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I'm curious to really know what you suggest by "concentrate on the architecture rather." My guess is that if the OP is right, just about any neural Internet architecture should have a simplicity bias.So from the enter/output standpoint, if features are coded inside a line, the right function is one level. In that sense you'll find without a do

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I think that research of this kind is very suitable for AI alignment. It seems rather plausible that neural networks, or one thing comparable to them, are going to be employed as being a ingredient of AGI. If that is the situation, then we wish to be able to reliably predict and rationale regarding how neural networks behave in new situations, and

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(Also: I don’t Believe the notion of “very low successful parameterisation” definitely captures what’s taking place below, but for The main reason you indicate yourself.)The rest of the comment is about things which i didn't have a powerful view on, so I am going to leave it for Zach to reply if he needs.--The stuff linked With this put up

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Hopeful millionaires who tuned in at 11 p.m. to begin to see the Stay drawing of your Powerball numbers alternatively heard which the drawing was currently being delayed as a result of technical troubles.The rest of the comment is about stuff which i didn't have a solid opinion on, so I will leave it for Zach to reply if he wishes.I'm undecided i

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(Also: I don’t Assume the Idea of “small effective parameterisation” truly captures what’s happening listed here, but for The main reason you point out oneself.)With this context, smoothness is just one these types of pertinent measure: clean features have small Kolmogorov complexity, but you will find other ways to acquire low Kolmogorov c

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