7 ms·

What are you trying to point out here ? Is there any question you can ask today that is not dependent on some existing knowledge that an AI would have seen ?

• razorbeamz 6mo ago
  The point I'm trying to make is that all LLM output is based on likelihood of one word coming after the next word based on the prompt. That is literally all it's doing. It's not "thinking." It's not "solving." It's simply stringing words together in a way that appears most likely. ChatGPT cannot do math. It can only string together words and numbers in a way that can convince an outsider that it can do math. It's a parlor trick, like Clever Hans [1]. A very impressive parlor trick that is very convincing to people who are not familiar with what it's doing, but a parlor trick nontheless. [1] https://en.wikipedia.org/wiki/Clever_Hans https://en.wikipedia.org/wiki/Clever_Hans
  • trick-or-treat 6mo ago
    > all LLM output is based on likelihood of one word coming after the next word based on the prompt. Right but it has to reason about what that next word should be. It has to model the problem and then consider ways to approach it.
    • razorbeamz 6mo ago
      No, it does not reason anything. LLM "reasoning" is just an illusion. When an LLM is "reasoning" it's just feeding its own output back into itself and giving it another go.
      • fenomas 6mo ago
        This is like saying chess engines don't actually "play" chess, even though they trounce grandmasters. It's a meaningless distinction, about words (think, reason, ..) that have no firm definitions.
        • trick-or-treat 6mo ago
          This exactly. The proof is in the pudding. If AI pudding is as good as (or better than) human pudding, and you continue to complain about it anyway... You're just being biased and unreasonable. And by the way, I don't think it's surprising that so many people are being unreasonable on this issue, there is a lot at stake and it's implications are transformative.
        • razorbeamz 6mo ago
          Chess engines are not a comparable thing. Chess is a solved game. There is always a mathematically perfect move.
          • sincerely 6mo ago
            Chess is absolutely not a solved game, outside of very limited situations like endgames. Just because a best move exists does not mean we (or even an engine) know what it is
          • trick-or-treat 6mo ago
            > Chess is a solved game. There is always a mathematically perfect move. This is a good example of being confidently misinformed. The best move is always a result of calculation. And the calculation can always go deeper or run on a stronger engine.
          • Scarblac 6mo ago
            We know that chess can be solved, in theory. It absolutely isn't and probably will never be in practice. The necessary time and storage space doesn't exist.
      • Scarblac 6mo ago
        Is that so different from brains? Even if it is, this sounds like "this submarine doesn't actually swim" reasoning.
  • brenschluss 6mo ago
    sigh; this argument is the new Chinese Room; easily described, utterly wrong. https://www.youtube.com/watch?v=YEUclZdj_Sc https://www.youtube.com/watch?v=YEUclZdj_Sc
    • razorbeamz 6mo ago
      Next-token-prediction cannot do calculations. That is fundamental. It can produce outputs that resemble calculations. It can prompt an agent to input some numbers into a separate program that will do calculations for it and then return them as a prompt. Neither of these are calculations.
      • parasubvert 6mo ago
        Humans can't do calculations either, by your definition. Only computers can.
        • datsci_est_2015 6mo ago
          Third things can exist. In other words, you’re implying a false dichotomy between “human computation” and “computer computation” and implying that LLMs must be one or the other. A pithy gotcha comment, no doubt. Edit: the implication comes from demanding that the OP’s definition must be rigorous enough to cover all models of “computation”, and by failing to do so, it means that LLMs must be more like humans than computers.
      • gf000 6mo ago
        So you don't think 50T parameter neural networks can encode the logic for adding two n-bit integers for reasonably sized integers? That would be pretty sad.
        • razorbeamz 6mo ago
          They do not. The fundamental technology behind LLMs does not allow that to be the case. You are hoping that an LLM can do something that it cannot do.
  • TheLNL 6mo ago
    > ChatGPT cannot do math. It can only string together words and numbers in a way that can convince an outsider that it can do math What am I as a human doing when I "Do math" ? 1.I am looking at the problem at hand, identifying what I have and what I need to get 2.I am then doing a prediction using my pretrained neural net to find possible courses of action to go in a direction that "feels" right 3.I am using my pretrained neural net to find pairs of values that I can substitute with each other (Think multiplication tables, standard results, etc...) 4.Repeat till I arrive at the answer or give up. As a simple example, when I try to find 600×74+42 I remember the steps for multiplication. I recall the associated pairs of numbers from my tables and complete the multiplication step by step. I then recall the associated pairs of numbers for addition of single digits and add from left to right. We need to remember that just because we are fast at doing this and are able to do it subconsciously it doesn't mean that we can natively do math, we just do association of information using the neural networks we have trained.