Former senior advisor at Open Philanthropy. Doctorate in philosophy from the University of Oxford. Opinions my own.
Joe Carlsmith
Karma: 5,684
By default step 3 (reward-on-the-episode seekers aren’t directly optimizing for your future efforts at studying their generalization to fail in the direction of AI takeover), but I do think the line here can get a bit blurry.
I appreciated the detailed discussion and literature review here—thanks.
I’m sympathetic to this—thanks Thomas.
Hi Steve—thanks for this comment, I can see how the vibe of the talk/piece might call to mind something like “studying/intervening on an existing AI system” rather than focusing on how its trained/constructed, but I do mean for the techniques I discuss to cover both. For example, and re: your Bob example, I talk about our existing knowledge of human behavior as an example of behavioral science here—and I talk lot about studying training as a part of behavioral science, e.g.:
Let’s call an AI’s full range of behavior across all safe and accessible-for-testing inputs its “accessible behavioral profile.” Granted the ability to investigate behavioral profiles of this kind in-depth, it also becomes possible to investigate in-depth the effect that different sorts of interventions have on the profile in question. Example effects like this include: how the AI’s behavioral profile changes over the course of training; how the behavioral profile varies across different forms of training; how it responds to other kinds of interventions on the AI’s internals (though: this starts to border on “transparency tools”); how it varies based on the architecture of the AI; etc. Here I sometimes imagine a button that displays some summary of an AI’s accessible behavioral profile when pressed. In principle, you could be pressing that button constantly, whenever you do anything to an AI, and seeing what you can learn.
And techniques for training/constructing AIs that benefit from understanding/direct design of their internals would count as “transparency tools” for me.
I’m a bit confused about your overall picture here. Sounds like you’re thinking something like:
“almost everything in the world is evaluable via waiting for it to fail and then noticing this. Alignment and bridge-building aren’t like this, but most other things are… Also, the way we’re going to automate long-horizon tasks is via giving AIs long-term goals. In particular: we’ll give them goal ‘get long-term human approval/reward’, which will lead to good-looking stuff until the AIs take over in order to get more reward. This will work for tons of stuff but not for alignment, because you can’t give negative reward for the alignment failure we ultimately care about, which is the AIs taking over.”
Is that roughly right?
I think it’s a fair point that if it turns out that current ML methods are broadly inadequate for automating basically any sophisticated cognitive work (including capabilities research, biology research, etc—though I’m not clear on your take on whether capabilities research counts as “science” in the sense you have in mind), it may be that whatever new paradigm ends up successful messes with various implicit and explicit assumptions in analyses like the one in the essay.
That said, I think if we’re ignorant about what paradigm will succeed re: automating sophisticated cognitive work and we don’t have any story about why alignment research would be harder, it seems like the baseline expectation (modulo scheming) would be that automating alignment is comparably hard (in expectation) to automating these other domains. (I do think, though, that we have reason to expect alignment to be harder even conditional on needing other paradigms, because I think it’s reasonable to expect some of the evaluation challenges I discuss in the post to generalize to other regimes.)
I’m happy to say that easy-to-verify vs. hard-to-verify is what ultimately matters, but I think it’s important to be clear what about makes something easier vs. harder to verify, so that we can be clear about why alignment might or might not be harder than other domains. And imo empirical feedback loops and formal methods are amongst the most important factors there.
If we assume that the AI isn’t scheming to actively withhold empirically/formally verifiable insights from us (I do think this would make life a lot harder), then it seems to me like this is reasonably similar to other domains in which we need to figure out how to elicit as-good-as-human-level suggestions from AIs that we can then evaluate well. E.g., it’s not clear to me why this would be all that different from “suggest a new transformer-like architecture that we can then verify improves training efficiency a lot on some metric.”
Or put another way: at least in the context of non-schemers, the thing I’m looking for isn’t just “here’s a way things could be hard.” I’m specifically looking for ways things will be harder than in the context of capabilities (or, to a lesser extent, in other scientific domains where I expect a lot of economic incentives to figure out how to automate top-human-level work). And in that context, generic pessimism about e.g. heavy RL doesn’t seem like it’s enough.
Sure, maybe there’s a band of capability where you can take over but you can’t do top-human-level alignment research (and where your takeover plan doesn’t involve further capabilities development that requires alignment). It’s not the central case I’m focused on, though.
Also, if there’s an alignment tax (or control tax), then that impacts the comparison, since the AIs doing alignment research are paying that tax whereas the AIs attempting takeover are not.
Is the thought here that the AIs trying to takeover aren’t improving their capabilities in a way that requires paying an alignment tax? E.g. if the tax refers to a comparison between (a) rushing forward on capabilities in a way that screws you on alignment vs. (b) pushing forward on capabilities in a way that preserves alignment, AIs that are fooming will want to do (b) as well (though they may have an easier time of it for other reasons). But if it refers to e.g. “humans will place handicaps on AIs that they need to ensure are aligned, including AIs they’re trying to use for alignment research, whereas rogue AIs that have also freed themselves human control will also be able to get rid of these handicaps,” then yes, that’s an advantage the rogue AIs will have (though note that they’ll still need to self-filtrate etc).
Re: examples of why superintelligences create distinctive challenges: superintelligences seem more likely to be schemers, more likely to be able to systematically and successfully mess with the evidence provided by behavioral tests and transparency tools, harder to exert option-control over, better able to identify and pursue strategies humans hadn’t thought of, harder to supervise using human labor, etc.
If you’re worried about shell games, it’s OK to round off “alignment MVP” to hand-off-ready AI, and to assume that the AIs in question need to be able to pursue make and pursue coherent long-term plans.[1] I don’t think the analysis in the essay alters that much (for example, I think very little rests on the idea that you can get by with myopic AIs), and better to err on the side of conservatism.
I wanted to set aside “hand-off” here because in principle, you don’t actually need to hand-off until humans stop being able to meaningfully contribute to the safety/quality of the automated alignment work, which doesn’t necessarily need to start around the time we have AIs capable of top-human-level alignment work (e.g., human evaluation of the research, or involvement in other aspects of control—e.g., providing certain kinds of expensive, trusted supervision—could persist after that). And when exactly you hand-off depends on a bunch of more detailed, practical trade-offs.
As I said in the post, one way that humans still being involved might not bottleneck the process is if they’re only reviewing the work to figure out whether there’s a problem they need to actively intervene on.
even if human labor is still playing a role in ensuring safety, it doesn’t necessarily need to directly bottleneck the research process – or at least, not if things are going well. For example: in principle, you could allow a fully-automated alignment research process to proceed forward, with humans evaluating the work as it gets produced, but only actively intervening if they identify problems.
And I think you can still likely radically speed up and scale up your alignment research even e.g. you still care about humans reviewing and understanding the work in question.
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Though for what it’s worth I don’t think that the task of assessing “is this a good long-term plan for achieving X” needs itself to involve long-term optimization for X. For example: you could do that task over five minutes in exchange for a piece of candy.
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Fair point, and plausible that I’m too much taking for granted a certain subset of development pathways. That is: I’m focused in the essay on threat models that proceed via the automation of capabilities R&D, but it’s possible that this isn’t necessary.
By “never build superintelligence” I was assuming we were talking about superintelligent AI, so if the humans in question never build superintelligent AI I’d count this path under that bucket. But as I discussed in my first post in the series, you can indeed get access to the benefits of superintelligence without building superintelligent AI in particular.
I suspect the author might already agree with all this (the existence of this logical risk, the social dynamics, the conclusion about norms/laws being needed to reduce AI risk beyond some threshold)...
Yes I think I basically agree. That is, I think it’s very possible that capabilities research is inherently easier to automate than alignment research; I am very worried about the least cautious actors pushing forward prematurely; as I tried to emphasize in the post, I think capability restraint is extremely important (and: important even if we can successfully automate alignment research); and I think that norms/laws are likely to play an important role there.
Note that conceptual research, as I’m understanding it, isn’t defined by the cognitive skills involved in the research—i.e., by whether the researchers need to have “conceptual thoughts” like “wait, is this measuring what I think it’s measuring?”. I agree that normal science involves a ton of conceptual thinking (and many “number-go-up” tasks do too). Rather, conceptual research as I’m understanding it is defined by the tools available for evaluating the research in question.[1] In particular, as I’m understanding it, cases where neither available empirical tests nor formal methods help much.
Thus, in your neurotransmitter example, it does indeed take some kind of “conceptual thinking” to come up with the thought “maybe it actually takes longer for neurotransmitters to get re-absorbed than it takes for them to clear from the cleft.” But if some AI presented us with this claim, the question is whether we could evaluate it via some kind of empirical test, which it sounds like we plausibly could. Of course, we do still need to interpret the results of these tests—e.g., to understand enough about what we’re actually trying to measure to notice that e.g. one measurement is getting at it better than another. But we’ve got rich empirical feedback loops to dialogue with.
So if we interpret “conceptual work” as conceptual thinking, I do agree that “there will be market pressure to make AI good at conceptual work, because that’s a necessary component of normal science.” And this is closely related to the comforts I discuss in section 6.1. That is: a lot of alignment research seems pretty comparable to me to the sort of science at stake in e.g. biology, physics, computer science, etc, where I think human evaluation has a decent track record (or at least, a better track record than philosophy/futurism), and where I expect a decent amount of market pressure to resolve evaluation difficulties adequately. So (modulo scheming AIs differentially messing with us in some domains vs. others), at least by the time we’re successfully automating these other forms of science, I think we should be reasonably optimistic about automating that kind of alignment research as well. But this still leaves the type of alignment research that looks more centrally like philosophy/futurism, where I think evaluation is additionally challenging, and where the human track record looks worse.
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Thus, from section 6.2.3: “Conceptual research, as I’m understanding it, is defined by the methods available for evaluating it, rather than the cognitive skills involved in producing it. For example: Einstein on relativity was clearly a giant conceptual breakthrough. But because it was evaluable via a combination of empirical predictions and formal methods, it wouldn’t count as ‘conceptual research’ in my sense.”
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I agree it’s generally better to frame in terms of object-level failure modes rather than “objections” (though: sometimes one is intentionally responding to objections that other people raise, but that you don’t buy). And I think that there is indeed a mindset difference here. That said: your comment here is about word choice. Are there substantive considerations you think that section is missing, or substantive mistakes you think it’s making?
Thanks, John. I’m going to hold off here on in-depth debate about how to choose between different ontologies in this vicinity, as I do think it’s often a complicated and not-obviously-very-useful thing to debate in the abstract, and that lots of taste is involved. I’ll flag, though, that the previous essay on paths and waystations (where I introduce this ontology in more detail) does explicitly name various of the factors you mention (along with a bunch of other not-included subtleties). E.g., re the importance of multiple actors:
Now: so far I’ve only been talking about one actor. But AI safety, famously, implicates many actors at once – actors that can have different safety ranges and capability frontiers, and that can make different development/deployment decisions. This means that even if one actor is adequately cautious, and adequately good at risk evaluation, another might not be...
And re: e.g. multidimensionality, and the difference between “can deploy safely” and “would in practice” -- from footnote 14:
Complexities I’m leaving out (or not making super salient) include: the multi-dimensionality of both the capability frontier and the safety range; the distinction between safety and elicitation; the distinction between development and deployment; the fact that even once an actor “can” develop a given type of AI capability safely, they can still choose an unsafe mode of development regardless; differing probabilities of risk (as opposed to just a single safety range); differing severities of rogue behavior (as opposed to just a single threshold for loss of control); the potential interactions between the risks created by different actors; the specific standards at stake in being “able” to do something safely; etc.
I played around with more complicated ontologies that included more of these complexities, but ended up deciding against. As ever, there are trade-offs between simplicity and subtlety, I chose a particular way of making those trade-offs, and so far I’m not regretting.
Re: who is risk-evaluating, how they’re getting the information, the specific decision-making processes: yep, the ontology doesn’t say, and I endorse that, I think trying to specify would be too much detail.
Re: why factor apart the capability frontier and the safety range—sure, they’re not independent, but it seems pretty natural to me to think of risk as increasing as frontier capabilities increase, and of our ability to make AIs safe as needing to keep up with that. Not sure I understand your alternative proposals re: “looking at their average and difference as the two degrees of freedom, or their average and difference in log space, or the danger line level and the difference, or...”, though, or how they would improve matters.
As I say, people have different tastes re: ontologies, simplifications, etc. My own taste finds this one fairly natural and useful—and I’m hoping that the use I give it in the rest of series (e.g., in classifying different waystations and strategies, in thinking about these different feedback loops, etc) can illustrate why (see also the slime analogy from the previous post for another intuition pump). But I welcome specific proposals for better overall ways of thinking about the issues in play.
Thanks, John—very open to this kind of push-back (and as I wrote in the fake thinking post, I am definitely not saying that my own thinking is free of fakeness). I do think the post (along with various other bits of the series) is at risk of being too anchored on the existing discourse. That said: do you have specific ways in which you feel like the frame in the post is losing contact with the territory?
That seems like a useful framing to me. I think the main issue is just that often, we don’t think of commitment as literally closing off choice—e.g., it’s still a “choice” to keep a promise. But if you do think of it as literally closing off choice then yes, you can avoid the violation of Guaranteed Payoffs, at least in cases where you’ve actually already made the commitment in question.
There’s also a bit more context in a footnote on the first post:
“Some content in the series is drawn/adapted from content that I’ve posted previously on LessWrong and the EA Forum, though not on my website or substack. My aim with those earlier posts was to get fast, rough versions of my thinking out there on the early side; here I’m aiming to revise, shorten, and reconsider. And some of the content in the series is wholly new.”
See also this flag at the beginning of the series you linked to:
“The content here is rough. I’m hoping, later, to revise it, along with some other work (including some of the future posts just mentioned), into something more polished and cohesive. But for various reasons, I wanted to get it out there on the earlier side.”
Hey Adam — thanks for this. I wrote about this kind of COI in the post, but your comment was a good nudge to think more seriously about my take here.
Basically, I care here about protecting two sorts of values. On the one hand, I do think the sort of COI you’re talking about is real. That is, insofar as people at AI companies who have influence over trade-offs the company makes between safety and commercial success hold equity, deciding in favor of safety will cause them to lose money — and potentially, for high-stakes decisions like dropping out of the race, a lot of money. This is true of people in safety-focused roles, but it’s true of other kinds of employees as well — and of course, especially true of leadership, who have both an outsized amount of equity and an outsized amount of influence. This sort of COI can be a source of epistemic bias (e.g. in safety evaluations of the type you’re focused on), but it can also just be a more straightforward misalignment where e.g. what’s best by the lights of an equity-holder might not be best for the world. I really don’t want my decision-making as an Anthropic employee to end up increasing existential risk from AI because of factors like this. And indeed, given that Anthropic’s stated mission is (roughly) to do what’s best for the world re: AI, in some sense it’s in the job description of every employee to make sure this doesn’t happen.[1] And just refusing to hold equity would indeed go far on this front (though: you can also get similar biases without equity — e.g., maybe you don’t want to put your cash salary at risk by making waves, pissing people off, etc). And even setting aside the reality of a given level of bias/misalignment, there can be additional benefits to it being legible to the world that this kind of bias/misalignment isn’t present (though I am currently much more concerned about the reality of the bias/misalignment at stake).
On the other hand: the amount of money at stake is enough that I don’t turn it down casually. This is partly due to donation potential. Indeed, my current guess is that (depending ofc on values and other views) many EA-ish folks should be glad on net that various employees at Anthropic (including some in leadership, and some who work on safety) didn’t refuse to take any equity in the company, despite the COIs at stake — though it will indeed depend on how much they actually end up donating, and to where. But beyond donation potential, I’m also giving weight to factors like freedom, security, flexibility in future career choices, ability to self-fund my own projects, trading-money-for-time/energy/attention, helping my family, maybe having/raising kids, option value in an uncertain world, etc. Some of these mix in impartially altruistic considerations in important ways, but just to be clear: I care about both altruistic and non-altruistic values; I give weight to both in my decision-making in general; and I am giving both weight here.
I’ll also note a different source of uncertainty for me — namely, what policy/norm would be best to promote here overall. This is a separate question from what *I* should do personally, but insofar as part of the value of e.g. refusing the equity would be to promote some particular policy/norm, it matters to me how good the relevant policy/norm is — and in some cases here, I’m not sure. I’ve put a few more comments on this in footnote.[2]
Currently, my best-guess plan for balancing these factors is to accept the equity and the corresponding COI for now (at least assuming that I stay at Anthropic long enough for the equity to vest[3]), but to keep thinking about it, learning more, and talking with colleagues and other friends/advisors as I actually dive into my role at Anthropic — and if I decide later that I should divest/give up the equity (or do something more complicated to mitigate this and other types of COI), to do that. This could be because my understanding of costs/benefits at stake in the current situation changes, or because the situation itself (e.g., my role/influence, or the AI situation more generally) changes.
Which isn’t to say that people will live up to this.
There’s one question whether it would be good (and suitably realistic) for *no* employees at Anthropic, or at any frontier AI company, to hold equity, and to be paid in cash instead (thus eliminating this source of COI in general). There’s another question whether, at the least, safety-focused employees in particular should be paid in cash, as your post here seems to suggest, while making sure that their overall *level* of compensation remains comparable to that of non-safety-focused employees. Then, in the absence of either of these policies, there’s a different question whether safety-focused employees should be paid substantially less than non-safety-focused employees — a policy which would then reduce the attractiveness of these roles relative to e.g. capabilities roles, especially for people who are somewhat interested in safety but who also care a lot about traditional financial incentives as well (I think many strong AI researchers may be in this category, and increasingly so as safety issues become more prominent). And then there’s a final question of whether, in the absence of any changes to how AI companies currently operate, there should be informal pressure/expectation on safety-focused-employees to voluntarily take very large pay cuts (equity is a large fraction of total comp) relative to non-safety-focused employees for the sake of avoiding COI (one could also distribute this pressure/expectation more evenly across all employees at AI companies — but the focus on safety evaluators in your post is more narrow).
And I’ll still have COI in the meantime due to the equity I’d get if I stayed long enough.