Rana Dexsin

You see either something special, or nothing special.

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That's what I did, though with do on 1 and mi′ on 0 (treating it as 10). I'm not sure what similarity metric is most relevant here, but in case of near-collisions, small additions of supporting rhythm or harmony could help nudge some sequences apart; swing time would give added contrast to even vs odd positions, for instance. Anyway, it sounds like this might not generalize well across people…

Aside: this is a time when I'm really appreciating two-axis voting. The vote-signal of “that's mildly interesting but really doesn't seem like it'd work” is very useful to see in a compact form, even though the written responses contain similar information in more detail.

I'm actually writing from experience there! That's how my memory still mainly encodes the few main home telephone numbers from when I was growing up (that is, I remember the imputed melodies before the digits in any other form), but it's possible they were unusually harmonious. I don't think it was suggested by family, just something I did spontaneously, and I am not at all sure how well this would carry more generally… it may also be relevant that I had a bunch of exposure to Chinese numeric musical notation.

Assigning digits to scale degrees feels like an interesting alternative here, especially for the musically-inclined.

I did in fact go back and listen to that part, but I interpreted that clarifying expansion as referring to the latter part of your quoted segment only, and the former part of your quoted segment to be separate—using cryptocurrency as a bridging topic to get to cryptography afterwards. Anyway, your interpretation is entirely reasonable as well, and you probably have a much better Eliezer-predictor than I do; it just seemed oddly unconservative to interpolate that much into a transcript proper as part of what was otherwise described as an error correction pass.

We have people in crypto[graphy] who are good at breaking things, and they're the reason why anything is not on fire. Some of them might go into breaking AI systems instead, because that's where you learn anything.

Was there out-of-band clarification that Eliezer meant “cryptography” here (at 01:28:41)? He verbalized “crypto”, and I interpreted it as “cryptocurrency” myself, partly to tie things in with both the overall context of the podcast and the hosts' earlier preemptively-retracted question which was more clearly about cryptocurrency. Certainly I would guess that the first statement there is informally true either way, and there's a lot of overlap. (I don't interpret the “cryptosystem” reference a few sentences later to bias it much, to be clear, due to that overlap.)

If you weren't already aware, look up “circuit bending” if you want to see a grungier DIY/hacker-style subcultural activity around producing interesting sounds via ad-hoc manipulations of electronics.

Tiberium at HN seems to think not. Copied and lightly reformatted, with the 4chan URLs linkified:

It seems that the leak originated from 4chan [1]. Two people in the same thread had access to the weights and verified that their hashes match [2][3] to make sure that the model isn't watermarked. However, the leaker made a mistake of adding the original download script which had his unique download URL to the torrent [4], so Meta can easily find them if they want to.

I haven't looked at the linked content myself yet.

Long before we get to the “LLMs are showing a number of abilities that we don't really understand the origins of” part (which I think is the most likely here), a number of basic patterns in chess show up in the transcript semi-directly depending on the tokenization. The full set of available board coordinates is also countable and on the small side. Enough games and it would be possible to observe that “. N?3” and “. N?5” can come in sequence but the second one has some prerequisites (I'm using the dot here to point out that there's adjacent text cues showing which moves are from which side), that if there's a “0-0” there isn't going to be a second one in the same position later, that the pawn moves “. ?2” and “. ?1” never show up… and so on. You could get a lot of the way toward inferring piece positions by recognizing the alternating move structure and then just taking the last seen coordinates for a piece type, and a layer of approximate-rule-based discrimination would get you a lot further than that.

I wonder whether, when approving applications for the full models for research, they watermark the provided data somehow to be able to detect leaks. Would that be doable by using the low-order bits of the weights or something, for instance?

I mean in the paragraph “If anyone wants to check it out, the app can be found at: https://cyborg-psychologiy.com”. I suppose I should have said “the main pseudo-link”. 🙂 What you gave in this last comment points to the blog post, not (directly) to the app.

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