5 ms·

I mostly agree. I really think chess is just a terrible example though. You're really asking a lot of it but I'm honestly shocked it can do what it can. It see

I mostly agree. I really think chess is just a terrible example though. You're really asking a lot of it but I'm honestly shocked it can do what it can. It seems to know some opening books, but falls down immediately. Which really makes sense because you'll find a lot of reading material on specific openings, but the problem space of the game is just too big to find texts about any given game state. Maybe if you have it reason about the board state and "think" about tactics you could push it farther. But we've already solved this. We have Stockfish et al and they've literally changed the game. Asking an LLM to play chess, while cool, is like trying to train a fish to dance. I think once we have an AI that's built with a bunch of different models that specialize in different things the idea of understanding is going to get even blurrier to the point that we might even say "yes, it doesn't 'understand' things, but it's better at humans at literally everything" so the difference becomes meaningless. I'm also of the opinion that humans are fancy automatons, so I tend to argue both sides. I'll say yeah it's thinking and so do we or ok it's not, but either do we.

unclebucknasty 3y ago
>Asking an LLM to play chess, while cool, is like trying to train a fish to dance. 100%. I didn't expect much from it going in. Was more curious to know how good/bad it might be and, really, whether it could play at all. But, I mention it here because it illustrates the disparity between understanding/reasoning (LLMs bad) and semantics (LLMs good), per the subject of the thread. >get even blurrier to the point that we might even say "yes, it doesn't 'understand' things, but it's better at humans at literally everything" so the difference becomes meaningless. I get exactly what you're saying here and I've wondered the same. But, that's the thing I'm not sure about, given its inability to reason. I mean, there's the idea that it doesn't matter whether it "understands" if it correctly answers. But, then I wonder how it can "truly" be better than humans at "literally everything", if only humans truly understand.
- famouswaffles 3y ago
  >Asking an LLM to play chess, while cool, is like trying to train a fish to dance. 100%. I didn't expect much from it going in. Was more curious to know how good/bad it might be and, really, whether it could play at all. >But, I mention it here because it illustrates the disparity between understanding/reasoning (LLMs bad) and semantics (LLMs good), per the subject of the thread. LLMs can play chess (links above) so i'm curious if you still have this opinion.
  - unclebucknasty 3y ago
    >i'm curious if you still have this opinion Largely. I'm not surprised that another model could be trained on a larger chess corpus or otherwise tuned for improvement. I actually alluded to that in my original comment above. In fact, I was really surprised that gpt4 was so bad, given the total volume of training data it had obviously ingested. So, my initial point was around how that revealed the disparity between LLMs appearing to understand versus actually understanding (i.e. engaging in reasoning).
    - famouswaffles 3y ago
      If i came upon a random stranger who did not know how to play chess (but otherwise had heard of the game) and i put the board in front of him, made my move and told him to proceed, he will almost certainly move a piece to some other position on the board. There is no difference between this exercise and the one you have given GPT-4. In fact, the results are manifested the exact same way. By at most a few moves in, i will notice that both the stranger and GPT-4 are moving pieces to some general area of the board but woefully failing to adhere to the rules of the game. "The stranger can not reason" would be an absurd conclusion. Swapping out The stranger for GPT-4 does not make it any less so. So really by this standard then Humans regularly only "appear to understand" a great number of things. I can get behind this, but I doubt this is your conclusion. It seems to me that you were using "appear to understand" and "understand" as something that separates Humans from LLMs.
      - bongodongobob 3y ago
        This is a fantastic argument. Great food for thought.
      - unclebucknasty 3y ago
        Except that the premise of ChatGPT is that it "knows" things, and that you can ask it about these things in specific ways and it will answer. Further, that knowledge represents a vast body of training data that certainly includes the rules of chess, how pieces move, etc. This is verifiable. Go ask ChatGPT those questions now. How does a bishop or rook move? Can a piece move through or over another piece, etc. What is en passant? When can it be employed? You will find that it answers these correctly, and even provides examples. It talks about ranks and files, and authoritatively sprinkles in other knowledge related to the questions. But, then you put a board in front of it and you ask it to make moves, and it doesn't adhere to these rules. You're not asking it to win the game. Just make a legal move. But it cannot, even while confidently responding with invalid moves. So, it's not about whether it's been tuned to be a grandmaster. It's about whether it can even execute on the knowledge it clearly possesses. Now, turning back to your analogy with a human player. You ask Gary all of these questions and he gives you the answers perfectly, so you recognize that his factual knowledge is intact. But, despite his best efforts, he repeatedly makes illegal moves. You would have to conclude that there is some problem with reasoning or understanding what those facts actually mean in practice. That is the difference between appearing to understand and actually understanding. And certainly, there are some humans who fit this "appearance" description as well, perhaps through some processing deficit or otherwise. But as a broad category, this is something humans do quite well. The concept of understanding is really not complicated. When we explain something to someone and we ask the question "do you understand?", we're not asking whether they can memorize or repeat back what we just told them. We're asking something else. That something else is the well-understood (irony intended) meaning of "understand".
famouswaffles 3y ago
>I really think chess is just a terrible example though. You're really asking a lot of it but I'm honestly shocked it can do what it can. It seems to know some opening books, but falls down immediately. Which really makes sense because you'll find a lot of reading material on specific openings, but the problem space of the game is just too big to find texts about any given game state. Maybe if you have it reason about the board state and "think" about tactics you could push it farther. But we've already solved this. Just so you know, LLMs can play chess just fine. Like this isn't some inability. GPT-3.5-instruct-turbo plays at about 1800 Elo and makes legal moves consistently well past openings. https://github.com/adamkarvonen/chess_gpt_eval https://github.com/adamkarvonen/chess_gpt_eval https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html https://adamkarvonen.github.io/machine_learning/2024/01/03/c...
- unclebucknasty 3y ago
  From the README: >Per move, a model gets 5 illegal moves before forced resignation of the round. >Most of gpt-4's losses were due to illegal moves, so it may be possible to come up with a prompt to have gpt-4 correct illegal moves and improve its score. My experience exactly with gpt4. I first solved by keeping a list of illegal moves and subsequently prompting to move again and explicitly exclude those moves. Idea was to constrain gpt as little as possible, but that became inefficient. So I began to prompt for each move with the complete set of legal moves to choose from. In any case, at a certain point it becomes somewhat random when the goal is just to produce a legal move. But, it's interesting that producing legal moves is a challenge for gpt4, especially given that it's such a discrete problem.
- bongodongobob 3y ago
  The github you linked shows ~1200, not 1800 which is orders of magnitude difference. The other is fine tuning, which is out of scope. A 1200 has something like a 1% chance of beating an 1800. 1200 is sort of you know the rules and maybe have a general idea of an openings, make weak tactical moves mid game and poor concept of endgame. It's just too "deep" for an LLM and a poor standard regardless. As an avid chess and GPT person, I've tried to get it to play decent chess but can't. Neither of your links support it, but if you've got anything else, send it.

unclebucknasty 3y ago

>Asking an LLM to play chess, while cool, is like trying to train a fish to dance. 100%. I didn't expect much from it going in. Was more curious to know how good/bad it might be and, really, whether it could play at all. But, I mention it here because it illustrates the disparity between understanding/reasoning (LLMs bad) and semantics (LLMs good), per the subject of the thread. >get even blurrier to the point that we might even say "yes, it doesn't 'understand' things, but it's better at humans at literally everything" so the difference becomes meaningless. I get exactly what you're saying here and I've wondered the same. But, that's the thing I'm not sure about, given its inability to reason. I mean, there's the idea that it doesn't matter whether it "understands" if it correctly answers. But, then I wonder how it can "truly" be better than humans at "literally everything", if only humans truly understand.

famouswaffles 3y ago
>Asking an LLM to play chess, while cool, is like trying to train a fish to dance. 100%. I didn't expect much from it going in. Was more curious to know how good/bad it might be and, really, whether it could play at all. >But, I mention it here because it illustrates the disparity between understanding/reasoning (LLMs bad) and semantics (LLMs good), per the subject of the thread. LLMs can play chess (links above) so i'm curious if you still have this opinion.
- unclebucknasty 3y ago
  >i'm curious if you still have this opinion Largely. I'm not surprised that another model could be trained on a larger chess corpus or otherwise tuned for improvement. I actually alluded to that in my original comment above. In fact, I was really surprised that gpt4 was so bad, given the total volume of training data it had obviously ingested. So, my initial point was around how that revealed the disparity between LLMs appearing to understand versus actually understanding (i.e. engaging in reasoning).
  - famouswaffles 3y ago
    If i came upon a random stranger who did not know how to play chess (but otherwise had heard of the game) and i put the board in front of him, made my move and told him to proceed, he will almost certainly move a piece to some other position on the board. There is no difference between this exercise and the one you have given GPT-4. In fact, the results are manifested the exact same way. By at most a few moves in, i will notice that both the stranger and GPT-4 are moving pieces to some general area of the board but woefully failing to adhere to the rules of the game. "The stranger can not reason" would be an absurd conclusion. Swapping out The stranger for GPT-4 does not make it any less so. So really by this standard then Humans regularly only "appear to understand" a great number of things. I can get behind this, but I doubt this is your conclusion. It seems to me that you were using "appear to understand" and "understand" as something that separates Humans from LLMs.
    - bongodongobob 3y ago
      This is a fantastic argument. Great food for thought.
    - unclebucknasty 3y ago
      Except that the premise of ChatGPT is that it "knows" things, and that you can ask it about these things in specific ways and it will answer. Further, that knowledge represents a vast body of training data that certainly includes the rules of chess, how pieces move, etc. This is verifiable. Go ask ChatGPT those questions now. How does a bishop or rook move? Can a piece move through or over another piece, etc. What is en passant? When can it be employed? You will find that it answers these correctly, and even provides examples. It talks about ranks and files, and authoritatively sprinkles in other knowledge related to the questions. But, then you put a board in front of it and you ask it to make moves, and it doesn't adhere to these rules. You're not asking it to win the game. Just make a legal move. But it cannot, even while confidently responding with invalid moves. So, it's not about whether it's been tuned to be a grandmaster. It's about whether it can even execute on the knowledge it clearly possesses. Now, turning back to your analogy with a human player. You ask Gary all of these questions and he gives you the answers perfectly, so you recognize that his factual knowledge is intact. But, despite his best efforts, he repeatedly makes illegal moves. You would have to conclude that there is some problem with reasoning or understanding what those facts actually mean in practice. That is the difference between appearing to understand and actually understanding. And certainly, there are some humans who fit this "appearance" description as well, perhaps through some processing deficit or otherwise. But as a broad category, this is something humans do quite well. The concept of understanding is really not complicated. When we explain something to someone and we ask the question "do you understand?", we're not asking whether they can memorize or repeat back what we just told them. We're asking something else. That something else is the well-understood (irony intended) meaning of "understand".

famouswaffles 3y ago

>I really think chess is just a terrible example though. You're really asking a lot of it but I'm honestly shocked it can do what it can. It seems to know some opening books, but falls down immediately. Which really makes sense because you'll find a lot of reading material on specific openings, but the problem space of the game is just too big to find texts about any given game state. Maybe if you have it reason about the board state and "think" about tactics you could push it farther. But we've already solved this. Just so you know, LLMs can play chess just fine. Like this isn't some inability. GPT-3.5-instruct-turbo plays at about 1800 Elo and makes legal moves consistently well past openings. https://github.com/adamkarvonen/chess_gpt_eval https://github.com/adamkarvonen/chess_gpt_eval https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html https://adamkarvonen.github.io/machine_learning/2024/01/03/c...

unclebucknasty 3y ago
From the README: >Per move, a model gets 5 illegal moves before forced resignation of the round. >Most of gpt-4's losses were due to illegal moves, so it may be possible to come up with a prompt to have gpt-4 correct illegal moves and improve its score. My experience exactly with gpt4. I first solved by keeping a list of illegal moves and subsequently prompting to move again and explicitly exclude those moves. Idea was to constrain gpt as little as possible, but that became inefficient. So I began to prompt for each move with the complete set of legal moves to choose from. In any case, at a certain point it becomes somewhat random when the goal is just to produce a legal move. But, it's interesting that producing legal moves is a challenge for gpt4, especially given that it's such a discrete problem.
bongodongobob 3y ago
The github you linked shows ~1200, not 1800 which is orders of magnitude difference. The other is fine tuning, which is out of scope. A 1200 has something like a 1% chance of beating an 1800. 1200 is sort of you know the rules and maybe have a general idea of an openings, make weak tactical moves mid game and poor concept of endgame. It's just too "deep" for an LLM and a poor standard regardless. As an avid chess and GPT person, I've tried to get it to play decent chess but can't. Neither of your links support it, but if you've got anything else, send it.