Doing AI Safety research for ethical reasons.
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Martín Soto
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I don’t see how Take 4 is anything other than simplicity (in the human/computational language). As you say, it’s a priori unclear whether a an agent is an instance of a human or the other way around. You say the important bit is that you are subtracting properties from a human to get an agent. But how shall we define subtraction here? In one formal language, the definition of human will indeed be a superset of that of agent… but in another one it will not. So you need to choose a language. And the natural way forward every time this comes up (many times), is to just “weigh by Turing computations in the real world” (instead of choosing a different and odd-looking Universal Turing Machine), that is, a simplicity prior.
Imo rationalists tend to underestimate the arbitrariness involved in choosing a CEV procedure (= moral deliberation in full generality).
Like you, I endorse the step of “scoping the reference class” (along with a thousand other preliminary steps). Preemptively fixing it in place helps you to the extent that the humans wouldn’t have done it by default. But if the CEV procedure is governed by a group of humans so selfish/unthoughtful as to not even converge on that by themselves, then I’m sure that there’ll be at lesat a few hundred other aspects (both more and less subtle than this one) that you and me obviously endorse, but they will not implement, and will drastically affect the outcome of the whole procedure.
In fact, it seems strikingly plausible that even among EAs, the outcome could depend drastically on seemingly-arbitrary starting conditions (like “whether we use deliberation-and-distillation procedure #194 or #635, which differ in some details”). And “drastically” means that, even though both outcomes still look somewhat kindness-shaped and friendly-shaped, one’s optimum is worth <10% to the other’s utility (or maybe, this holds for the scope-sensitive parts of their morals, since the scope-insensitive ones are trivial to satisfy).To pump related intuitions about how difficult and arbitrary moral deliberation can get, I like Demski here.
I’m sure some of people’s ignorance of these threat models comes from the reasons. But my intuition is that most of it comes from “these are vaguer threat models that seem very up in the air, and other ones seem more obviously real and more shovel-ready” (this is similar to your “Flinch”, but I think more conscious and endorsed).
Thus, I think the best way to converge on whether these threat models are real/likely/actionable is to work through as-detailed-as-possible example trajectories. Someone objects that the state will handle it? Let’s actually think through how the state might look like in 5 years! Someone objects that democracy will prevent it? Let’s actually think through the actual consequences of cheap cognitive labor in democracy!
This is analogous to what pessimists about single-single alignment have gone through. They have some abstract arguments, people don’t buy them, so they start working through them in more detail or provide example failures. I buy some parts of them, but not others. And if you did the same for this threat model, I’m uncertain how much I’d buy!Of course, the paper might have been your way of doing that. I enjoyed it, but still would have preferred more fully detailed examples, on top of the abstract arguments. You do use examples (both past and hypothetical), but they are more like “small, local examples that embody one of the abstract arguments”, rather than “an ambitious (if incomplete) and partly arbitrary picture of how these abstract arguments might actually pan out in practice”. And I would like to know the messy details of how you envision these abstract arguments coming into contact with reality. This is why I liked TASRA, and indeed I was more looking forward to an expanded, updated and more detailed version of TASRA.
Just writing a model that came to mind, partly inspired by Ryan here.
Extremely good single-single alignment should be highly analogous to “current humans becoming smarter and faster thinkers”.
If this happens at roughly the same speed for all humans, then each human is better equipped to increase their utility, but does this lead to a higher global utility? This can be seen as a race between the capability (of individual humans) to find and establish better coordination measures, and their capability to selfishly subvert these coordination measures. I do think it’s more likely than not that the former wins, but it’s not guaranteed.
Probably someone like Ryan believes most of those failures will come in the form of explicit conflict or sudden attacks. I can also imagine slower erosions of global utility, for example by safe interfaces/defenses between humans becoming unworkable slop into which most resources go.If this doesn’t happen at roughly the same speed for all humans, you also get power imbalance and its consequences. One could argue that differences in resources between humans will augment, in which case this is the only stable state.
If instead of perfect single-single alignment we get the partial (or more taxing) fix I expect, the situation degrades further. Extending the analogy, this would be the smart humans sometimes being possessed by spirits with different utilities, which not only has direct negative consequences but could also complicate coordination once it’s common knowledge.
Fantastic snapshot. I wonder (and worry) whether we’ll look back on it with similar feelings as those we have for What 2026 looks like now.
There is also no “last resort war plan” in which the president could break all of the unstable coordination failures and steer the ship.
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There are no clear plans for what to do under most conditions, e.g. there is no clear plan for when and how the military should assume control over this technology.
These sound intuitively unlikely to me, by analogy to nuclear or bio. Of course, that is not to say these protocols will be sufficient or even sane, by analogy to nuclear or bio.
This makes it really unclear what to work on.
It’s not super obvious to me that there won’t be clever ways to change local incentives / improve coordination, and successful interventions in this direction would seem incredibly high-leveraged, since they’re upstream of many of the messy and decentralized failure modes. If they do exist, they probably look not like “a simple cooridnation mechanism”, and more like “a particular actor gradually steering high-stakes conversations (through a sequence of clever actions) to bootstrap minimal agreements”. Of course, similarity to past geopolitical situations does make it seem unlikely on priors.
There is no time to get to very low-risk worlds anymore. There is only space for risk reduction along the way.
My gut has been in agreement for some time that the most cost-effective x-risk reduction now probably looks like this.
I agree with conjunctiveness, although again more optimistic about huge improvements. I mostly wanted to emphasize that I’m not sure there are structurally robust reasons (as opposed to personal whims) why huge spendings on safety won’t happen
Speaking for myself (not my coauthors), I don’t agree with your two items, because:
if your models are good enough at code analysis to increase their insecurity self-awareness, you can use them in other more standard and efficient ways to improve the dataset
doing self-critique the usual way (look over your own output) seems much more fine-grained and thus efficient than asking the model whether it “generally uses too many try-excepts”
More generally, I think behavioral self-awareness for capability evaluation is and will remain strictly worse than the obvious capability evaluation techniques.
That said, I do agree systematic inclusion of considerations about negative externalities should be a norm, and thus we should have done so. I will shortly say now that a) behavioral self-awareness seems differentially more relevant to alignment than capabilities, and b) we expected lab employees to find out about this themselves (in part because this isn’t surprising given out-of-context reasoning), and we in fact know that several lab employees did. Thus I’m pretty certain the positive externalities of building common knowledge and thinking about alignment applications are notably bigger.
Most difficulties you raise here could imo change drastically with tens of billions being injected into AI safety, especially thanks to new ideas coming out of left field that might make safety cases way more efficient. (I’m probably more optimistic about new ideas than you, partly because “it always subjectively feels like there are no big ideas left”, and AI safety is so young.)
If your government picks you as a champion and gives you amazing resources, you no longer have to worry about national competition, and that amount seems doable. You still have to worry about international competition, but will you feel so closely tied that you can’t even spare that much? My guess would be no. That said, I still don’t expect certain lab leaders to want to do this.The same is not true of security though, that’s a tough one.
See our recent work (especially section on backdoors) which opens the door to directly asking the model. Although there are obstacles like Reversal Curse and it’s unclear if it can be made to scale.
Tell me about yourself: LLMs are aware of their learned behaviors
I have two main problems with t-AGI:
A third one is a definitory problem exacerbated by test-time compute: What does it mean for an AI to succeed at task T (which takes humans X hours)? Maybe it only succeeds when an obscene amount of test-time compute is poured. It seems unavoidable to define things in terms of resources as you do
Near- and medium-term AI Control Safety Cases
Very cool! But I think there’s a crisper way to communicate the central point of this piece (or at least, a way that would have been more immediately transparent to me). Here it is:
Say you are going to use Process X to obtain a new Model. Process X can be as simple as “pre-train on this dataset”, or as complex as “use a bureaucracy of Model A to train a new LLM, then have Model B test it, then have Model C scaffold it into a control protocol, then have Model D produce some written arguments for the scaffold being safe, have a human read them, and if they reject delete everything”. Whatever Process X is, you have only two ways to obtain evidence that Process X has a particular property (like “safety”): looking a priori at the spec of Process X (without running it), or running (parts of) Process X and observing its outputs a posteriori. In the former case, you clearly need an argument for why this particular spec has the property. But in the latter case, you also need an argument for why observing those particular outputs ensures the property for this particular spec. (Pedantically speaking, this is just Kuhn’s theory-ladenness of observations.)
Of course, the above reasoning doesn’t rule out the possibility that the required arguments are pretty trivial to make. That’s why you summarize some well-known complications of automation, showing that the argument will not be trivial when Process X contains a lot of automation, and in fact it’d be simpler if we could do away with the automation.
It is also the case that the outputs observed from Process X might themselves be human-readable arguments. While this could indeed alleviate the burden of human argument-generation, we still need a previous (possibly simpler) argument for why “a human accepting those output arguments” actually ensures the property (especially given those arguments could be highly out-of-distribution for the human).
My understanding from discussions with the authors (but please correct me):
This post is less about pragmatically analyzing which particular heuristics work best for ideal or non-ideal agents in common environments (assuming a background conception of normativity), and more about the philosophical underpinnings of normativity itself.
Maybe it’s easiest if I explain what this post grows out of:
There seems to be a widespread vibe amongst rationalists that “one-boxing in Newcomb is objectively better, because you simply obtain more money, that is, you simply win”. This vibe is no coincidence, since Eliezer and Nate, in some of their writing about FDT, use language strongly implying that decision theory A is objectively better than decision theory B because it just wins more. Unfortunately, this intuitive notion of winning cannot actually be made into a philosophically valid objective metric. (In more detail, a precise definition of winning is already decision-theory-complete, so these arguments beg the question.) This point is well-known in philosophical academia, and was already succinctly explained in a post by Caspar (which the authors mention).
In the current post, the authors extend a similar philosophical critique to other widespread uses of winning, or background assumptions about rationality. For example, some people say that “winning is about not playing dominated strategies”… and the authors agree about avoiding dominated strategies, but point out that this is not too action-guiding, because it is consistent with many policies. Or also, some people say that “rationality is about implementing the heuristics that have worked well in the past, and/or you think will lead to good future performance”… but these utterances hide other philosophical assumptions, like assuming the same mechanisms are at play in the past and future, which are especially tenuous for big problems like x-risk. Thus, vague references to winning aren’t enough to completely pin down and justify behavior. Instead, we fundamentally need additional constraints or principles about normativity, what the authors call non-pragmatic principles. Of course, these principles cannot themselves be justified in terms of past performance (which would lead to circularity), so they instead need to be taken as normative axioms (just like we need ethical axioms, because ought cannot be derived from is).
GEB
Like Andrew, I don’t see strong reasons to believe that near-term loss-of-control accounts for more x-risk than medium-term multi-polar “going out with a whimper”. This is partly due to thinking oversight of near-term AI might be technically easy. I think Andrew also thought along those lines: an intelligence explosion is possible, but relatively easy to prevent if people are scared enough, and they probably will be. Although I do have lower probabilities than him, and some different views on AI conflict. Interested in your take @Daniel Kokotajlo
You know that old thing where people solipsistically optimizing for hedonism are actually less happy? (relative to people who have a more long-term goal related to the external world) You know, “Whoever seeks God always finds happiness, but whoever seeks happiness doesn’t always find God”.
My anecdotal experience says this is very true. But why?
One explanation could be in the direction of what Eliezer says here (inadvertently rewarding your brain for suboptimal behavior will get you depressed):
Someone with a goal has an easier time getting out of local minima, because it is very obvious those local minima are suboptimal for the goal. For example, you get out of bed even when the bed feels nice. Whenever the ocasional micro-breakdown happens (like feeling a bit down), you power through for your goal anyway (micro-dosing suffering as a consequence), so your brain learns that micro-breakdowns only ever lead to bad immediate sensations and fixes them fast.
Someone whose only objective is the satisfaction of their own appetites and desires has a harder time reasoning themselves out of local optima. Sure, getting out of bed allows me to do stuff that I like. But those feel distant now, and the bed now feels comparably nice… You are now comparing apples to apples (unlike someone with an external goal), and sometimes you might choose the local optimum. When the ocasional micro-breakdown happens, you are more willing to try to soften the blow and take care of the present sensation (instead of getting over the bump quickly), which rewards in the wrong direction.
Another possibly related dynamic: When your objective is satisfying your desires, you pay more conscious attention to your desires, and this probably creates more desires, leading to more unsatisfied desires (which is way more important than the amount of satisfied desires?).
hahah yeah but the only point here is: it’s easier to credibly commit to a threat if executing the threat is cheap for you. And this is simply not too interesting a decision-theoretic point, just one more obvious pragmatic consideration to throw into the bag. The story even makes it sound like “Vader will always be in a better position”, or “it’s obvious that Leia shouldn’t give in to Tarkin but should give in to Vader”, and that’s not true. Even though Tarkin loses more from executing the threat than Vader, the only thing that matters for Leia is how credible the threat is. So if Tarkin had any additional way to make his commitment credible (like program the computer to destroy Alderaan if the base location is not revealed), then there would be no difference between Tarkin and Vader. The fact that “Tarkin might constantly reconsider his decision even after claiming to commit” seems like a contingent state of affairs of human brains (or certain human brains in certain situations), not something important in the grander scheme of decision theory.
It’s unclear what the optimal amount of thinking per step is. My initial guess would have been that letting Claude think for a whole paragraph before each single action (rather than only each 10 actions, or whenever it’s in a match, or whatever) scores slightly better than letting it think more (sequentially). But I guess this might work better if it’s what the streamer is using after some iteration.
The story for parallel checks could be different though. My guess would be going all out and letting Claude generate the paragraph 5 times and then generate 5 more parallel paragraphs about whether it has gotten something wrong, and then having a lower-context version of Claude decide whether there are any important disagreements, and if not just majority-vote, would improve robustness problems (like “I close a goal before actually achieving it”). But maybe this adds too much bloat and opportunities for mistakes, or makes some mistakes better but others way worse.