@ereliuer_eteer You may actually want to adapt your random distribution deliberately to exclude numbers lying in high probability mass range of numbers typically lazily chosen by humans. While still maintaining high entropy ofc.
@ereliuer_eteer And lazily chosen by human numbers are much lower entropy than the random source. So strategy of attacker is smth like attack on belief that we are in 1% because then significantly easier to crack.
And now as a human who is genuinely pseudorandomly generating.
@taoroalin Idk in a lot of settings where hacking is easier than solving the task I don't know why it wouldn't just learn policy of hacking every time. So wouldn't improve on just stop training once you learn to hack.
@_jake_ward Am I right you'd expect the angel here to learn via RL as usual, gradually learning slower as the model learns to hack more, before freezing learning when at a policy of hacking every time.
@letonyo@billionsmustliv Not super clear. Based on how unreadable some proofs used to be, it's clear that there's no inherent reason for improvements in maths ability to lead to better writing.
You're betting on the writing skills of model making a leap forwards.
@banburismus_ I think how Nick himself presents his essay is a big contributor to this reaction.
If you say that this approach makes you confident we are gonna solve alignment in time, then you best have a pretty solid case!
@EkdeepL@HistedLab@nickcammarata Nick's work isn't really about Monosemanticity, is it? It's more about constructing a plausible developmental story as an explanation for neurons that are for sure not monosemantic.
@juli_li_@NeelNanda5@nickcammarata The way I view this stuff is that in memorization you have many neurons which are each weak classifiers for their value lookup. And you have a mixture of these weak clasifiers. So ofc we'd expect to be able to ablate quite a few of them before seeing a drop in accuracy.
@juli_li_@NeelNanda5@nickcammarata Therefore in the ablation there is a substantial portion of neurons with weakish activations remaining. What it shows is that lookup type results distributed across many neurons are resistant to substantial amounts of ablation, but that'd be expected of distributed activations.
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