I've gotten enormous value out of LW and its derived communities during my life, at least some of which is attributable to the LW2.0 revival and its effects on those communities. More recently, since moving to the Bay, I've been very excited by a lot of the in-person events that Lighthaven has helped facilitate. Also, LessWrong is doing so many things right as a website and source-of-content that no one else does (karma-gated RSS feeds! separate upvote and agree-vote! built-in LaTeX support!) and even if I had no connection to the other parts of its mission I'd want to support the existence of excellently-done products. (Of course there's also the altruistic case for impact on how-well-the-future-goes, which I find compelling on its own merits.) Have donated $5k for now, but I might increase that when thinking more seriously about end-of-year donations.

(Conflict of interest notice: two of my housemates work at Lightcone Infrastructure and I would be personally sad and slightly logistically inconvenienced if they lost their jobs. I don't think this is a big contributor to my donation.)

The theoretical maximum FLOPS of an Earth-bound classical computer is something like $2^{35}$.

Is this supposed to have a different base or exponent? A single H100 already gets like $2^{45}$ FLOP/s.

So I would guess it should be possible to post-train an LLM to give answers like "................... Yes" instead of "Because 7! contains both 3 and 5 as factors, which multiply to 15 Yes", and the LLM would still be able to take advantage of CoT

This doesn't necessarily follow - on a standard transformer architecture, this will give you more parallel computation but no more serial computation than you had before. The bit where the LLM does N layers' worth of serial thinking to say "3" and then that "3" token can be fed back into the start of N more layers' worth of serial computation is not something that this strategy can replicate!

Empirically, if you look at figure 5 in Measuring Faithfulness in Chain-of-Thought Reasoning, adding filler tokens doesn't really seem to help models get these questions right:

I don't think that's true - in eg the GPT-3 architecture, and in all major open-weights transformer architectures afaik, the attention mechanism is able to feed lots of information from earlier tokens and "thoughts" of the model into later tokens' residual streams in a non-token-based way. It's totally possible for the models to do real introspection on their thoughts (with some caveats about eg computation that occurs in the last few layers), it's just unclear to me whether in practice they perform a lot of it in a way that gets faithfully communicated to the user.

Yeah, I'm thinking about this in terms of introspection on non-token-based "neuralese" thinking behind the outputs; I agree that if you conceptualize the LLM as being the entire process that outputs each user-visible token including potentially a lot of CoT-style reasoning that the model can see but the user can't, and think of "introspection" as "ability to reflect on the non-user-visible process generating user-visible tokens" then models can definitely attain that, but I didn't read the original post as referring to that sort of behavior.

In other words, they can think about the thoughts "behind" the previous words they wrote. If you doubt me on this, try asking one what its words are referring to, with reference to its previous words. Its "attention" modules are actually intentionally designed to know this sort of thing, using using key/query/value lookups that occur "behind the scenes" of the text you actually see on screen.

I don't think that asking an LLM what its words are referring to is a convincing demonstration that there's real introspection going on in there, as opposed to "plausible confabulation from the tokens written so far". I think it is plausible there's some real introspection going on, but I don't think this is a good test of it - the sort of thing I would find much more compelling is if the LLMs could reliably succeed at tasks like

Human: Please think of a secret word, and don't tell me what it is yet.
LLM: OK!
Human: What's the parity of the alphanumeric index of the penultimate letter in the word, where A=1, B=2, etc?
LLM: Odd.
Human: How many of the 26 letters in the alphabet occur multiple times in the word?
LLM: None of them.
Human: Does the word appear commonly in two-word phrases, and if so on which side?
LLM: It appears as the second word of a common two-word phrase, and as the first word of a different common two-word phrase.
Human: Does the word contain any other common words as substrings?
LLM: Yes; it contains two common words as substrings, and in fact is a concatenation of those two words.
Human: What sort of role in speech does the word occupy?
LLM: It's a noun.
Human: Does the word have any common anagrams?
LLM: Nope.
Human: How many letters long is the closest synonym to this word?
LLM: Three.
Human: OK, tell me the word.
LLM: It was CARPET.

but couldn't (even with some substantial effort at elicitation) infer hidden words from such clues without chain-of-thought when it wasn't the one to think of them. That would suggest to me that there's some pretty real reporting on a piece of hidden state not easily confabulated about after the fact.

I think my original comment was ambiguous - I also consider myself to have mostly figured it out, in that I thought through these considerations pretty extensively before joining and am in a "monitoring for new considerations or evidence or events that might affect my assessment" state rather than a "just now orienting to the question" state. I'd expect to be most useful to people in shoes similar to my past self (deciding whether to apply or accept an offer) but am pretty happy to talk to anyone, including eg people who are confident I'm wrong and want to convince me otherwise.

See my reply to Ryan - I'm primarily interested in offering advice on something like that question since I think it's where I have unusually helpful thoughts, I don't mean to imply that this is the only question that matters in making these sorts of decisions! Feel free to message me if you have pitches for other projects you think would be better for the world.

Yeah, I agree that you should care about more than just the sign bit. I tend to think the magnitude of effects of such work is large enough that "positive sign" often is enough information to decide that it dominates many alternatives, though certainly not all of them. (I also have some kind of virtue-ethical sensitivity to the zero point of the impacts of my direct work, even if second-order effects like skill building or intra-lab influence might make things look robustly good from a consequentialist POV.)

The offer of the parent comment is more narrowly scoped, because I don't think I'm especially well suited to evaluate someone else's comparative advantages but do have helpful things to say on the tradeoffs of that particular career choice. Definitely don't mean to suggest that people (including myself) should take on capability-focused roles iff they're net good!

I did think a fair bit about comparative advantage and the space of alternatives when deciding to accept my offer; I've put much less work into exploration since then, arguably too much less (eg I suspect I don't quite meet Raemon's bar). Generally happy to get randomly pitched on things, I suppose! 

I work on a capabilities team at Anthropic, and in the course of deciding to take this job I've spent^[1] a while thinking about whether that's good for the world and which kinds of observations could update me up or down about it. This is an open offer to chat with anyone else trying to figure out questions of working on capability-advancing work at a frontier lab! I can be reached at "graham's number is big" sans spaces at gmail.

1. ^{^}

   and still spend - I'd like to have Joseph Rotblat's virtue of noticing when one's former reasoning for working on a project changes.