I think the reason nobody will do anything useful-to-John as a result of the control critique post is that control is explicitly not aiming at the hard parts of the problem, and knows this about itself. In that way, control is an especially poorly selected target if the goal is getting people to do anything useful-to-John. I'd be interested in a similar post on the Alignment Faking paper (or model organisms more broadly), on RAT, on debate, on faithful CoT, on specific interpretability paradigms (circuits v SAEs, vs some coherentist approach vs shards vs....), and would expect those to have higher odds of someone doing something useful-to-John. But useful-to-John isn't really the metric I think the field should be using, either....
I'm kind of picking on you here because you are least guilty of this failing relative to researchers in your reference class. You are actually saying anything at all, sometimes with detail, about how you feel about particular things. However, you wouldn't be my first-pick judge for what's useful; I'd rather live in a world where like half a dozen people in your reference class are spending non-zero time arguing about the details of the above agendas and how they interface with your broader models, so that the researchers working on those things can update based on those critiques (there may even be ways for people to apply the vector implied by y'all's collective input, and generate something new / abandon their doomed plans).
there are plenty of cases where we can look at what people are doing and see pretty clearly that it is not progress toward the hard problem
There are plenty of cases where John can glance at what people are doing and see pretty clearly that it is not progress toward the hard problem.
Importantly, people with the agent foundations class of anxieties (which I embrace; I think John is worried about the right things!) do not spend time engaging on a gears level with prominent prosaic paradigms and connecting the high level objection ("it ignores the hard part of the problem") with the details of the research.
"But Tsvi and John actually spend a lot of time doing this."
No, they don't! They paraphrase the core concern over and over again, often seemingly without reading the paper. I don't think reading the paper would change your minds (nor should it!), but I think that there's a culture problem tied to this off-hand dismissal of prosaic work that disincentivizes potential agent foundations (or similar new thing that shares the core concerns of agent foundations) researchers from engaging with, i.e., John.
Prosaic work is fraught and, much of it, doomed. New researchers over-index on tractability because short feedback loops are comforting ('street-lighting'). Why aren't we explaining why that is, on the terms of the research itself, rather than expecting people to be persuaded by the same high level point getting hammered into them again and again?
I've watched this work in real-time. If you listen to someone talk about their work, or read their paper and follow up in person, they are often receptive to a conversation about worlds in which their work is ineffective, evidence that we're likely to be in such a world, and even to shifting the direction of their work in recognition of that evidence.
Instead, people with their eye on the ball are doing this tribalistic(-seeming) thing.
Yup, the deck is stacked against humanity solving the hard problems; for some reason, folks who know that are also committed to playing their hands poorly, and then blaming (only) the stacked deck!
John's recent post on control is a counter-example to the above claims and was, broadly, a big step in the right direction, but had some issues with it, as raised by Redwood in the comments, which are a natural consequence of it being ~a new thing John was doing. I look forward to more posts like that in the future, from John and others, that help new entrants to empirical work (which has a robust talent pipeline!) understand, integrate, and even pivot in response to, the hard parts of the problem.
[edit: I say 'gears level' a couple times, but mean 'more in the direction of gears-level than the critiques that have existed so far']
If you wrote this exact post, it would have been upvoted enough for the Redwood team to see it, and they would have engaged with you similarly to how they engaged with John here (modulo some familiarity, because theyse people all know each other at least somewhat, and in some pairs very well actually).
If you wrote several posts like this, that were of some quality, you would lose the ability to appeal to your own standing as a reason not to write a post.
This is all I'm trying to transmit.
[edit: I see you already made the update I was encouraging, an hour after leaving the above comment to me. Yay!]
Writing (good) critiques is, in fact, a way many people gain standing. I’d push back on the part of you that thinks all of your good ideas will be ignored (some of them probably will be, but not all of them; don’t know until you try, etc).
More partial credit on the second to last point:
https://home.treasury.gov/news/press-releases/jy2766
Aside: I don’t think it’s just that real world impacts take time to unfold. Lately I’ve felt that evals are only very weakly predictive of impact (because making great ones is extremely difficult). Could be that models available now don’t have substantially more mundane utility (economic potential stemming from first order effects), outside of the domains the labs are explicitly targeting (like math and code), than models available 1 year ago.
Is the context on “reliable prediction and ELK via empirical route” just “read the existing ELK literature and actually follow it” or is it stuff that’s not written down? I assume you’ve omitted it to save time, and so no worries if the latter.
EDIT: I was slightly tempted to think of this also as ‘Ryan’s ranking of live agendas that aren’t control’, but I’m not sure if ‘what you expect to work conditional on delegating to AIs’ is similar to ‘what you expect to work if humans are doing most of it?’ (my guess is the lists would look similar, but with notable exceptions, eg humans pursuing GOFAI feels less viable than ML agents pursuing GOFAI)
My understanding is that ~6 months ago y’all were looking for an account of the tasks an automated AI safety researcher would hopefully perform, as part of answering the strategic question ‘what’s the next step after building [controlled] AGI?’ (with ‘actually stop there indefinitely’ being a live possibility)
This comment makes me think you’ve got that account of safety tasks to be automated, and are feeling optimistic about automated safety research.
Is that right and can you share a decently mechanistic account of how automated safety research might work?
[I am often skeptical of, to straw man the argument, ‘make ai that makes ai safe’, got the sense Redwood felt similarly, and now expect this may have changed.]
Thanks for the clarification — this is in fact very different from what I thought you were saying, which was something more like "FATE-esque concerns fundamentally increase x-risk in ways that aren't just about (1) resource tradeoffs or (2) side-effects of poorly considered implementation details."
Anthropic should take a humanist/cosmopolitan stance on risks from AGI in which risks related to different people having different values are very clearly deprioritized compared to risks related to complete human disempowerment or extinction, as worry about the former seems likely to cause much of the latter
Can you say more about the section I've bolded or link me to a canonical text on this tradeoff?
Question for Ben:
Are you inviting us to engage with the object level argument, or are you drawing attention to the existence of this argument from a not-obviously-unreasonable-source as a phenomenon we are responsible for (and asking us to update on that basis)?
On my read, he’s not saying anything new (concerns around military application are why ‘we’ mostly didn’t start going to the government until ~2-3 years ago), but that he’s saying it, while knowing enough to paint a reasonable-even-to-me picture of How This Thing Is Going, is the real tragedy.