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Geoffrey Irving (Research Director, AI Safety Institute)
Given the tweet thread Geoffrey wrote during the board drama, it seems pretty clear that he's willing to publicly disparage OpenAI. (I used to work with Geoffrey, but have no private info here)
1Oh, that's great! Was that recently changed? I swear I looked shortly after release and it just showed me a job ad when I clicked on a feature...
Thanks! Note that this work uses steering vectors, not SAEs, so the technique is actually really easy and cheap - I actively think this is one of the main selling points (you can jailbreak a 70B model in minutes, without any finetuning or optimisation). I am excited at the idea of seeing if you can improve it with SAEs though - it's not obvious to me that SAEs are better than steering vectors, though it's plausible.
I may take you up on the two hours offer, thanks! I'll ask my co-authors
But it mostly seems like it would be helpful because it gives you well-tuned baselines to compare your results to. I don't think you have results that can cleanly be compared to well-established baselines?
If we compared our jailbreak technique to other jailbreaks on an existing benchmark like Harm Bench and it does comparably well to SOTA techniques, or does even better than SOTA techniques, would you consider this success at doing something useful on a real task?
+1, I think the correct conclusion is "a16z are making bald faced lies to major governments" not "a16z were misled by Anthropic hype"
I only ever notice it on my own posts when I get a notification about it
I see this is strongly disagree voted - I don't mind, but I'd be curious for people to reply with which parts they disagree with! (Or at least disagree react to specific lines). I make a lot of claims in that comment, though I personally think they're all pretty reasonable. The one about not wanting inexperienced researchers to start orgs, or "alignment teams at scaling labs are good actually" might be spiciest?
Might be mainly driven by an improved tokenizer.
I would be shocked if this is the main driver, they claim that English only has 1.1x fewer tokens, but seem to claim much bigger speed-ups
(EDIT: I just saw Ryan posted a comment a few minutes before mine, I agree substantially with it)
As a Google DeepMind employee I'm obviously pretty biased, but this seems pretty reasonable to me, assuming it's about alignment/similar teams at those labs? (If it's about capabilities teams, I agree that's bad!)
I think the alignment teams generally do good and useful work, especially those in a position to publish on it. And it seems extremely important that whoever makes AGI has a world-class alignment team! And some kinds of alignment research can only really be done with direct access to frontier models. MATS scholars tend to be pretty early in their alignment research career, and I also expect frontier lab alignment teams are a better place to learn technical skills especially engineering, and generally have a higher talent density there.
UK AISI/US AISI/METR seem like solid options for evals, but basically just work on evals, and Ryan says down thread that only 18% of scholars work on evals/demos. And I think it's valuable both for frontier labs to have good evals teams and for there to be good external evaluators (especially in government), I can see good arguments favouring either option.
44% of scholars did interpretability, where in my opinion the Anthropic team is clearly a fantastic option, and I like to think DeepMind is also a decent option, as is OpenAI. Apollo and various academic labs are the main other places you can do mech interp. So those career preferences seem pretty reasonable to me there for interp scholars.
17% are on oversight/control, and for oversight I think you generally want a lot of compute and access to frontier models? I am less sure for control, and think Redwood is doing good work there, but as far as I'm aware they're not hiring.
This is all assuming that scholars want to keep working in the same field they did MATS for, which in my experience is often but not always true.
I'm personally quite skeptical of inexperienced researchers trying to start new orgs - starting a new org and having it succeed is really, really hard, and much easier with more experience! So people preferring to get jobs seems great by my lights
Fair point