4 ms·

>it is repeating the most common... This nonsense hasn't been true since GPT-2, and even before that it was a poor description. For instance, do you think one

>it is repeating the most common... This nonsense hasn't been true since GPT-2, and even before that it was a poor description. For instance, do you think one just solves dozens of Erdős problems with the "most common textual sequence": https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems https://github.com/teorth/erdosproblems/wiki/AI-contribution...

applfanboysbgon 6mo ago
A slight oversimplification, as LLMs are also capable of generating the most statistically plausible textual sequence, which can be a sequence not found in the dataset but rather a synthesized combination of the likely sequences of multiple preceding sets of tokens, but yes, that is in fact what it is doing. Computer software does what it is programmed to do, and LLMs are not programmed to do logical inference in any capacity but rather operate entirely on probabilities learned from a mind-bogglingly large corpus of text (influenced by things like RLHF, which is still just massaging probabilities). The claims about solving Erdos problems have been wildly overstated, and notably pushed by people who have a very large financial stake in hyping up LLMs. Nonetheless, I did not say that LLMs are useless. If they are trained on sufficient data, it should not be surprising that correct answers are probabilistically likely to occur. Like any computer software, that makes them a useful tool. It does not make them in any way intelligent, any more than a calculator would be considered intelligent despite being completely superior to human intelligence in accomplishing their given task.
- frozenseven 6mo ago
  >not programmed to do logical inference in any capacity Yet have no problem doing so when solving Erdős problems. This isn't up for debate at this point. >The claims about solving Erdos problems have been wildly overstated These are verified solutions. They exist, are not trivial, and are of obvious interest to the math community. Take it up with Terence Tao and co. >pushed by people who have a very large financial stake in hyping up LLMs Libel. >It does not make them in any way intelligent Word games.
  - applfanboysbgon 6mo ago
    > This isn't up for debate at this point. If by not up for debate, you mean that it is delusional and literally evidence of psychosis to suggest that computer software is doing something it is not programmed to do, you would be correct. Probabilistic analysis can carry you very, very far in doing something that looks like logical inference at the surface level, but it is nonetheless not logical inference. LLM models have been getting increasingly good at factoring in larger and longer contexts and still managing to generate plausibly correct answers, becoming more and more useful all the while, but are still not capable of logical inference. This is why your genius mathematician AGI consciousness stumbles on trivial logic puzzles it has not seen before like the car wash meme.
    - frozenseven 6mo ago
      >delusional and literally evidence of psychosis to suggest that computer software is doing something it is not programmed to do These are just insults and outright lies, and you know that. We're done here. AI progress from here on out will be extra sweet.
      - card_zero 6mo ago
        You don't have the ability to predict progress, either.
        frozenseven 6mo ago
        Well, I'm not clairvoyant, but this is a very easy prediction to make. And we're not talking about decades in the future, this is simply a matter of letting the near-future unfold.
    - Mithriil 5mo ago
      > Probabilistic analysis can carry you very, very far in doing something that looks like logical inference at the surface level, but it is nonetheless not logical inference. A statistical approximation of logical inference (as vague as I state it) could (and will) very well pass for logical inference, at least for the common people, whose logic skills are far from perfect. Also, humans are certainly not capable of the perfect logical inference you speak of. And I get the irony of what I'm saying with such certitude. Logic is still framed in axioms that are framed in languages, we'll never truly get there. Ah, but absoluteness gets in the way of practicality. Yet, here we are with a tool, that is maybe not at its prime yet, that equals and beat many human beings at logical inference on some problems that are pragmatically relevant. Should I say symptoms of logical inference at that point? As to why LLMs capacity for (apparent) logical inference is only limited to specific use cases, I don't have a clue. But I'd like to argue that, humans are like that too.

applfanboysbgon 6mo ago

A slight oversimplification, as LLMs are also capable of generating the most statistically plausible textual sequence, which can be a sequence not found in the dataset but rather a synthesized combination of the likely sequences of multiple preceding sets of tokens, but yes, that is in fact what it is doing. Computer software does what it is programmed to do, and LLMs are not programmed to do logical inference in any capacity but rather operate entirely on probabilities learned from a mind-bogglingly large corpus of text (influenced by things like RLHF, which is still just massaging probabilities). The claims about solving Erdos problems have been wildly overstated, and notably pushed by people who have a very large financial stake in hyping up LLMs. Nonetheless, I did not say that LLMs are useless. If they are trained on sufficient data, it should not be surprising that correct answers are probabilistically likely to occur. Like any computer software, that makes them a useful tool. It does not make them in any way intelligent, any more than a calculator would be considered intelligent despite being completely superior to human intelligence in accomplishing their given task.

frozenseven 6mo ago
>not programmed to do logical inference in any capacity Yet have no problem doing so when solving Erdős problems. This isn't up for debate at this point. >The claims about solving Erdos problems have been wildly overstated These are verified solutions. They exist, are not trivial, and are of obvious interest to the math community. Take it up with Terence Tao and co. >pushed by people who have a very large financial stake in hyping up LLMs Libel. >It does not make them in any way intelligent Word games.
- applfanboysbgon 6mo ago
  > This isn't up for debate at this point. If by not up for debate, you mean that it is delusional and literally evidence of psychosis to suggest that computer software is doing something it is not programmed to do, you would be correct. Probabilistic analysis can carry you very, very far in doing something that looks like logical inference at the surface level, but it is nonetheless not logical inference. LLM models have been getting increasingly good at factoring in larger and longer contexts and still managing to generate plausibly correct answers, becoming more and more useful all the while, but are still not capable of logical inference. This is why your genius mathematician AGI consciousness stumbles on trivial logic puzzles it has not seen before like the car wash meme.
  - frozenseven 6mo ago
    >delusional and literally evidence of psychosis to suggest that computer software is doing something it is not programmed to do These are just insults and outright lies, and you know that. We're done here. AI progress from here on out will be extra sweet.
    - card_zero 6mo ago
      You don't have the ability to predict progress, either.
      - frozenseven 6mo ago
        Well, I'm not clairvoyant, but this is a very easy prediction to make. And we're not talking about decades in the future, this is simply a matter of letting the near-future unfold.
  - Mithriil 5mo ago
    > Probabilistic analysis can carry you very, very far in doing something that looks like logical inference at the surface level, but it is nonetheless not logical inference. A statistical approximation of logical inference (as vague as I state it) could (and will) very well pass for logical inference, at least for the common people, whose logic skills are far from perfect. Also, humans are certainly not capable of the perfect logical inference you speak of. And I get the irony of what I'm saying with such certitude. Logic is still framed in axioms that are framed in languages, we'll never truly get there. Ah, but absoluteness gets in the way of practicality. Yet, here we are with a tool, that is maybe not at its prime yet, that equals and beat many human beings at logical inference on some problems that are pragmatically relevant. Should I say symptoms of logical inference at that point? As to why LLMs capacity for (apparent) logical inference is only limited to specific use cases, I don't have a clue. But I'd like to argue that, humans are like that too.