3 ms·

so prove it! get a public repo out there, have it play against some open source engines also I think the operative letter in AGI is the G - and if the G is sho

by paimapi 18d ago

so prove it! get a public repo out there, have it play against some open source engines also I think the operative letter in AGI is the G - and if the G is short for 'variably competent savant-like hyperfocus on certain kinds of software coding and not any other general skill' then its not really G at all, is it?

BobbyJo 18d ago
I suck at chess. Are you saying I can't be intelligent?
- paimapi 18d ago
  is that what I'm saying? or am I talking about AGI? perhaps there's some irony here to be explored when it comes to basic reading comprehension gaps
  - jibal 18d ago
    That's a polite way to put it. :-)
  - BobbyJo 18d ago
    My point was you are misunderstanding G, or at least applying it erroneously here. Being good at chess is not a generalization of any other body of knowledge, it is a rigorous set of rules. The only way to be good at chess is to practice chess, or to apply deep calculations. The latter is the model writing code. The illegal move aspect has more to do with a failure of online/in-context learning, which would support your point. I tend to think it is a byproduct of reasoning in language, which newer architectures would fix, but we shall see.
    - paimapi 17d ago
      chess is not just a rigorous set of rules, it is rules as foundation with layers of strategy on top. and so is, for example, scientific methodology or chemical interactions or virtually everything else under-the-sun that comprises human knowledge knowledge for chess is derived from memorizing strategies that have been well-defined for decades paired with in-game reasoning processes. this is not at all different from any other body of knowledge. Noble gases, laws of thermodynamics, organic chemistry just to name a few - these are all 'strategies' that define observed phenomena, analytical frameworks that trace a logical, rational set of interactions and which can predict the next for an AGI, all of this should be a cakewalk, trained as it were to surpass human capability in any and every domain [0] (thus the G for 'general' and not 'N' for 'narrow' [1]). it should be a natural at everything, infinitely adaptable on-the-fly. the whole point of AGI is that it surpasses human capabilities even at our frontiers and bleeding edge (unless you're private enterprise and you've moved the goalposts for industry [2]) currently, it's only AGI-seeming if it gets benchmaxxed enough. otherwise it sucks at what it does and then is only barely competent at tasks if paired with enough skills and tests to make it more diligent at its work. this makes sense to me - for any probabilistically trained tool, even one that you post-train and fill with nothing but the best-quality evidence, the ultimate result is the lowest-common-denominator output for your sample set. there's no natural reasoning the AI does itself to make itself better at what it does - it's all human curation and categorization of sources ingested paired with RLHF post-training that we can get the mediocre-at-chess-at-best results that we see now and the benchmaxxed scores against whatever arbitrary and pre-defined measure that's not AGI by any classical definition. that's a cool, useful, and powerful tool that makes our lives easier, much like a hammer, nail, and studs make mounting a picture frame easier than if we only had our hands alone [0] https://ischool.syracuse.edu/types-of-ai/#:~:text=General%20AI%20%28Strong%20AI https://ischool.syracuse.edu/types-of-ai/#:~:text=General%20... [1] https://aiethicslab.rutgers.edu/e-floating-buttons/weak-ai-narrow-ai/ https://aiethicslab.rutgers.edu/e-floating-buttons/weak-ai-n... [2] https://aibusiness.com/ml/what-exactly-is-artificial-general-intelligence-ask-deepmind- https://aibusiness.com/ml/what-exactly-is-artificial-general...
      - BobbyJo 17d ago
        > an AGI, all of this should be a cakewalk, trained as it were to surpass human capability in any and every domain. AGI != ASI. You are confusing the two.
        paimapi 17d ago
        I'm not. AGI is almost necessarily closer to ASI than it is to human intelligence by definition. it's become pretty obvious that even what seems like irrelevant domain knowledge has utility applied to other domains - that's why we're pursuing general-use models presumably, an 'AGI' that is generally as good as a really good human at every task under-the-sun will already be much better than most humans at the task because it can incorporate cross-domain knowledge and apply it in a reasonable fashion. it's like the parable of Newton and the apple - the domain knowledge that an apple falls according to certain rules observed through historic experience igniting the creative spark that led to universal gravitation
        BobbyJo 17d ago
        > presumably, an 'AGI' that is generally as good as a really good human at every task under-the-sun will already be much better than most humans at the task because it can incorporate cross-domain knowledge and apply it in a reasonable fashion. I disagree with this definition of AGI, and I disagree that chess skills significantly benefit from generalizing non-chess knowledge, outside of computing moves probabilistically. AGI has historically been defined as human level or better, with generality to new domains. I think blurring it with ASI makes the terminology confusing to use. Chess is learned rules and the ability to apply those rules. Strategy as a whole is applying a set of rules to circumstances, that's how it is taught: "here are examples of circumstances and actions, try to pattern match to future circumstance and apply commensurate action." If you make the point that chess is a large part of the training data, or that LLMs are unable to learn chess well, I'll accept that as refuting that LLMs are AGI, but these other points I disagree with.
- nmehner 18d ago
  If you read all chess tutorials, strategy documentation and game archives on the internet and then would still suck at chess: yes.
  - brindleth 18d ago
    Declarative knowledge is not the same as procedural knowledge. You can read as many chess tutorials, strategy documentation and game archives as you like, they won't make you good at chess until you actually start practicing chess.
    - _superposition_ 18d ago
      Does practical improvement apply to only humans or intelligence in general?
- foldr 18d ago
  The issue with the models isn't that they play a bad game, but that they persist in making illegal moves. An average intelligent human can be told the rules of chess and then play chess, badly, within the rules.
  - empath75 17d ago
    An average human would have a physical chess board in front of them to remind them of the current state.
    - foldr 17d ago
      Sure, but the LLM is free to construct a representation of the chess board and update it as it goes along. It is not in any way banned from using a virtual board, or whatever representation of game state it pleases. AFAIK, current models will still sometimes make illegal moves even if given the entire game state (e.g. in FEN notation), so it is not purely an issue with the models’ ability to keep track of sequences of moves.

by paimapi 18d ago

BobbyJo 18d ago

I suck at chess. Are you saying I can't be intelligent?

paimapi 18d ago
is that what I'm saying? or am I talking about AGI? perhaps there's some irony here to be explored when it comes to basic reading comprehension gaps
- jibal 18d ago
  That's a polite way to put it. :-)
- BobbyJo 18d ago
  My point was you are misunderstanding G, or at least applying it erroneously here. Being good at chess is not a generalization of any other body of knowledge, it is a rigorous set of rules. The only way to be good at chess is to practice chess, or to apply deep calculations. The latter is the model writing code. The illegal move aspect has more to do with a failure of online/in-context learning, which would support your point. I tend to think it is a byproduct of reasoning in language, which newer architectures would fix, but we shall see.
  - paimapi 17d ago
    chess is not just a rigorous set of rules, it is rules as foundation with layers of strategy on top. and so is, for example, scientific methodology or chemical interactions or virtually everything else under-the-sun that comprises human knowledge knowledge for chess is derived from memorizing strategies that have been well-defined for decades paired with in-game reasoning processes. this is not at all different from any other body of knowledge. Noble gases, laws of thermodynamics, organic chemistry just to name a few - these are all 'strategies' that define observed phenomena, analytical frameworks that trace a logical, rational set of interactions and which can predict the next for an AGI, all of this should be a cakewalk, trained as it were to surpass human capability in any and every domain [0] (thus the G for 'general' and not 'N' for 'narrow' [1]). it should be a natural at everything, infinitely adaptable on-the-fly. the whole point of AGI is that it surpasses human capabilities even at our frontiers and bleeding edge (unless you're private enterprise and you've moved the goalposts for industry [2]) currently, it's only AGI-seeming if it gets benchmaxxed enough. otherwise it sucks at what it does and then is only barely competent at tasks if paired with enough skills and tests to make it more diligent at its work. this makes sense to me - for any probabilistically trained tool, even one that you post-train and fill with nothing but the best-quality evidence, the ultimate result is the lowest-common-denominator output for your sample set. there's no natural reasoning the AI does itself to make itself better at what it does - it's all human curation and categorization of sources ingested paired with RLHF post-training that we can get the mediocre-at-chess-at-best results that we see now and the benchmaxxed scores against whatever arbitrary and pre-defined measure that's not AGI by any classical definition. that's a cool, useful, and powerful tool that makes our lives easier, much like a hammer, nail, and studs make mounting a picture frame easier than if we only had our hands alone [0] https://ischool.syracuse.edu/types-of-ai/#:~:text=General%20AI%20%28Strong%20AI https://ischool.syracuse.edu/types-of-ai/#:~:text=General%20... [1] https://aiethicslab.rutgers.edu/e-floating-buttons/weak-ai-narrow-ai/ https://aiethicslab.rutgers.edu/e-floating-buttons/weak-ai-n... [2] https://aibusiness.com/ml/what-exactly-is-artificial-general-intelligence-ask-deepmind- https://aibusiness.com/ml/what-exactly-is-artificial-general...
    - BobbyJo 17d ago
      > an AGI, all of this should be a cakewalk, trained as it were to surpass human capability in any and every domain. AGI != ASI. You are confusing the two.
      - paimapi 17d ago
        I'm not. AGI is almost necessarily closer to ASI than it is to human intelligence by definition. it's become pretty obvious that even what seems like irrelevant domain knowledge has utility applied to other domains - that's why we're pursuing general-use models presumably, an 'AGI' that is generally as good as a really good human at every task under-the-sun will already be much better than most humans at the task because it can incorporate cross-domain knowledge and apply it in a reasonable fashion. it's like the parable of Newton and the apple - the domain knowledge that an apple falls according to certain rules observed through historic experience igniting the creative spark that led to universal gravitation
        BobbyJo 17d ago
        > presumably, an 'AGI' that is generally as good as a really good human at every task under-the-sun will already be much better than most humans at the task because it can incorporate cross-domain knowledge and apply it in a reasonable fashion. I disagree with this definition of AGI, and I disagree that chess skills significantly benefit from generalizing non-chess knowledge, outside of computing moves probabilistically. AGI has historically been defined as human level or better, with generality to new domains. I think blurring it with ASI makes the terminology confusing to use. Chess is learned rules and the ability to apply those rules. Strategy as a whole is applying a set of rules to circumstances, that's how it is taught: "here are examples of circumstances and actions, try to pattern match to future circumstance and apply commensurate action." If you make the point that chess is a large part of the training data, or that LLMs are unable to learn chess well, I'll accept that as refuting that LLMs are AGI, but these other points I disagree with.
nmehner 18d ago
If you read all chess tutorials, strategy documentation and game archives on the internet and then would still suck at chess: yes.
- brindleth 18d ago
  Declarative knowledge is not the same as procedural knowledge. You can read as many chess tutorials, strategy documentation and game archives as you like, they won't make you good at chess until you actually start practicing chess.
  - _superposition_ 18d ago
    Does practical improvement apply to only humans or intelligence in general?
foldr 18d ago
The issue with the models isn't that they play a bad game, but that they persist in making illegal moves. An average intelligent human can be told the rules of chess and then play chess, badly, within the rules.
- empath75 17d ago
  An average human would have a physical chess board in front of them to remind them of the current state.
  - foldr 17d ago
    Sure, but the LLM is free to construct a representation of the chess board and update it as it goes along. It is not in any way banned from using a virtual board, or whatever representation of game state it pleases. AFAIK, current models will still sometimes make illegal moves even if given the entire game state (e.g. in FEN notation), so it is not purely an issue with the models’ ability to keep track of sequences of moves.