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elifland
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[cross-posting from blog]
I made a spreadsheet for forecasting the 10th/50th/90th percentile for how you think GPT-4.5 will do on various benchmarks (given 6 months after the release to allow for actually being applied to the benchmark, and post-training enhancements). Copy it here to register your forecasts.
If you’d prefer, you could also use it to predict for GPT-5, or for the state-of-the-art at a certain time e.g. end of 2024 (my predictions would be pretty similar for GPT-4.5, and end of 2024).
You can see my forecasts made with ~2 hours of total effort on Feb 17 in this sheet; I won’t describe them further here in order to avoid anchoring.
There might be a similar tournament on Metaculus soon, but not sure on the timeline for that (and spreadsheet might be lower friction). If someone wants to take the time to make a form for predicting, tracking and resolving the forecasts, be my guest and I’ll link it here.
This is indeed close enough to Epoch’s median estimate of 7.7e25 FLOPs for Gemini Ultra 1.0 (this doc cites an Epoch estimate of around 9e25 FLOPs).
FYI at the time that doc was created, Epoch had 9e25. Now the notebook says 7.7e25 but their webpage says 5e25. Will ask them about it.
Interesting, thanks for clarifying. It’s not clear to me that this is the right primary frame to think about what would happen, as opposed to just thinking first about how big compute bottlenecks are and then adjusting the research pace for that (and then accounting for diminishing returns to more research).
I think a combination of both perspectives is best, as the argument in your favor for your frame is that there will be some low-hanging fruit from changing your workflow to adapt to the new cognitive labor.
Physical bottlenecks still exist, but is it really that implausible that the capabilities workforce would stumble upon huge algorithmic efficiency improvements? Recall that current algorithms are much less efficient than the human brain. There’s lots of room to go.
I don’t understand the reasoning here. It seems like you’re saying “Well, there might be compute bottlenecks, but we have so much room left to go in algorithmic improvements!” But the room to improve point is already the case right now, and seems orthogonal to the compute bottlenecks point.
E.g. if compute bottlenecks are theoretically enough to turn the 5x cognitive labor into only 1.1x overall research productivity, it will still be the case that there is lots of room for improvement but the point doesn’t really matter as research productivity hasn’t sped up much. So to argue that the situation has changed dramatically you need to argue something about how big of a deal the compute bottlenecks will in fact be.
Imagine the current AGI capabilities employee’s typical work day. Now imagine they had an army of AI assisstants that can very quickly do 10 hours worth of their own labor. How much more productive is that employee compared to their current state? I’d guess at least 5x. See section 6 of Tom Davidson’s takeoff speeds framework for a model.
Can you elaborate how you’re translating 10-hour AI assistants into a 5x speedup using Tom’s CES model?
I agree that <15% seems too low for most reasonable definitions of 1-10 hours and the singularity. But I’d guess I’m more sympathetic than you, depending on the definitions Nathan had in mind.
I think both of the phrases “AI capable doing tasks that took 1-10 hours” and “hit the singularity” are underdefined and making them more clear could lead to significantly different probabilities here.
For “capable of doing tasks that took 1-10 hours in 2024”:
If we’re saying that “AI can do every cognitive task that takes a human 1-10 hours in 2024 as well as (edit: the best)
ahuman expert”, I agree it’s pretty clear we’re getting extremely fast progress at that point not least because AI will be able to do the vast majority of tasks that take much longer than that by the time it can do all of 1-10 hour tasks.However, if we’re using a weaker definition like the one Richard used on most cognitive tasks, it beats most human experts who are given 1-10 hours to perform the task, I think it’s much less clear due to human interaction bottlenecks.
Also, it seems like the distribution of relevant cognitive tasks that you care about changes a lot on different time horizons, which further complicates things.
Re: “hit the singularity”, I think in general there’s little agreement on a good definition here e.g. the definition in Tom’s report is based on doubling time of “effective compute in 2022-FLOP” shortening after “full automation”, which I think is unclear what it corresponds to in terms of real-world impact as I think both of these terms are also underdefined/hard to translate into actual capability and impact metrics.
I would be curious to hear the definitions you and Nathan had in mind regarding these terms.
In his AI Insight Forum statement, Andrew Ng puts 1% on “This rogue AI system gains the ability (perhaps access to nuclear weapons, or skill at manipulating people into using such weapons) to wipe out humanity” in the next 100 years (conditional on a rogue AI system that doesn’t go unchecked by other AI systems existing). And overall 1 in 10 million of AI causing extinction in the next 100 years.
Among existing alignment research agendas/projects, Superalignment has the highest expected value
I’m mainly arguing against public AI safety advocacy work, which was recently upvoted highly on the EA Forum.
I had the impression that it was more than just that, given the line: “In light of recent news, it is worth comprehensively re-evaluating which sub-problems of AI risk are likely to be solved without further intervention from the AI risk community (e.g. perhaps deceptive alignment), and which ones will require more attention.” and the further attention devoted to deceptive alignment.
I appreciate these predictions, but I am not as interested in predicting personal of public opinions. I’m more interested in predicting regulatory stringency, quality, and scope.
If you have any you think faithfully represent a possible disagreement between us go ahead. I personally feel it will be very hard to operationalize objective stuff about policies in a satisfying way. For example, a big issue with the market you’ve made is that it is about what will happen in the world, not what will happen without intervention from AI x-risk people. Furthermore it has all the usual issues with forecasting on complex things 12 years in advance, regarding the extent to which it operationalizes any disagreement well (I’ve bet yes on it, but think it’s likely that evaluating and fixing deceptive alignment will remain mostly unsolved in 2035 conditional on no superintelligence, especially if there were no intervention from x-risk people).
I have three things to say here:
Thanks for clarifying.
Several months ago I proposed general, long-term value drift as a problem that I think will be hard to solve by default. I currently think that value drift is a “hard bit” of the problem that we do not appear to be close to seriously addressing, perhaps because people expect easier problems won’t be solved either without heroic effort. I’m also sympathetic to Dan Hendrycks’ arguments about AI evolution. I will add these points to the post.
Don’t have a strong opinion here, but intuitively feels like it would be hard to find tractable angles for work on this now.
I mostly think people should think harder about what the hard parts of AI risk are in the first place. It would not be surprising if the “hard bits” will be things that we’ve barely thought about, or are hard to perceive as major problems, since their relative hiddenness would be a strong reason to believe that they will not be solved by default.
Maybe. In general, I’m excited about people who have the talent for it to think about previously neglected angles.
The problem of “make sure policies are well-targeted, informed by the best evidence, and mindful of social/political difficulties” seems like a hard problem that societies have frequently failed to get right historically, and the relative value of solving this problem seems to get higher as you become more optimistic about the technical problems being solved.
I agree this is important and it was in your post but it seems like a decent description of what the majority of AI x-risk governance people are already working on, or at least not obviously a bad one. This is the phrase that I was hoping would get made more concrete.
I want to emphasize that the current policies were crafted in an environment in which AI still has a tiny impact on the world. My expectation is that policies will get much stricter as AI becomes a larger part of our life. I am not making the claim that current policies are sufficient; instead I am making a claim about the trajectory, i.e. how well we should expect society to respond at a time, given the evidence and level of AI capabilities at that time.
I understand this (sorry if wasn’t clear), but I think it’s less obvious than you do that this trend will continue without intervention from AI x-risk people. I agree with other commenters that AI x-risk people should get a lot of the credit for the recent push. I also provided example reasons that the trend might not continue smoothly or even reverse in my point (3).
There might also be disagreements around:
Not sharing your high confidence in slow, continuous takeoff.
The strictness of regulation needed to make a dent in AI risk, e.g. if substantial international coordination is required it seems optimistic to me to assume that the trajectory will by default lead to this.
The value in things getting done faster than they would have done otherwise, even if they would have been done either way. This indirectly provides more time to iterate and get to better, more nuanced policy.
I believe that current evidence supports my interpretation of our general trajectory, but I’m happy to hear someone explain why they disagree and highlight concrete predictions that could serve to operationalize this disagreement.
Operationalizing disagreements well is hard and time-consuming especially when we’re betting on “how things would go without intervention from a community that is intervening a lot”, but a few very rough forecasts, all conditional on no TAI before resolve date:
75%: In Jan 2028, less than 10% of Americans will consider AI the most important problem.
60%: In Jan 2030, Evan Hubinger will believe that if x-risk-motivated people had not worked on deceptive alignment at all, risk from deceptive alignment would be at least 50% higher, compared to a baseline of no work at all (i.e. if risk is 5% and it would be 9% with no work from anyone, it needs to have been >7% if no work from x-risk people had been done to resolve yes).
35%: In Jan 2028, conditional on a Republican President being elected in 2024, regulations on AI in the US will be generally less stringent than they were when the previous president left office.Edit:
Thus, due to no one’s intent, insufficiently justified concerns about current open-source AI are propagated to governance orgs, which recommend banning open source based on this research.
The recommendation that current open-source models should be banned is not present in the policy paper, being discussed, AFAICT. The paper’s recommendations are pictured below:
Edited to add: there is a specific footnote that says “Note that we do not claim that existing models are already too risky. We also do not make any predictions about how risky the next generation of models will be. Our claim is that developers need to assess the risks and be willing to not open-source a model if the risks outweigh the benefits” on page 31
I agree much of the community (including me) was wrong or directionally wrong in the past about the level of AI regulation and how quickly it would come.
Regarding the recommendations made in the post for going forward given that there will be some regulation, I feel confused in a few ways.
Can you provide examples of interventions that meet your bar for not being done by default? It’s hard to understand the takeaways from your post because the negative examples are made much more concrete than the proposed positive ones
You argue that we perhaps shouldn’t invest as much in preventing deceptive alignment because “regulators will likely adapt, adjusting policy as the difficulty of the problem becomes clearer”
If we are assuming that regulators will adapt and adjust regarding deception, can you provide examples of interventions that policymakers will not be able to solve themselves and why they will be less likely to notice and deal with them than deception?
You say “we should question how plausible it is that society will fail to adequately address such an integral part of the problem”. What things aren’t integral parts of the problem but that should be worked on?
I feel we would need much better evidence of things being handled competently to invest significantly less into integral parts of the problem.
You say: ‘Of course, it may still be true that AI deception is an extremely hard problem that reliably resists almost all attempted solutions in any “normal” regulatory regime, even as concrete evidence continues to accumulate about its difficulty—although I consider that claim unproven, to say the least’
If we expect some problems in AI risk to be solved by default mostly by people outside the community, it feels to me like one takeaway would be that we should shift resources to portions of the problem that we expect to be the hardest
To me, intuitively, deceptive alignment might be one of the hardest parts of the problem as we scale to very superhuman systems, even if we condition on having time to build model organisms of misalignment and experiment with them for a few years. So I feel confused about why you claim a high level of difficulty is “unproven” as a dismissal; of course it’s unproven but you would need to argue that in worlds where the AI risk problem is fairly hard, there’s not much of a chance of it being very hard.
As someone who is relatively optimistic about concrete evidence of deceptive alignment increasing substantially before a potential takeover, I think I still put significantly lower probability on it than you do due to the possibility of fairly fast takeoff.
I feel like this post is to some extent counting our chickens before they hatch (tbc I agree with the directional update as I said above). I’m not an expert on what’s going on here but I imagine any of the following happening (non-exhaustive list) that make the current path to potentially sensible regulation in the US and internationally harder:
The EO doesn’t lead to as many resources dedicated to AI-x-risk-reducing things as we might hope. I haven’t read it myself, just the fact sheet and Zvi’s summary but Zvi says “If you were hoping for or worried about potential direct or more substantive action, then the opposite applies – there is very little here in the way of concrete action, only the foundation for potential future action.”
A Republican President comes in power in the US and reverses a lot of the effects in the EO
Rishi Sunak gets voted out in the UK (my sense is that this is likely) and the new Prime Minister is much less gung-ho on AI risk
I don’t have strong views on the value of AI advocacy, but this post seems overconfident in calling it out as being basically not useful based on recent shifts.
It seems likely that much stronger regulations will be important, e.g. the model reporting threshold in the EO was set relatively high and many in the AI risk community have voiced support for an international pause if it were politically feasible, which the EO is far from.
The public still doesn’t consider AI risk to be very important. <1% of the American public considers it the most important problem to deal with. So to the extent that raising that number was good before, it still seems pretty good now, even if slightly worse.
fOh, I’m certainly not claiming that no-one should attempt to make the estimates.
Ah my bad if I lost the thread there
I’d want regulators to push for safer strategies, not to run checks on unsafe strategies—at best that seems likely to get a local minimum (and, as ever, overconfidence).
Seems like checks on unsafe strategies does well encourages safer strategies, I agree overconfidence is an issue though
More [evaluate the plan to get through the minefield], and less [estimate whether we’ll get blown up on the next step]
Seems true in an ideal world but in practice I’d imagine it’s much easier to get consensus when you have more concrete evidence of danger / misalignment. Seems like there’s lots of disagreement even within the current alignment field and I don’t expect that to change absent of more evidence of danger/misalignment and perhaps credible estimates.
To be clear I think if we could push a button for an international pause now it would be great, and I think it’s good to advocate for that to shift the Overton Window if nothing else, but in terms of realistic plans it seems good to aim for stuff a bit closer to evaluating the next step than overall policies, for which there is massive disagreement.
(of course there’s a continuum between just looking at the next step and the overall plan, there totally should be people doing both and there are so it’s a question at the margin, etc.)
The other portions of your comment I think I’ve already given my thoughts on previously, but overall I’d say I continue to think it depends a lot on the particulars of the regulation and the group doing the risk assessment; done well I think it could set up incentives well but yes if done poorly it will get Goodharted. Anyway, I’m not sure it’s particularly likely to get enshrined into regulation anytime soon, so hopefully we will get some evidence as to how feasible it is and how it’s perceived via pilots and go from there.
GPT-4 + unknown unknowns = stop. (whether they say “unknown unknowns so 5% chance of 8 billion deaths”, or “unknown unknowns so 0.1% chance of 8 billion deaths
I feel like .1% vs. 5% might matter a lot, particularly if we don’t have strong international or even national coordination and are trading off more careful labs going ahead vs. letting other actors pass them. This seems like the majority of worlds to me (i.e. without strong international coordination where US/China/etc. trust each other to stop and we can verify that), so building capacity to improve these estimates seems good. I agree there are also tradeoffs around alignment research assistance that seem relevant. Anyway, overall I’d be surprised if it doesn’t help substantially to have more granular estimates.
my worry isn’t that it’s not persuasive that time. It’s that x will become the standard, OpenAI will look at the report, optimize to minimize the output of x, and the next time we’ll be screwed.
This seems to me to be assuming a somewhat simplistic methodology for the risk assessment; again this seems to come down to how good the team will be, which I agree would be a very important factor.
Thanks for calling me out on this. I think you’re likely right. I will cross out that line of the comment, and I have updated toward the effect size of strong AI regulation being larger and am less skeptical of the 10x risk reduction, but my independent impression would still be much lower (~1.25x or smth, while before I would have been at ~1.15x).
I still think the AI case has some very important differences with the examples provided due to the general complexity of the situation and the potentially enormous difficulty of aligning superhuman AIs and preventing misuse (this is not to imply you disagree, just stating my view).
I would strongly disagree with a claim that +3 OOMs of effort and a many-year pause can’t cut risk by much
This seems to be our biggest crux, as I said interested in analyses of alignment difficulty distribution if any onlookers know. Also, a semantic point but under my current views I’d view cutting ~5% of the risk as a huge deal that’s at least an ~80th percentile outcome for the AI risk community if it had a significant counterfactual impact on it, but yes not much compared to 10x.
[EDIT: After thinking about this more I’ve realized that I was to some extent conflating my intuition that it will be hard for the x-risk community to make a large counterfactual impact on x-risk % with the intuition that +3 OOMs of effort doesn’t cut more than ~5% of the risk. I haven’t thought much about exact numbers but now maybe ~20% seems reasonable to me now]Quick thoughts on the less cruxy stuff:
You need to apply consistent standards that output “unsafe” in >90% of cases where things really are unsafe.
Fair, though I think 90% would be too low and the more you raise the longer you have to maintain the pause.
(based on context) I’m implicitly anchoring to the levels of political will that would be required to implement something like a global moratorium
This might coincidentally be close to the 95th percentile I had in mind.
So at that point you obviously aren’t talking about 100% of countries voluntarily joining
Fair, I think I was wrong on that point. (I still think it’s likely there would be various other difficulties with enforcing either RSPs or a moratorium for an extended period of time, but I’m open to changing mind)
I’m not convinced open source models are a relevant risk (since the whole proposal is gating precautions on hazardous capabilities of models rather than size, and so again I think that’s fair to include as part of “very good”)
Sorry if I wasn’t clear: my worry is that open-source models will get better over time due to new post-training enhancements, not about their capabilities upon release.
Thanks! I agree with a lot of this, will pull out the 2 sentences I most disagree with. For what it’s worth I’m not confident that this type of risk assessment would be a very valuable idea (/ which versions would be best). I agree that there is significant risk of non-cautious people doing this poorly.
The reason I’m against it is that it’ll actually be rough and unprincipled, but this will not clear—in most people’s minds (including most regulators, I imagine) it’ll map onto the kind of systems that we have for e.g. nuclear risks.
I think quantifying “rough and unprincipled” estimates is often good, and if the team of forecasters/experts is good then in the cases where we have no idea what is going on yet (as you mentioned, the x-risk case especially as systems get stronger) they will not produce super confident estimates. If the forecasts were made by the same people and presented in the exact same manner as nuclear forecasts, that would be bad, but that’s not what I expect to happen if the team is competent.
The first makes this kind of approach unnecessary—better to get the cautious people make the case that we have no solid basis to make these assessments that isn’t a wild guess.
I’d guess something like “expert team estimates a 1% chance of OpenAI’s next model causing over 100 million deaths, causing 1 million deaths in expectation” might hit harder to policymakers than “experts say we have no idea whether OpenAI’s models will cause a catastrophe”. The former seems to sound more clearly an alarm than the latter. This is definitely not my area of expertise though.
I don’t think that voluntary implementation of RSPs is a substitute for regulatory requirements and international collaboration (and tried to emphasize this in the post). In talking about a 10x risk reduction I’m absolutely imagining international coordination to regulate AI development.
Appreciate this clarification.
I think that very good RSPs would effectively require a much longer pause if alignment turns out to be extremely difficult.
(but conditioning on a very good implementation)
I’m still confused about the definition of “very good RSPs” and “very good implementation” here. If the evals/mitigations are defined and implemented in some theoretically perfect way by all developers of course it will lead to drastically reduced risk, but “very good” has a lot of ambiguity. I was taking it to mean something like “~95th percentile of the range of RSPs we could realistically hope to achieve before doom”, but you may have meant something different. It’s still very hard for me to see how under the definition I’ve laid out we could get to a 10x reduction.
Even just priors on how large effect sizes of interventions are feels like it brings it under 10x unless there are more detailed arguments given for 10x, but I’ll give some more specific thoughts below.I think that very good RSPs would effectively require a much longer pause if alignment turns out to be extremely difficult.
In terms of “mistakes in evals” I don’t think this is the right picture of how this works. If you have noticed serious enough danger that leading developers have halted further development, and also have multiple years of experience with those systems establishing alignment difficulty and the nature of dangerous capabilities, you aren’t just relying on other developers to come up with their own independent assessments. You have an increasingly robust picture of what would be needed to proceed safely, and if someone claims that actually they are the one developer who has solved safety, that claim is going to be subject to extreme scrutiny.
I agree directionally with all of the claims you are making, but (a) I’d guess I have much less confidence than you that even applying very large amounts of effort / accumulated knowledge we will be able to reliably classify a system as safe or not (especially once it is getting close to and above human-level) and (b) even if we could after several years do this reliably, if you have to do a many-year pause there are various other sources of risk like countries refusing to join / pulling out of the pause and risks from open-source models including continued improvements via fine-tuning/scaffolding/etc.
I guess I don’t think situations will be that “normal-ish” in the world where a $10 trillion industry has been paused for years over safety concerns, and in that regime I think we have more like 3 orders of magnitude of gap between “low effort” and “high effort” which is actually quite large. I also think there very likely ways to get several orders of magnitude of additional output with AI systems using levels of caution that are extreme but knowably possible
Yeah normal-ish was a bad way to put it. I’m skeptical that 3 marginal OOMs is significantly more than ~5% probability to tip the scales but this is just intuition (if anyone knows of projects on the distribution of alignment difficulty, would be curious). I agree that automating alignment is important and that’s where a lot of my hope comes from.
[EDIT: After thinking about this more I’ve realized that I was to some extent conflating my intuition that it will be hard for the x-risk community to make a large counterfactual impact on x-risk % with the intuition that +3 OOMs of effort doesn’t cut more than ~5% of the risk. I haven’t thought much about exact numbers but now maybe ~20% seems reasonable to me now][edited to remove something that was clarified in another comment]
I’d be curious whether you think that it has been a good thing for Dario Amodei to publicly state his AI x-risk estimate of 10-25%, even though it’s very rough and unprincipled. If so, would it be good for labs to state a very rough estimate explicitly for catastrophic risk in the next 2 years, to inform policymakers and the public? If so, why would having teams with ai, forecasting, and risk management expertise make very rough estimates of risk from model training/deployment and releasing them to policymakers and maybe the public be bad?
I’m curious where you get off the train of this being good, particularly when it becomes known to policymakers and the public that a model could pose significant risk, even if we’re not well-calibrated on exactly what the risk level is.
I think 356 or more people in the population needed to make there be a >5% of 2+ deaths in a 2 month span from that population