I believe that exploration of this sort is extremely relevant for AI alignment. It seems rather plausible that neural networks, or anything much like them, will be made use of being a ingredient of AGI. If that is the circumstance, then we want in order to reliably forecast and rationale about how neural networks behave in new scenarios, And just how they communicate with other methods, and it is difficult to imagine how that would be probable without having a deep knowledge of the dynamics at play when neural networks understand from info.
The paper talks a bunch about such things as SGD getting (Just about) Bayesian along with the neural network prior possessing low Kolmogorov complexity; I discovered these for being distractions from the main place.
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The authors take into consideration (a quantity akin to) the fraction of inputs in S for which a given function outputs one.
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But, I do agree that the Levin certain argument doesn’t truly seize the “system†of what’s occurring right here. I can of course only discuss for myself (and not the Others involved with this work), but I visualize the Levin sure argument as essentially a more formal solution to point out this instinct. I.e., It's really a free argument for why we needn't be amazed that easy functions have larger sized steps in parameter-House, regardless of whether the argument doesn’t establish the many exact specifics of how this is effective in almost any unique case.
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(That is an exaggeration but I do think it's directionally correct. Undoubtedly Once i browse the title "neural networks are essentially Bayesian" I was thinking of anything quite distinctive.)
So, I agree there are specified features with very low Kolmogorov complexity that a neural network normally cannot "see" thoroughly. I also Consider a single could frame a great 파워볼 deal of the research on acquiring new neural community architectures as becoming about building neural networks capable to "see" much more styles of features.
 But the example displays which the declare about complexity actions will only keep if our translation is "adequate" in certain sense.  If we don't have any plan what "good enough" signifies, something is missing within the story.)
Consequently, we should always be expecting functions with substantial volumes in parameter-House being basic, and capabilities with little volumes to get elaborate.