Alternative frame: I’ve been poking at the idea of quantum resource theories periodically, literally on the strength of a certain word-similarity between quantum stuff and alignment stuff.
The root inspiration for this comes from Scott Aaronson’s Quantum Computing Since Democritus, specifically two things: one, the “certain generalization of probability” lens pretty directly liberates me to throw QM ideas at just about anything, the same way I might with regular probability; two, the introduction of negative probability and through that “cancelling out” possibilities is super cool and feels like a useful way to think about certain problems.
So, babbling: can we loot resource theories from quantum thermodynamics as a way to reason more precisely about the constraints we want for alignment?
“A resource theory is a simple model for any situation in which the actions you can perform and the systems you can access are restricted for some reason,” said the physicist Nicole Yunger Halpern of the National Institutes of Standards and Technology.
This sounds like a good match for alignment-ish problems on the face of it. In the alignment case the some reason for the restrictions is so it doesn’t kill us. There are two elements to the resource theory: firstly a set of free operations and states we assume can be gotten to at no cost; secondly valuable resources like entanglement, purity, and asymmetry which are states which can be achieved at a cost (and therefore are limited). The gist is, what if we swapped out words like entanglement and purity with words like corrigibility and interpretability?
quantum probability is a very specific thing; I agree that it’s an incredibly interesting metaphor, and I also think there’s something to be had there, but I’d caution against applying it too literally without care. the kinds of interference patterns at quantum scale are in fact qualitatively different from the ones at larger spatial scales under most conditions.
neural networks are not usually complex valued, for starters. and not because it hasn’t been tried.
anything processing complex valued phenomena or modeling reality in high enough resolution that the network should learn small-scale complex valued patterns; so, chemistry, fluid waves eg sound, electricity, etc. some very solid results: https://arxivxplorer.com/?query=complex+valued+neural+networks
Alternative frame: I’ve been poking at the idea of quantum resource theories periodically, literally on the strength of a certain word-similarity between quantum stuff and alignment stuff.
The root inspiration for this comes from Scott Aaronson’s Quantum Computing Since Democritus, specifically two things: one, the “certain generalization of probability” lens pretty directly liberates me to throw QM ideas at just about anything, the same way I might with regular probability; two, the introduction of negative probability and through that “cancelling out” possibilities is super cool and feels like a useful way to think about certain problems.
So, babbling: can we loot resource theories from quantum thermodynamics as a way to reason more precisely about the constraints we want for alignment?
A Quanta article animating the thought: https://www.quantamagazine.org/physicists-trace-the-rise-in-entropy-to-quantum-information-20220526/
Direct quote -
This sounds like a good match for alignment-ish problems on the face of it. In the alignment case the some reason for the restrictions is so it doesn’t kill us. There are two elements to the resource theory: firstly a set of free operations and states we assume can be gotten to at no cost; secondly valuable resources like entanglement, purity, and asymmetry which are states which can be achieved at a cost (and therefore are limited). The gist is, what if we swapped out words like entanglement and purity with words like corrigibility and interpretability?
quantum probability is a very specific thing; I agree that it’s an incredibly interesting metaphor, and I also think there’s something to be had there, but I’d caution against applying it too literally without care. the kinds of interference patterns at quantum scale are in fact qualitatively different from the ones at larger spatial scales under most conditions.
neural networks are not usually complex valued, for starters. and not because it hasn’t been tried.
Which areas of neural network would fit under the complex number paradigm?
anything processing complex valued phenomena or modeling reality in high enough resolution that the network should learn small-scale complex valued patterns; so, chemistry, fluid waves eg sound, electricity, etc. some very solid results: https://arxivxplorer.com/?query=complex+valued+neural+networks