with human leg power alone. We've also got human powered ornithopters (flapping wing designs) which can fly but only for very short times before the pilot is exhausted. I expect that another 100 years from now, both records will be exceeded, altough probably for scientific curiosity more than because human powered flight is actually useful.
  - ben_w 3y ago
    I knew about the legs (there was a model in the London Science Museum when I was a kid), but I didn't know about the ornithopter. https://en.wikipedia.org/wiki/UTIAS_Snowbird https://en.wikipedia.org/wiki/UTIAS_Snowbird 13 years ago! Wow, how did I miss that?