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I mostly agree with this post’s characterization of my position.

Places where I disagree with your characterization of my view:

  • I don’t assume that powerful actors can’t coordinate, and I don’t think that assumption is necessary. I would describe the situation as: over the course of time influence will necessarily shift sometimes due to forces we endorse—like deliberation or reconciliation—and sometimes due to forces we don’t endorse—like compatibility with an uncontrolled expansion strategy. Even if powerful actors can form perfectly-coordinated coalitions, a “weak” actor positioned to benefit from competitive expansion would simply decline to participate in that coalition unless offered extremely generous terms. I don’t see how the situation changes unless the strong actors use force. I do think that’s reasonably likely, I would more describe alignment as a first line of defense or a complement to approaches like regulation. I generally agree that good coordination can substitute for technical weakness.
  • I don’t think I rely on or even implicitly use a unidimensional model of power. I do use a concept like “total wealth” or “total influence,” which seems almost but not quite tautologically well-defined (as the output of a competitive/bargaining dynamic) and in particular is compatible with knowledge vs. resources vs. whatever. Being “competitive” seems to make sense in very complex worlds, when I say something like “win in a fistfight” I mean to quantify over all possible fistfights (including science, economic competition, persuasion, war, etc. etc.).
  • I have strong intuitions about my approach being workable, and either the approach will succeed or I at least will feel that I have learned something substantial. I expect many more pivots and adjustments to be necessary, but don’t expect to get stuck with plausibility arguments that are nearly as weak as the current arguments.

Place where I disagree with your view:

  • I agree that there are many drivers of AI other than learning. However, I think that learning is (a) currently the dominant component of powerful AI and so both more urgent and easier to study, (b) poses a much harder safety problems than other AI techniques under discussion, and (c) appears to be the “hard part” of analyzing procedures like evolution, fine-tuning brain-inspired architectures, or analyzing reasoning (it’s where I run into a wall when trying to analyze these other alternatives).
  • I think that all of capability amplification, informed oversight, and red teams / adversarial training are amenable to theoretical analysis with realistic amounts of philosophical progress. For example, I think that it will be possible to analyze these schemes using only abstractions like optimization power, without digging into models of bounded rationality at all. I may have understated my optimism on this (for capability amplification in particular) in our last discussion—I do believe that we won’t have a formal argument, but I think we should aim for an argument that is based on plausible empirical assumptions plus very good evidence for those assumptions.
  • Altruism as in “concern for future generations” does not fall out of coordination strategies, and seems more like a spandrel to me. But I do agree that many parts of altruism are more like coordination and this gives some prima facie reason for optimism about getting to some Pareto frontier.

Take-aways that I agree with:

  • We will need to have a better understanding of deliberation in order to be confident in any alignment scheme. (I prefer a more surgical approach than most MIRI folk, trying to figure exactly what we need to know rather than trying to have an expansive understanding of what good reasoning looks like.)
  • It is valuable for people to step back from particular approaches to alignment and to try to form a clearer understanding of the problem, explore completely new approaches, etc.


by Wei Dai 150 days ago | link

Given that ALBA was not meant to be a realistic aligned AI design in and of itself, but just a way to get insights into how to build a realistic aligned AI (which I hadn’t entirely understood until now), I wonder if it makes sense to try to nail down all the details and arguments for it before checking to see if you generated any such insights. If we assume that aligned learning roughly looks like ALBA, what does that tell you about what a more realistic aligned AI looks like? It seems worth asking this, in case you, for example, spend a lot of time figuring out exactly how capability amplification could work, and then it ends up that capability amplification isn’t even used in the final aligned AI design, or in case designing aligned AI out of individual AI components doesn’t actually give you much insight into how to design more realistic aligned AI.

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by Wei Dai 137 days ago | link

We will need to have a better understanding of deliberation in order to be confident in any alignment scheme. (I prefer a more surgical approach than most MIRI folk, trying to figure exactly what we need to know rather than trying to have an expansive understanding of what good reasoning looks like.)

I can see two kinds of understanding of deliberation that could help us achieve confidence in alignment schemes:

  1. white-box understanding, where we understand what good reasoning is, in each of philosophical/mathematical/algorithmic levels, i.e., we can formally define what ideal reasoning is, have a clear understanding of why the formal definition is correct/normative and why some specific algorithm is a good approximation of the mathematical ideal.
  2. black box understanding, where we define “ideal reasoning” as the distribution of outcomes induced by placing a group of humans in an ideally safe and supportive environment and given a question to deliberate over, and show that a specific practical implementation of deliberation (e.g., meta-execution) induces a distribution that is acceptably similar to the ideal.

(Note that I’m using the same white-box/black-box terminology as here, but the meaning is a bit different since I’m now applying the terms to understandings of deliberation as opposed to implementations of deliberation.)

The problem with 2, which I see you as implicitly advocating (since I don’t see how else you hope to eventually be confident in your alignment scheme), is that I have a low prior for any specific implementation of deliberation (such as meta-execution) producing distributions of outcomes acceptably close to the ideal (unless it’s just a very close direct approximation of the ideal process like using a group of highly accurate uploads), and I don’t currently see what kind of arguments or evidence we can hope to produce in a relevant timeframe that would make me update enough to become confident. (Aside from something like achieving a white-box understanding of deliberation and then concluding that both the black-box definition of “ideal reasoning” and the actual implementation would be able to approximate the white-box definition of “ideal reasoning”, but presumably that’s not what you have in mind.)

Perhaps you think empirical work would help, but even if you’re able to gather a lot of data on what black-box ideal reasoning eventually produces (which you can’t for certain types of reasoning, e.g., philosophical reasoning) and are able to compare that with the AI alignment scheme, how would you rule out the possibility of edge cases where the two don’t match?

Another possibility is that you think black-box ideal reasoning would first decide to follow a set of rules and procedures before doing any further deliberation, and that would make it easier for an AI alignment scheme to approximate the ideal. But A) the group of humans would likely spend a lot of time exploring different alternative for how to do further deliberation, and B) whatever rules/procedures they end up adopting would likely include ways to back out of those rules and/or adopt new rules. For 2, you would need to predict with justifiably high confidence what rules/procedures they eventually converge upon (if they in fact converge instead of, e.g., diverge depending on what question they face or which humans we start with), and again I don’t see how you hope to do that, within the time we likely have available.

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by Wei Dai 136 days ago | link

I want to add that I think meta-execution, in particular, will have problems with deliberation for the same reason that it will have problems with learning: when you hear an argument or explanation (like when you learn), your mind is changed in ways that are hard or impossible to articulate. If every 10 minutes (or 1 day, or what have you) you throw away the part of what your brain does that it can’t write down, it seems highly plausible that in many cases you won’t be able to reproduce what the brain does over a longer period of time, especially if you’re trying to match its natural trajectory, as opposed to trying to hit some objectively measurable benchmark.

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by Paul Christiano 135 days ago | link

An aligned AI doesn’t need to share human preferences-on-reflection. It just needs to (a) be competent, and (b) help humans remain in control of the AI while carrying out whatever reflective process they prefer (including exploration of different approaches, reconciliation of different perspectives, etc.).

So all I’m hoping is to show something about how deliberation can (a) be smarter, while (b) avoiding introducing “bad” (incorrigible?) optimization. I don’t think this requires either your #1 or #2.

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by Wei Dai 134 days ago | link

So all I’m hoping is to show something about how deliberation can (a) be smarter, while (b) avoiding introducing “bad” (incorrigible?) optimization. I don’t think this requires either your #1 or #2.

If you try to formalize (a) and (b), what does that look like, and how would you reach the conclusion that an AI actually has (a) and (b)? My #1 and #2 are things I can come up with when I try to think how we might come to have justified confidence that an AI has good reasoning, and I’m not seeing what other solutions pop up if we say an aligned AI doesn’t need to share human preferences-on-reflection but only needs to be competent and help humans remain in control.

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by Paul Christiano 133 days ago | link

Competence can be an empirical claim, so (a) seems much more straightforward.

Is there some sense in which “argue that a process is normatively correct” is more of a solution than “argue that a process doesn’t optimize for something ‘bad’”? I agree that both of of the properties are hard to formalize or achieve, the second one currently looks easier to me (and may even be a subproblem of the first one—e.g. my current best guess is that good reasoners need a cognitive immune system).

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by Wei Dai 133 days ago | link

Competence can be an empirical claim, so (a) seems much more straightforward.

Once again I’m having trouble seeing something that you think is straightforward. If an AI can’t determine my values-upon-reflection, that seems like a kind of incompetence. If it can’t do that, it seems likely there are other things it can’t do. Perhaps you can define “competence” in a way that excludes that class of things and argue that’s good enough, but I’m not sure how you’d do that.

Is there some sense in which “argue that a process is normatively correct” is more of a solution than “argue that a process doesn’t optimize for something ‘bad’”?

I think we might eventually be able to argue that a process is normatively correct by understanding it at each of philosophical/mathematical/algorithmic levels, but that kind of white-box understanding does not seem possible if your process incorporates an opaque object (e.g., a machine-learned imitation of human behavior), so I think the best you can hope to achieve in that case is a black-box understanding where you show that your process induces the same (or close enough) distribution of outcomes as a group of humans in an ideal environment.

If your “argue that a process doesn’t optimize for something ‘bad’” is meant to be analogous to my white-box understanding, it seems similarly inapplicable due to presence of the opaque object in your process. If it’s meant to be analogous to my black-box understanding, I don’t see what the analogy is. In other words, what are you hoping to show instead of “induces the same (or close enough) distribution of outcomes as a group of humans in an ideal environment”?

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by Paul Christiano 133 days ago | link

but that kind of white-box understanding does not seem possible if your process incorporates an opaque object (e.g., a machine-learned imitation of human behavior), so I think the best you can hope to achieve in that case is a black-box understanding where you show that your process induces the same (or close enough) distribution of outcomes as a group of humans in an ideal environment

Suppose I use normatively correct reasoning, but I also use a toaster designed by a normal engineer. The engineer is less capable than I am in every respect, and I watched them design and build the toaster to verify that they didn’t do anything tricky. Then I verified that the toaster does seem to toast toast. But I have no philosophical or mathematical understanding of the toaster-design-process. Your claim seems to be that there are no rational grounds for accepting use of the toaster, other than to argue that accepting it doesn’t change the distribution of outcomes (which probably isn’t true, since it e.g. slightly changes the relative influence of different internal drives by changing what food I eat). Is that right?

What if they designed a SAT solver for me? Or wrote a relativity textbook? Do I need to be sure that nothing like that happens in my deliberative process, in order to have confidence in it?

If those cases don’t seem analogous, can you be more clear about what you mean by “opaque,” or what quantitative factors make an opaque object problematic? So far your argument doesn’t seem rely on any properties of the opaque object at all.

(This case isn’t especially analogous to the deliberative process I’m interested in. I’m bringing it up because I don’t think I yet understand your intuitive dichotomy.)

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by Wei Dai 131 days ago | link

When you wrote “suppose I use normatively correct reasoning” did you mean suppose you, Paul, use normatively correct reasoning, or suppose you are an AI who uses normatively correct reasoning? I’ll assume the latter for now.

Generally, the AI would use its current reasoning process to decide whether or not to incorporate new objects into itself. I’m not sure what that reasoning process will do exactly, but presumably it would involve something like looking for and considering proofs/arguments/evidence to the effect that incorporating the new object in some specified way will allow it to retain its normatively correct status, or the distribution of outcomes will be sufficiently unchanged.

If an AI uses normatively correct reasoning, the toaster shouldn’t change the distribution of outcomes, since letting food influence its reasoning process is obviously not a normative thing to do, and it should be easy to show that there is no influence. For the SAT solver, the AI should be able to argue that it’s safe to use it for certain purposes, because it can verify the answer that the solver gives, and for the relativity textbook, it may be able to directly verify that the textbook doesn’t contain anything that can manipulate or bias its outputs.

I guess by “opaque” I meant a complex object that wasn’t designed to be easily reasoned about, so it’s very hard to determine whether it has a given property that would be relevant to showing that it can be used safely as part of an AI’s reasoning process. For example a typical microprocessor is an opaque object because it may come with hard to detect flaws and backdoors, whereas a microprocessor designed to be provably correct would be a transparent object.

(Does that help?)

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by Paul Christiano 131 days ago | link

Suppose that I, Paul, use a toaster or SAT solver or math textbook.

I’m happy to drop the normatively correct reasoning assumption if the counterfactual begs the question. The important points are:

  • I’m happy trusting future Paul’s reasoning (in particular I do not consider it a top altruistic priority to find a way to avoid trusting future Paul’s reasoning)
  • That remains true even though future Paul would happily use an opaque toaster or textbook (under the conditions described).

I’m not convinced that any of your arguments would be sufficient to trust a toaster / textbook / SAT solver:

and it should be easy to show that there is no influence

Having new memories will by default change the output of deliberation, won’t it?

For the SAT solver, the AI should be able to argue that it’s safe to use it for certain purposes, because it can verify the answer that the solver gives

Satisfying instances produced by an arbitrarily powerful adversary don’t seem safe for anything.

and for the relativity textbook, it may be able to directly verify that the textbook doesn’t contain anything that can manipulate or bias its outputs

I don’t see how this would fit into your framework, without expanding it far enough that it could contain the kind of argument I’m gesturing at (by taking bad === “manipulating or biasing its outputs.”)

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by Wei Dai 130 days ago | link

If we’re talking about you, Paul, then what’s different is that since you don’t have a good understanding of what normatively correct reasoning is, you can only use black-box type reasoning to conclude that certain things are safe to do. We’d happily use the opaque toaster or textbook because we have fairly strong empirical evidence that doing so doesn’t change the distribution of outcomes much. Using a toaster might change a particular outcome vs not using it, but there seems to be enough stochasticity in a human deliberation process that it wouldn’t make a significant difference to the overall distribution of outcomes. With a textbook, you reason that with enough time you’d reproduce its contents yourself, and whatever actual differences between reading the textbook and figuring out relativity by yourself is again lost in the overall noise of the deliberative process. (We have fairly strong empirical evidence that reading such a textbook written by another human is unlikely to derail our deliberative process in a way that’s not eventually recoverable.)

One reply to this might be that we can hope to gather an amount of empirical evidence about meta-execution that would be comparable to the evidence we have about toasters and textbooks. I guess my concern there is that we’ll need much stronger assurances if we’re going to face other superintelligent AIs in our environment. For example that textbook might contain subtle mistakes that cause you to reason incorrectly about certain questions (analogous to edge case questions where your meta-execution would give significantly different answers than your reflective equilibrium), but there is no one in your current environment who can exploit such errors.

ETA: Another reason to be worried is that, compared to humans using things produced by other humans, it seems reasonable to suspect (have a high prior) that meta-execution’s long run safety can’t be extrapolated well from what it does in the short term, since meta-execution is explicitly built out of a component that emphasizes imitation of short-term human behavior while throwing away internal changes that might be very relevant to long-run outcomes. (Again this may be missing your point about not needing to reproduce values-upon-reflection but I just don’t understand how your alternative approach to understanding deliberation would work if you tried to formalize it.)

Satisfying instances produced by an arbitrarily powerful adversary don’t seem safe for anything.

Not sure if this is still relevant to the current interpretation of your question, but couldn’t you use it to safely break encryption schemes, at least?

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