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by Vadim Kosoy 353 days ago | David Krueger and Jessica Taylor like this | link | parent

I feel that there is a false dichotomy going on here. In order to say we “solved” AGI alignment, we must have some mathematical theory that defines “AGI”, defines “aligned AGI” and a proof that some specific algorithm is an “aligned AGI.” So, it’s not that important whether the algorithm is “messy” or “principled” (actually I don’t think it’s a meaningful distinction), it’s important what we can prove about the algorithm. We might relax the requirement of strict “proof” and be satisfied with a well-defined conjecture that has lots of backing evidence (like \(P \ne NP\)) but it seems to me that we wouldn’t want to give up on at least having a well-defined conjecture (unless under conditions of extreme despair, which is not what I would call a successful solution to AGI alignment).

So, we can still argue about which subproblems have the best chance of leading us to such a mathematical theory, but it feels like there is a fuzzy boundary there rather than a sharp division into two or more irreconcilable approaches.



by Jessica Taylor 353 days ago | link

I agree that the original question (messy vs principled) seems like a false dichotomy at this point. It’s not obvious where the actual disagreement is.

My current guess is that the main disagreement is something like: on the path to victory, did we take generic AGI algorithms (e.g. deep learning technology + algorithms layered on top of it) and figure out how to make aligned versions of them, or did we make our own algorithms? Either way we end up with an argument for why the thing we have at the end is aligned. (This is just my current guess, though, and I’m not sure if it’s the most important disagreement.)

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