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Notes -
Neural Networks Are Already Clean
Nick Cammarata (Goodfire AI; formerly of OpenAI) claims to have discovered that earlier foundational research on the semantic structure of neural networks (especially "superposition") were wrong, and they actually (and necessarily!) work pretty much the naive way everybody used to expect them to: particular neurons mean particular, ultimately interpretable things.
My (admittedly jumpy and currently highly-sperged) gut instinct is that this may be one of the most significant findings in history or at least since AIAYN.
Very interesting, if true. And an example of recursive improvement of AI along a totally different axis, since the interpretability pipeline uses agent driven investigations.
If it disproves the ‘AI is a shoggoth’ theory it will be massively consequential.
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Big if true. I've only skimmed the first section so far, but my heuristics for skepticism go in this order:
I'm probably cranky and judgy, but with the amount of slop research that has come out in the past couple years, incredibly sensationalist claims like "polysemanticity was just an mistake" from a researcher who's bio is "I like doing LSD and writing philosophy blogs inbetween startup gigs" leaves me very cynical. But let me dig through it and update when i get done.
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This is a cheap shot, but I feel like if 'AIAYN' were one of the most significant findings in history, I would not have had to google it to understand what you're talking about.
The most significant finding since 2017, maybe? I guess? But there's a pretty significant gap between 'all of history' and 'the last nine years'.
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