4 ms·

> since the issue is general I'm not sure what that means specifically. I don't agree overall. Only certain types of problems encountered by LLMs map cleanly t

by jmmcd 2y ago

> since the issue is general I'm not sure what that means specifically. I don't agree overall. Only certain types of problems encountered by LLMs map cleanly to well-understood problems where existing solvers are perfect.

mdp2021 2y ago
I am stating that since the ability to solve those puzzles is critical in an intelligence, and the general questions I can think of require an intelligence as processor, if to solve those problems the LLMs "should write code" then in general they should. All problems require proficient reasoning to get a proper solution - not only puzzles. Without proper reasoning you can get some "heuristic", which can only be useful if you only needed an unreliable result based on "grosso modo" criteria.
- jmmcd 2y ago
  > Without proper reasoning you can get some "heuristic" Right, but the question is whether this is good enough. And what counts as "proper". A lot of what we call proper reasoning is still quite informal, and even mathematics is usually not formal enough to be converted directly into a formal language like Coq. So this is a deep question: is talking reasoning? Humans talk (out loud, or in their heads). Are they then reasoning? Sure, some of what happens internally is not just self-talk, but the thought experiment goes: if the problem is not completely ineffable, then (a bit like Borges' library) there is some 1000-word text which is the best possible reasoned, witty, English-language 1000-word solution to the problem. In principle, an LLM can generate that. If your goal is a reductio, ie my statement must be false since it implies models should write code for every problem - then I disagree, because while the ability to solve these problems might be a requirement to be deemed "an intelligence", nonetheless many other problems which require an intelligence don't require the ability to solve these problems.
  - mdp2021 2y ago
    > Are they then reasoning Reasoning properly is at least operating through processes that output correct results. > Borges' library Which in fact is exactly made of "non-texts" (the process that produces them is `String s = numToString(n++);` - they are encoded numbers, not "weaved ideas"). > many other problems which require an intelligence don't require the ability to solve these problems Which ones? Which problems that demand producing correct solutions could be solved by a general processor which could not solve a "detective game"?
    - jmmcd 2y ago
      > Reasoning properly is at least operating through processes that output correct results. Human "reasoning" (ie speech or self-talk that sounds a bit like reasoning) often outputs correct results. Does "often" fit the definition? > Which problems that demand producing correct solutions could be solved by a processor which could not solve a "detective game"? For example, "what colour is the sky right now?". A lot of people could solve this (even if they haven't looked outside), and so could a lot of language models, which can't solve this detective game.
      - mdp2021 2y ago
        > Does "often" fit the definition? No: "proper reasoning" is that process which given sufficient input will surely bring to a correct output owing to the effectiveness of its inner workings. > what colour is the sky right now That is not a general problem solver, and "output the most common recorded reply to a question" is certainly not a general problem solver, and the responses from the box indicated will easily be worthless for all special cases in which the question will make sense.
    - jmmcd 2y ago
      I can't reply to your new post below, I guess the thread is too deep. But you've bit the bullet and stated that what humans do is not reasoning, I think. You didn't like "what colour is the sky" (without looking), ok. "Given the following [unseen during training] page of text, can you guess what emotion the main character is feeling at the end?" This is a problem that a human can solve, and many LLMs can solve, even if they can't solve the detective puzzle. In case it doesn't sound important, this can be reframed as a customer-service sentiment-recognition problem.

by jmmcd 2y ago

mdp2021 2y ago

I am stating that since the ability to solve those puzzles is critical in an intelligence, and the general questions I can think of require an intelligence as processor, if to solve those problems the LLMs "should write code" then in general they should. All problems require proficient reasoning to get a proper solution - not only puzzles. Without proper reasoning you can get some "heuristic", which can only be useful if you only needed an unreliable result based on "grosso modo" criteria.

jmmcd 2y ago
> Without proper reasoning you can get some "heuristic" Right, but the question is whether this is good enough. And what counts as "proper". A lot of what we call proper reasoning is still quite informal, and even mathematics is usually not formal enough to be converted directly into a formal language like Coq. So this is a deep question: is talking reasoning? Humans talk (out loud, or in their heads). Are they then reasoning? Sure, some of what happens internally is not just self-talk, but the thought experiment goes: if the problem is not completely ineffable, then (a bit like Borges' library) there is some 1000-word text which is the best possible reasoned, witty, English-language 1000-word solution to the problem. In principle, an LLM can generate that. If your goal is a reductio, ie my statement must be false since it implies models should write code for every problem - then I disagree, because while the ability to solve these problems might be a requirement to be deemed "an intelligence", nonetheless many other problems which require an intelligence don't require the ability to solve these problems.
- mdp2021 2y ago
  > Are they then reasoning Reasoning properly is at least operating through processes that output correct results. > Borges' library Which in fact is exactly made of "non-texts" (the process that produces them is `String s = numToString(n++);` - they are encoded numbers, not "weaved ideas"). > many other problems which require an intelligence don't require the ability to solve these problems Which ones? Which problems that demand producing correct solutions could be solved by a general processor which could not solve a "detective game"?
  - jmmcd 2y ago
    > Reasoning properly is at least operating through processes that output correct results. Human "reasoning" (ie speech or self-talk that sounds a bit like reasoning) often outputs correct results. Does "often" fit the definition? > Which problems that demand producing correct solutions could be solved by a processor which could not solve a "detective game"? For example, "what colour is the sky right now?". A lot of people could solve this (even if they haven't looked outside), and so could a lot of language models, which can't solve this detective game.
    - mdp2021 2y ago
      > Does "often" fit the definition? No: "proper reasoning" is that process which given sufficient input will surely bring to a correct output owing to the effectiveness of its inner workings. > what colour is the sky right now That is not a general problem solver, and "output the most common recorded reply to a question" is certainly not a general problem solver, and the responses from the box indicated will easily be worthless for all special cases in which the question will make sense.
  - jmmcd 2y ago
    I can't reply to your new post below, I guess the thread is too deep. But you've bit the bullet and stated that what humans do is not reasoning, I think. You didn't like "what colour is the sky" (without looking), ok. "Given the following [unseen during training] page of text, can you guess what emotion the main character is feeling at the end?" This is a problem that a human can solve, and many LLMs can solve, even if they can't solve the detective puzzle. In case it doesn't sound important, this can be reframed as a customer-service sentiment-recognition problem.