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Post #2790158

2023-04-07 16:49 UTC

@Moon@shitposter.club @PCOWandre@jauntygoat.net @thatguyoverthere@shitposter.club @DrewKadel@social.coop there really isn't a hard distinction between "reasoning" and "string language tokens together using a mathematical model that people find pleasing", though "reasoning" of the kind happens in solving a maths problem is a matter of shuffling higher/lossier/more-general abstractions. it's easier to play ma-jan or chess or whatever when you have hard, discrete gamepieces to move around a low-res board, rather than sliding around little piles of goop in a puddle. and following only certain game rules and not drawing on other things you happen to have read ("this sounds math-y") requires sharply-defined partitioning as well because our resources are so limited, humans often abstract prematurely, leading to the platonist error of projecting neat little models in our heads onto a complex world that doesn't well fit them. but these models have the opposite problem, so that even with enormous databases and computing resources they're too inefficient at model abstraction (i.e. compression) to model the world effectively (point again this conversation of woman specialises in producing simplified models with similar performance at greater efficiency https://traffic.libsyn.com/secure/computingup/computingup-ep265-11.mp3

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  • @Moon@shitposter.club @DrewKadel@social.coop @PCOWandre@jauntygoat.net @thatguyoverthere@shitposter.club conversation she talks about putting humans into the loop to help with the current stumbling block, defining where and how to abstract ("when you're looking for a tumour, pay attention to *this* in the scan and not *that*). similarly, when a model has the pieces and rules of chess predefined for it it then can out-perform human grandmasters, showing this really is the current limiting factor humans have got a lot of these abstractions predefined for us as well in our embryonic-developed architecture (e.g. facial recognition hardware and whatever in practice constitutes chompy's "language acquisition device"), but the algorithmic approach we use is clearly also much more aggressive at abstracting on the fly, and we'll need to adjust the training of models to do the same

    Open ##2790162