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This doesn't really engage with my points. Yes of course programming a narrow chess engine doesn't extrapolate to domains outside of games. But my point is once

This doesn't really engage with my points. Yes of course programming a narrow chess engine doesn't extrapolate to domains outside of games. But my point is once a system can induce a self-play dynamic in itself simply from being prompted with the rules of chess, then this represents a significant qualitative change. I only chose chess for the irony of it wrongly being represented as AGI-complete in the past. None of your points are relevant to this claim. The issue of how the human mind works is tangential, but its also not true that computer chess has no relation to how the human mind works. Neural net based evaluations result in moves that more closely match human expert choices[1], which suggests a similarity in mechanism compared to prior evaluation mechanisms. >The problem with statistical machine learning algorithms is that they have to be trained to do one thing, and one thing only. They're one-trick ponies. LLMs show the beginnings of breaking out of this limitation. People who argue LLMs are just another example in the history of narrow AI don't seem to have a strong response to the example of in-context learning. The straightforward understanding is that we're seeing the beginnings of true generalization. I have not seen a convincing argument that in-context learning is just another case of what we've seen before. [1]https://www.cs.toronto.edu/~ashton/pubs/maia-kdd2020.pdf https://www.cs.toronto.edu/~ashton/pubs/maia-kdd2020.pdf

YeGoblynQueenne 4y ago
>> This doesn't really engage with my points. Sorry. I thought you described a system that did what MuZero did. It still seems to me that's what you're describing. "In-context learning" is a big potato. It's just some jargon that OpenAI came up with to play up the capabilities of GPT-3, just another example of the time-honoured tradition in AI of inventing grandiloquent terms to describe something much simpler. See Drew McDermot's "Artificial Intelligence meets Natural Stupidity", a paper from 1976, that castigates this already very common habit of AI researchers: https://dl.acm.org/doi/10.1145/1045339.1045340 https://dl.acm.org/doi/10.1145/1045339.1045340 To clarify, "in-context learning" is what we used to call "sampling from a model" back in my time (in 2014, when I did my Master's and studied neural nets and language modelling, and also game-playing algos; not coincidentally, because I usually engage in conversation about things that I understand, so you won't catch me talking about quantum computers, say, or physics). Models got better and sampling from them gets better results, so OpenAI figured they could embiggen it and call it something more impressive than "sampling". But it's sampling, not learning. >> People who argue LLMs are just another example in the history of narrow AI don't seem to have a strong response to the example of in-context learning. I don't think it's very productive to try and fit opinions into "people who say" kinds of boxes. I don't treat my interlocutors like that. Perhaps I haven't yet heard all the arguments on any particular claim. I'll never know if I assume everyone is just repeating the same old stuff. Anyway there's no point in arguing anything here. If you've used language models before, you know how sampling works and it's obvious it's what OpenAI call "in-context learning". They're not even pretending it's not sampling. They're just claiming it's different because they're sampling from a large Language model. Edit: >> Neural net based evaluations result in moves that more closely match human expert choices[1], which suggests a similarity in mechanism compared to prior evaluation mechanisms. The paper you linked describes a system that was explicitly trained to predict human moves. That's probably why it can do better at simulating the way humans play chess than a system that wasn't trained to simulate the way humans play chess.
- hackinthebochs 4y ago
  > I thought you described a system that did what MuZero did. It still seems to me that's what you're describing. I can see your reading of it. My point wasn't totally clear. What I was getting at is a system that automatically induces internal computational graphs based on its competence with natural language and its facility with solving planning problems. It's essentially a more general MuZero that is driven by natural language and can solve general computational problems over its entire learned world model. For example, when asking what is the 256th fibonacci number, instead of either retrieving from memory or confabulating a correct looking number, it constructs the right sequence of computational steps from its representation of the fibonacci sequence and runs through it. The difference is largely one of having the components of the system be self-directed through internal planning mechanisms, i.e. a cybernetic governor that can plan out general computational strategies. Natural language is a general representational framework and so a planning and computing facility over natural language representations is in my view a general problem solving framework. >To clarify, "in-context learning" is what we used to call "sampling from a model" back in my time [...] Models got better and sampling from them gets better results, so OpenAI figured they could embiggen it and call it something more impressive than "sampling". But it's sampling, not learning. The way I interpret this (analogous to sampling from a probability distribution), there is certainly a relationship between the two. But the difference is that in-context learning is much more general and flexible than anything that has come before. When the range of behavior far surpasses what has come before, its important to recognize the paradigm shift. There's a danger of expertise in old paradigms causing resistance to seeing these qualitative shifts. As Max Planck said, science progresses one funeral at a time. I can't help but see a case of this resistance in those who are downplaying the qualitative shift that in-context learning represents. >The paper you linked describes a system that was explicitly trained to predict human moves. That's probably why it can do better at simulating the way humans play chess than a system that wasn't trained to simulate the way humans play chess. The evaluation in the paper wasn't just for the network tuned to match human performance, it was also tested against Stockfish and Leela. Leela more closely matched human move choices at all human skill levels. It was also widely commented at the time that AlphaZero played a more human style of chess. The point is that this says something about how humans play chess, i.e. heavier on positional evaluation and light on calculation.

YeGoblynQueenne 4y ago

>> This doesn't really engage with my points. Sorry. I thought you described a system that did what MuZero did. It still seems to me that's what you're describing. "In-context learning" is a big potato. It's just some jargon that OpenAI came up with to play up the capabilities of GPT-3, just another example of the time-honoured tradition in AI of inventing grandiloquent terms to describe something much simpler. See Drew McDermot's "Artificial Intelligence meets Natural Stupidity", a paper from 1976, that castigates this already very common habit of AI researchers: https://dl.acm.org/doi/10.1145/1045339.1045340 https://dl.acm.org/doi/10.1145/1045339.1045340 To clarify, "in-context learning" is what we used to call "sampling from a model" back in my time (in 2014, when I did my Master's and studied neural nets and language modelling, and also game-playing algos; not coincidentally, because I usually engage in conversation about things that I understand, so you won't catch me talking about quantum computers, say, or physics). Models got better and sampling from them gets better results, so OpenAI figured they could embiggen it and call it something more impressive than "sampling". But it's sampling, not learning. >> People who argue LLMs are just another example in the history of narrow AI don't seem to have a strong response to the example of in-context learning. I don't think it's very productive to try and fit opinions into "people who say" kinds of boxes. I don't treat my interlocutors like that. Perhaps I haven't yet heard all the arguments on any particular claim. I'll never know if I assume everyone is just repeating the same old stuff. Anyway there's no point in arguing anything here. If you've used language models before, you know how sampling works and it's obvious it's what OpenAI call "in-context learning". They're not even pretending it's not sampling. They're just claiming it's different because they're sampling from a large Language model. Edit: >> Neural net based evaluations result in moves that more closely match human expert choices[1], which suggests a similarity in mechanism compared to prior evaluation mechanisms. The paper you linked describes a system that was explicitly trained to predict human moves. That's probably why it can do better at simulating the way humans play chess than a system that wasn't trained to simulate the way humans play chess.

hackinthebochs 4y ago
> I thought you described a system that did what MuZero did. It still seems to me that's what you're describing. I can see your reading of it. My point wasn't totally clear. What I was getting at is a system that automatically induces internal computational graphs based on its competence with natural language and its facility with solving planning problems. It's essentially a more general MuZero that is driven by natural language and can solve general computational problems over its entire learned world model. For example, when asking what is the 256th fibonacci number, instead of either retrieving from memory or confabulating a correct looking number, it constructs the right sequence of computational steps from its representation of the fibonacci sequence and runs through it. The difference is largely one of having the components of the system be self-directed through internal planning mechanisms, i.e. a cybernetic governor that can plan out general computational strategies. Natural language is a general representational framework and so a planning and computing facility over natural language representations is in my view a general problem solving framework. >To clarify, "in-context learning" is what we used to call "sampling from a model" back in my time [...] Models got better and sampling from them gets better results, so OpenAI figured they could embiggen it and call it something more impressive than "sampling". But it's sampling, not learning. The way I interpret this (analogous to sampling from a probability distribution), there is certainly a relationship between the two. But the difference is that in-context learning is much more general and flexible than anything that has come before. When the range of behavior far surpasses what has come before, its important to recognize the paradigm shift. There's a danger of expertise in old paradigms causing resistance to seeing these qualitative shifts. As Max Planck said, science progresses one funeral at a time. I can't help but see a case of this resistance in those who are downplaying the qualitative shift that in-context learning represents. >The paper you linked describes a system that was explicitly trained to predict human moves. That's probably why it can do better at simulating the way humans play chess than a system that wasn't trained to simulate the way humans play chess. The evaluation in the paper wasn't just for the network tuned to match human performance, it was also tested against Stockfish and Leela. Leela more closely matched human move choices at all human skill levels. It was also widely commented at the time that AlphaZero played a more human style of chess. The point is that this says something about how humans play chess, i.e. heavier on positional evaluation and light on calculation.