I feel that analysis of this type is incredibly applicable for AI alignment. It appears fairly plausible that neural networks, or something much like them, will probably be made use of being a part of AGI. If that is the case, then we want to have the ability to reliably predict and reason regarding how neural networks behave in new situations, And the way they communicate with other devices, and it is difficult to assume how that may be feasible and not using a deep idea of the dynamics at play when neural networks learn from facts.
The paper talks a bunch about things such as SGD becoming (almost) Bayesian as well as neural network prior getting low Kolmogorov complexity; I found these being distractions from the key point.
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We’re not expressing that discrete complexity actions entirely seize what we treatment about for NNs! We do nevertheless think that they are sufficiently pertinent being useful for The larger picture, regardless of whether just as a proxy for what we in fact care about.
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Most complexity actions give about equivalent values for that (relative) complexity of most objects, so our assumption is usually that if anything is the situation for a bunch of various tractable complexity steps, then this is also more likely to be the situation for Regardless of the “great†complexity evaluate could be while in the related scenario.
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So, If your Bayesian posterior completely accounts for generalization error and SGD is not contributing any further more inductive bias in the least, we would expect to check out generalization error around the get 파워볼사ì´íŠ¸ of five% from SGD.
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The items Joar is declaring there typically match my intuitions, Though there are some things unsaid where by my intuitions could diverge additional.
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