AnonResearcherMajorAILab

Karma: 161

AnonResearcherMajorAILab 4 Oct 2023 7:12 UTC
3 points
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in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
The way I meant it, 82% goes up to 89%.
Oops, sorry for the misunderstanding.
Taking your numbers at face value, and assuming that people have on average 40 years of life ahead of them (Google suggests median age is 30 and typical lifespan is 70-80), the pause gives an expected extra 2.75 years of life during the pause (delaying 55% chance of doom by 5 years) while removing an expected extra 2.1 years of life (7% of 30) later on. This looks like a win on current-people-only views, but it does seem sensitive to the numbers.
I’m not super sold on the numbers. Removing the full 55% is effectively assuming that the pause definitely happens and is effective—it neglects the possibility that advocacy succeeds enough to have the negative effects, but still fails to lead to a meaningful pause. I’m not sure how much probability I assign to that scenario but it’s not negligible, and it might be more than I assign to “advocacy succeeds and effective pause happens”.
It sounds like you don’t buy (1) at all.
I’d say it’s more like “I don’t see why we should believe (1) currently”. It could still be true. Maybe all the other methods really can’t work for some reason I’m not seeing, and that reason is overcome by public advocacy.

AnonResearcherMajorAILab 2 Oct 2023 8:26 UTC
1 point
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in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
So, the tradeoff is something like 55% death spread over a five-year period vs no death for five years, for an eventual reward of reducing total chance of death (over a 10y period or whatever) from 89% to 82%.
Oh we disagree much more straightforwardly. I think the 89% should be going up, not down. That seems by far the most important disagreement.
(I thought you were saying that person-affecting views means that even if the 89% goes up that could still be a good trade.)
I still don’t know why you expect the 89% to go down instead of up given public advocacy. (And in particular I don’t see why optimism vs pessimism has anything to do with it.) My claim is that it should go up.
I was thinking of something like the scenario you describe as “Variant 2: In addition to this widespread pause, there is a tightly controlled and monitored government project aiming to build safe AGI.” It doesn’t necessarily have to be government-led, but maybe the government has talked to evals experts and demands a tight structure where large expenditures of compute always have to be approved by a specific body of safety evals experts.
But why do evals matter? What’s an example story where the evals prevent Molochian forces from leading to us not being in control? I’m just not seeing how this scenario intervenes on your threat model to make it not happen.
(It does introduce government bureaucracy, which all else equal reduces the number of actors, but there’s no reason to focus on safety evals if the theory of change is “introduce lots of bureaucracy to reduce number of actors”.)
Maybe I’m wrong: If the people who are closest to DC are optimistic that lawmakers would be willing to take ambitious measures soon enough
This seems like the wrong criterion. The question is whether this strategy is more likely to succeed than others. Your timelines are short enough that no ambitious measure is going to come into place fast enough if you aim to save ~all worlds.
But e.g. ambitious measures in ~5 years seems very doable (which seems like it is around your median, so still in time for half of worlds). We’re already seeing signs of life:
note the existence of the UK Frontier AI Taskforce and the people on it, as well as the intent bill SB 294 in California about “responsible scaling”
You could also ask people in DC; my prediction is they’d say something reasonably similar.

AnonResearcherMajorAILab 1 Oct 2023 9:06 UTC
7 points
1 ∶ 0
in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
From my perspective you’re assuming we’re in an unlikely world where the public turns out to be saner than our baseline would suggest.
Hm, or that we get lucky in terms of the public’s response being a good one given the circumstances, even if I don’t expect the discourse to be nuanced.
That sounds like a rephrasing of what I said that puts a positive spin on it. (I don’t see any difference in content.)
To put it another way—you’re critiquing “optimistic EAs” about their attitudes towards institutions, but presumably they could say “we get lucky in terms of the institutional response being a good one given the circumstances”. What’s the difference between your position and theirs?
But my sense is that it’s unlikely we’d succeed at getting ambitious measures in place without some amount of public pressure.
Why do you believe that?
It also matters how much weight you give to person-affecting views (I’ve argued here for why I think they’re not unreasonable).
I don’t think people would be on board with the principle “we’ll reduce the risk of doom in 2028, at the cost of increasing risk of doom in 2033 by a larger amount”.
For me, the main argument in favor of person-affecting views is that they agree with people’s intuitions. Once a person-affecting view recommends something that disagrees with other ethical theories and with people’s intuitions, I feel pretty fine ignoring it.
The 89% p(doom) is more like what we should expect by default: things get faster and out of control and then that’s it for humans.
Your threat model seems to be “Moloch will cause doom by default, but with AI we have one chance to prevent that, but we need to do it very carefully”. But Molochian forces grow much stronger as you increase the number of actors! The first intervention would be to keep the number of actors involved as small as possible, which you do by having the few leaders race forward as fast as possible, with as much secrecy as possible. If this were my main threat model I would be much more strongly against public advocacy and probably also against both conditional and unconditional pausing.
(I do think 89% is high enough that I’d start to consider negative-expectation high-variance interventions. I would still be thinking about it super carefully though.)

AnonResearcherMajorAILab 1 Oct 2023 8:40 UTC
7 points
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in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
even with the pause mechanism kicking in (e.g., “from now on, any training runs that use 0.2x the compute of the model that failed the eval will be prohibited”), algorithmic progress at another lab or driven by people on twitter(/X) tinkering with older, already-released models, will get someone to the same capability threshold again soon enough. [...] you can still incorporate this concern by adding enough safety margin to when your evals trigger the pause button.
Safety margin is one way, but I’d be much more keen on continuing to monitor the strongest models even after the pause has kicked in, so that you notice the effects of algorithmic progress and can tighten controls if needed. This includes rolling back model releases if people on Twitter tinker with them to exceed capability thresholds. (This also implies no open sourcing, since you can’t roll back if you’ve open sourced the model.)
But I also wish you’d say what exactly your alternative course of action is, and why it’s better. E.g. the worry of “algorithmic progress gets you to the threshold” also applies to unconditional pauses. Right now your comments feel to me like a search for anything negative about a conditional pause, without checking whether that negative applies to other courses of action.

AnonResearcherMajorAILab 30 Sep 2023 11:01 UTC
2 points
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in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
Sounds like we roughly agree on actions, even if not beliefs (I’m less sold on fast / discontinuous takeoff than you are).
As a minor note, to keep incentives good, you could pay evaluators / auditors based on how much performance they are able to elicit. You could even require that models be evaluated by at least three auditors, and split up payment between them based on their relative performances. In general it feels like there a huge space of possibilities that has barely been explored.

AnonResearcherMajorAILab 30 Sep 2023 10:47 UTC
1 point
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in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
That’s not how I’d put it. I think we are still in a “typical” world, but the world that optimistic EAs assume we are in is the unlikely one where institutions around AI development an deployment suddenly turn out to be saner than our baseline would suggest.
I don’t see at all how this justifies that public advocacy is good? From my perspective you’re assuming we’re in an unlikely world where the public turns out to be saner than our baseline would suggest. I don’t think I have a lot of trust in institutions (though maybe I do have more trust than you do); I think I have a deep distrust of politics and the public.
I’m also not sure I understood your original argument any more. The argument I thought you were making was something like:
Consider an instrumental variable like “quality-weighted interventions that humanity puts in place to reduce AI x-risk”. Then public advocacy is:
1. Negative expectation: Public advocacy reduces the expected value of quality-weighted interventions, for the reasons given in the post.
2. High variance: Public advocacy also increases the variance of quality-weighted interventions (e.g. maybe we get a complete ban on all AI, which seems impossible without public advocacy).
However, I am pessimistic:
1. Pessimism: The required quality-weighted interventions to avoid doom is much higher than the default quality-weighted interventions we’re going to get.
Therefore, even though public advocacy is negative-expectation on quality-weighted interventions, it still reduces p(doom) due to its high variance.
(This is the only way I see to justify rebuttals like “I’m in an overall more pessimistic state where I don’t mind trying more desperate measures”, though perhaps I’m missing something.)
Is this what you meant with your original argument? If not, can you expand?
I think it’s not just the MIRI-sphere that’s very pessimistic
What is your p(doom)?
For reference, I think it seems crazy to advocate for negative-expectation high-variance interventions if you have p(doom) < 50%. As a first pass heuristic, I think it still seems pretty unreasonable all the way up to p(doom) of < 90%, though this could be overruled by details of the intervention (how negative is the expectation, how high is the variance).

AnonResearcherMajorAILab 30 Sep 2023 9:05 UTC
1 point
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in reply to: Oliver Sourbut’s comment on: Aim for conditional pauses
it seems like this might be a (minor?) loadbearing belief?
Yes, if I changed my mind about this I’d have to rethink my position on public advocacy. I’m still pretty worried about the other disadvantages so I suspect it wouldn’t change my mind overall, but I would be more uncertain.

AnonResearcherMajorAILab 30 Sep 2023 8:58 UTC
3 points
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in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
I like your points against the value of public advocacy. I’m not convinced overall, but that’s probably mostly because I’m in an overall more pessimistic state where I don’t mind trying more desperate measures.
People say this a lot, but I don’t get it.
1. Your baseline has to be really pessimistic before it looks good to throw in a negative-expectation high-variance intervention. (Perhaps worth making some math models and seeing when it looks good vs not.) Afaict only MIRI-sphere is pessimistic enough for this to make sense.
2. It’s very uncooperative and unilateralist. I don’t know why exactly it has became okay to say “well I think alignment is doomed, so it’s fine if I ruin everyone else’s work on alignment with a negative-expectation intervention”, but I dislike it and want it to stop.
Or to put it a bit more viscerally: It feels crazy to me that when I say “here are reasons your intervention is increasing x-risk”, the response is “I’m pessimistic, so actually while I agree that the effect in a typical world is to increase x-risk, it turns out that there’s this tiny slice of worlds where it made the difference and that makes the intervention good actually”. It could be true, but it sure throws up a lot of red flags.
Or in meme form:

AnonResearcherMajorAILab 30 Sep 2023 8:22 UTC
4 points
2 ∶ 0
in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
I feel like there are already a bunch of misaligned incentives for alignment researchers (and all groups of people in general), so if the people on the safety team aren’t selected to be great at caring about the best object-level work, then we’re in trouble either way.
I’m not thinking that much about the motivations of the people on the safety team. I’m thinking of things like:
1. Resourcing for the safety team (hiring, compute, etc) is conditioned on whether the work produces big splashy announcements that the public will like
2. Other teams in the company that are doing things that the public likes will be rebranded as safety teams
3. When safety teams make recommendations for safety interventions on the strongest AI systems, their recommendations are rejected if the public wouldn’t like them
4. When safety teams do outreach to other researchers in the company, they have to self-censor for fear that a well-meaning whistleblower will cause a PR disaster by leaking an opinion of the safety team that the public has deemed to be wrong
See also Unconscious Economics.
(I should have said that companies will face pressure, rather than safety teams. I’ll edit that now.)

AnonResearcherMajorAILab 30 Sep 2023 8:07 UTC
10 points
2 ∶ 1
in reply to: Lukas_Gloor’s comment on: Aim for conditional pauses
A conditional pause fails to prevent x-risk if either:
- The AI successfully exfiltrates itself (which is what’s needed to defeat rollback mechanisms) during training or evaluation, but before deployment.
- The AI successfully sandbags the evaluations. (Note that existing conditional pause proposals depend on capability evaluations, not alignment evaluations.)
(Obviously there’s also the normal institutional failures, e.g. if a company simply ignores the evaluation requirements and forges ahead. I’m setting those aside here.)
Both of these seem extremely difficult to me (likely beyond human-level, in the sense that if you somehow put a human in the situation the AI would be in, I would expect the human to fail).
How likely do you think it is that we get an AI capable of one of these failure modes, before we see an AI capable of e.g. passing 10 out of the 12 ARC Evals tasks? My answer would be “negligible”, and so I’m at least in favor of “pause once you pass 10 out of 12 ARC Evals tasks” over “pause now”. I think we can raise the difficulty of the bar a decent bit more before my answer stops being “negligible”.
I don’t think this depends on takeoff speeds at all, since I’d expect a conditional pause proposal to lead to a pause well before models are automating 20% of tasks (assuming no good mitigations in place by that point).
I don’t think this depends on timelines, except inasmuch as short timelines correlates with discontinuous jumps in capability. If anything it seems like shorter timelines argue more strongly for a conditional pause proposal, since it seems far easier to build support for and enact a conditional pause.

AnonResearcherMajorAILab 29 Sep 2023 7:48 UTC
3 points
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in reply to: gergo’s comment on: Aim for conditional pauses
Yes, in the longer term you would need to scale up the evaluation work that is currently going on. It doesn’t have to be done by the alignment community; there are lots of capable ML researchers and engineers who can do this (and I expect at least some would be interested in it).
I could see this being less of an issue if the evaluations only need to happen for the models that are really the most capable at a given point in time
Yes, I think that’s what you would do.
my worry would be that as capabilities increase, even if we test the top models rigorously, the second-tier models could still end up doing harm.
The proposal would be that if the top models are risky, then you pause. So, if you haven’t paused, then that means your tests concluded that the top models aren’t risky. In that case I don’t know why you expect the second-tier models to be risky?

AnonResearcherMajorAILab 29 Sep 2023 7:33 UTC
7 points
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in reply to: Davidmanheim’s comment on: Aim for conditional pauses
I didn’t say “blanket ban on ML,” I said “a blanket ban on very large models.”
I know? I never said you asked for a blanket ban on ML?
Because I have not seen, and don’t think anyone can make, a clear criterion for “too high risk of doom,”
1. My post discusses “pause once an agent passes 10 or more of the ARC Evals tasks”. I think this is too weak a criterion and I’d argue for a harder test, but I think this is already better than a blanket ban on very large models.
2. Anthropic just committed to a conditional pause.
3. ARC Evals is pushing responsible scaling, which is a conditional pause proposal.
But also, the blanket ban on very large models is implicitly saying that “more powerful than GPT-4” is the criterion of “too high risk of doom”, so I really don’t understand at all where you’re coming from.

AnonResearcherMajorAILab 29 Sep 2023 7:23 UTC
−2 points
0 ∶ 1
in reply to: Greg_Colbourn’s comment on: Aim for conditional pauses
Every comment of yours so far has misunderstood or misconstrued at least one thing I said, so I’m going to bow out now.

AnonResearcherMajorAILab 28 Sep 2023 7:35 UTC
1 point
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in reply to: Greg_Colbourn’s comment on: Aim for conditional pauses
we can’t reliably calibrate that systems more powerful than GPT-4 would not be very dangerous.
Seems false (unless you get Pascal’s Mugged by the word “reliably”).
We’re pretty good at evaluating models at GPT-4 levels of capability, and they don’t seem capable of autonomous replication. (I believe that evaluation is flawed because ARC didn’t have finetuning access, but we could do better evaluations.) I don’t see any good reason to expect this to change for GPT-4.5.
Or as a more social argument: I can’t think of a single ML expert who’d agree with your claim (including ones who work in alignment, but excluding alignment researchers who aren’t ML experts, so e.g. not Connor Leahy or Eliezer Yudkowsky). Probably some do exist, but it seems like it would be a tiny minority.
I really don’t think this is extreme at all.
I was using the word extreme to mean “one end of the spectrum” rather than “crazy”. I’ve changed it to “end” instead.
Though I do think it would both be an overreaction and would increase x-risk, so I am pretty strongly against it.

AnonResearcherMajorAILab 28 Sep 2023 7:07 UTC
7 points
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in reply to: Davidmanheim’s comment on: Aim for conditional pauses
I think that where we differ most is a strategic detail about how to get to the point where we have a robust international system that conditionally bans only potentially dangerous systems, namely, whether we need a blanket ban on very large models until that system is in place.
Yes, but it seems like an important detail, and I still haven’t seen you give a single reason why we should do the blanket ban instead of the conditional one. (I named four in my post, but I’m not sure if you care about any of them.)
The conditional one should get way more support! That seems really important! Why is everyone dying on the hill of a blanket ban?
I don’t think you can make general AI systems that are powerful enough to be used for misuse that aren’t powerful enough to pose risks of essentially full takeoff.
There are huge differences between misuse threat models and misalignment threat models, even if you set aside whether models will be misaligned in the first place:
1. In misuse, an AI doesn’t have to hide its bad actions (e.g. it can do chains of thought that explicitly reason about how to cause harm).
2. In misuse, an AI gets active help from humans (e.g. maybe the AI is great at creating bioweapon designs, but only the human can get them synthesized and released into the world).
It’s possible that the capabilities to overcome these issues comes at the same time as the capability for misuse, but I doubt that will happen.
Also, this seems to contradict your previous position:
The first is that x-risks from models come in many flavors, and uncontrolled AI takeoff is only one longer term concern. If there are nearer term risks from misuse, which I think are even more critical for immediate action, we should be worried about short timelines, in the lower single digit year range.

AnonResearcherMajorAILab 27 Sep 2023 7:06 UTC
0 points
0 ∶ 2
in reply to: Greg_Colbourn’s comment on: Aim for conditional pauses
You’re right, you can’t do it. Therefore we should not call for a pause on systems more powerful than GPT-4, because we can’t reliably calibrate that systems more powerful than GPT-4 would be plausibly very dangerous. </sarcasm>
If you actually engage with what I write, I’ll engage with what you write. For reference, here’s the paragraph around the line you quoted:
We can get much wider support for a conditional pause. Most people can get on board with the principle “if an AI system would be very dangerous, don’t build it”, and then the relevant details are about when a potential AI system should be considered plausibly very dangerous. At one extreme, a typical unconditional pause proposal would say “anything more powerful than GPT-4 should be considered plausibly very dangerous”. As you make the condition less restrictive and more obviously tied to harm, it provokes less and less opposition.
There are also a couple of places where I discuss specific conditional pause proposals, you might want to read those too.

AnonResearcherMajorAILab 27 Sep 2023 6:59 UTC
5 points
2 ∶ 0
in reply to: Davidmanheim’s comment on: Aim for conditional pauses
If there are nearer term risks from misuse, which I think are even more critical for immediate action, we should be worried about short timelines, in the lower single digit year range. (Perhaps you disagree?)
I agree, but in that case I’m even more baffled by the call for an unconditional pause. It’s so much easier to tell whether an AI system can be misused, relative to whether it is misaligned! Why would you not just ask for a ban on AI systems that pose great misuse risks?
In the rest of your comment you seem to assume that I think we shouldn’t push for action now. I’m not sure why you think that—I’m very interested in pushing for a conditional pause now, because as you say it takes time to actually implement.
(I agree that we mostly don’t disagree.)

AnonResearcherMajorAILab 26 Sep 2023 7:10 UTC
17 points
3 ∶ 0
in reply to: mic’s comment on: Aim for conditional pauses
I don’t know that the AGI labs as organizations are best modeled as trying to do good, rather than optimizing for objectives like outperforming competitors, attracting investment, and advancing exciting capabilities – subject to some safety-related concerns from leadership.
I will go further—it’s definitely the latter one for at least Google DeepMind and OpenAI; Anthropic is arguable. I still think that’s a much better situation than having public pressure when the ask is very nuanced (as it would be for alignment research).
For example, I’m currently glad that the EA community does not have the power to exert much pressure on the work done by the safety teams at AGI labs, because the EA community’s opinions on what alignment research should be done are bad, and the community doesn’t have the self-awareness to notice that themselves, and so instead safety teams at labs would have to spend hundreds of hours writing detailed posts explaining their work to defuse the pressure from the EA community.
At least there I expect the safety teams could defuse the pressure by spending hundreds of hours writing detailed posts, because EAs will read that. With the public there’s no hope of that, and safety teams instead just won’t do the work that they think is good, and instead do work that they can sell to the public.

AnonResearcherMajorAILab 26 Sep 2023 6:59 UTC
4 points
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in reply to: Aaron_Scher’s comment on: Aim for conditional pauses
I agree it’s important to think about the perceived opportunity cost as well, and that’s a large part of why I’m uncertain. I probably should have said that in the post.
I’d still guess that overall the increased clarity on risks will be the bigger factor—it seems to me that risk aversion is a much larger driver of policy than worries about economic opportunity cost (see e.g. COVID lockdowns). I would be more worried about powerful AI systems being seen as integral to national security; my understanding is that national security concerns drive a lot of policy. (But this could potentially be overcome with international agreements.)

AnonResearcherMajorAILab 26 Sep 2023 6:41 UTC
1 point
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in reply to: Aaron_Scher’s comment on: Aim for conditional pauses
Yeah, unless we get a lot better at alignment, the conditional pause should hit well before we create automated researchers.