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> The verifier taught AlphaGo that move Ok so it sounds like you want to give the rules of Go credit for that move, lol.
by trick-or-treat 7mo ago
> The verifier taught AlphaGo that move
Ok so it sounds like you want to give the rules of Go credit for that move, lol.
- deleted 7mo ago[deleted]
- wobfan 7mo agoIt feels like you're purposefully ignoring the logical points OP gives and you just really really want to anthropomorphize AlphaGo and make us appreciate how smart it (should I say he/she?) is ... while no one is even criticising the model's capabilities, but analyzing it.
- famouswaffles 7mo ago[dead]
- trick-or-treat 7mo agoCan you back that up with some logic for me? I don't really play Go but I play chess, and it seems to me that most of what humans consider creativity in GM level play comes not in prep (studying opening lines/training) but in novel lines in real games (at inference time?). But that creativity absolutely comes from recalling patterns, which is exactly what OP criticizes as not creative(?!) I guess I'm just having trouble finding a way to move the goalpost away from artificial creativity that doesn't also move it away from human creativity?
- datsci_est_2015 7mo agoHow a model is trained is different than how a model is constructed. A model’s construction defines its fundamental limitations, e.g. a linear regressor will never be able to provide meaningful inference on exponential data. Depending on how you train it, though, you can get such a model to provide acceptable results in some scenarios. Mixing the two (training and construction) is rhetorically convenient (anthropomorphization), but holds us back in critically assessing a model’s capabilities.
- hackinthebochs 7mo agoLinear regression has well characterized mathematical properties. But we don't know the computational limits of stacked transformers. And so declaring what LLMs can't do is wildly premature.
- datsci_est_2015 7mo ago> And so declaring what LLMs can't do is wildly premature. The opposite is true as well. Emergent complexity isn’t limitless. Just like early physicists tried to explain the emergent complexity of the universe through experimentation and theory, so should we try to explain the emergent complexity of LLMs through experimentation and theory. Specifically not pseudoscience, though.
- hackinthebochs 7mo agoSure, that's true as well. But I don't see this as a substantive response given that the only people making unsupported claims in this thread are those trying to deflate LLM capabilities.
- datsci_est_2015 7mo agoSo, to review this thread - OP asked for someone to make a logical argument for the separation of “training” from “model” - I made the argument - You cherry picked an argument against my specific example and made an appeal to emergent complexity - I pointed out that emergent complexity isn’t limitless - “the only people making unsupported claims in this thread are those trying to deflate LLM capabilities”
- famouswaffles 7mo agoYou made a pretty nonsensical argument, pretty much seems like the big standard for these arguments. What does linear regression have to do with the limitations of a stacked transfer ? Absolutely nothing. This is the problem here. You don't know shit and just make up whatever. You can see people doing the same thing in GPT-1, 2, 3, 4 threads all telling us why LLMs will never be able to do thing it manages to do later.