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by achrono
661 days ago
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>All one will be able to say is that somewhere out there in the computational universe there’s some (typically computationally irreducible) process that “happens” to be aligned with what we want. >There’s no overarching theory to it in itself; it’s just a reflection of the resources that were out there. Or, in the case of machine learning, one can expect that what one sees will be to a large extent a reflection of the raw characteristics of computational irreducibility Strikes me as a very reductive and defeatist take that flies in the face of the grand agenda Wolfram sets forth. It would have been much more productive to chisel away at it to figure out something rather than expecting the Theory to be unveiled in full at once. For instance, what I learn from the kinds of playing around that Wolfram does in the article is: neural nets are but one way to achieve learning & intellectual performance, and even within that there are a myriad different ways to do it, but most importantly: there is a breadth vs depth trade-off, in that neural nets being very broad/versatile are not quite the best at going deep/specialised; you need a different solution for that (e.g. even good old instruction set architecture might be the right thing in many cases). This is essentially why ChatGPT ended up needing Python tooling to reliably calculate 2+2. |
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This is untrue. ChatGPT very reliably calculates 2+2 without invoking any tooling.