David Krueger

David Krueger's Comments

Let's talk about "Convergent Rationality"
Sure, but within AI, intelligence is the main feature that we're trying very hard to increase in our systems that would plausibly let the systems we build outcompete us. We aren't trying to make AI systems that replicate as fast as possible. So it seems like the main thing to be worried about is intelligence.

Blaise Agüera y Arcas gave a keynote at this NeurIPS pushing ALife (motivated by specification problems, weirdly enough...: https://neurips.cc/Conferences/2019/Schedule?showEvent=15487).

The talk recording: https://slideslive.com/38921748/social-intelligence. I recommend it.


Let's talk about "Convergent Rationality"
With 0, the AI never does anything and so is basically a rock

I'm trying to point at "myopic RL", which does, in fact, do things.

You might object that all of these can be made state-dependent, but you can make your example state-dependent by including the current time in the state.

I do object, and still object, since I don't think we can realistically include the current time in the state. What we can include is: an impression of what the current time is, based on past and current observations. There's an epistemic/indexical problem here you're ignoring.

I'm not an expert on AIXI, but my impression from talking to AIXI researchers and looking at their papers is: finite-horizon variants of AIXI have this "problem" of time-inconsistent preferences, despite conditioning on the entire history (which basically provides an encoding of time). So I think the problem I'm referring to exists regardless.


Let's talk about "Convergent Rationality"
Sure, but within AI, intelligence is the main feature that we're trying very hard to increase in our systems that would plausibly let the systems we build outcompete us. We aren't trying to make AI systems that replicate as fast as possible. So it seems like the main thing to be worried about is intelligence.

I think I was maybe trying to convey too much of my high-level views here. What's maybe more relevant and persuasive here is this line of thought:

  • Intelligence is very multi-faceted
  • An AI that is super-intelligent in a large number (but small fraction) of the facets of intelligence could strategically outmanuver humans
  • Returning to the original point: such as AI could also be significantly less "rational" than humans

Also, nitpicking a bit: to a large extent, society is trying to make systems that are as competitive as possible at narrow, profitable tasks. There are incentives for excellence in many domains. FWIW, I'm somewhat concerned about replicators in practice, e.g. because I think open-ended AI systems operating in the real-world might create replicators accidentally/indifferently, and we might not notice fast enough.

My main opposition to this is that it's not actionable

I think the main take-away from these concerns is to realize that there are extra risk factors that are hard to anticipate and for which we might not have good detection mechanisms. This should increase pessimism/paranoia, especially (IMO) regarding "benign" systems.

Idk, if it's superintelligent, that system sounds both rational and competently goal-directed to me.

(non-hypothetical Q): What about if it has a horizon of 10^-8s? Or 0?

I'm leaning on "we're confused about what rationality means" here, and specifically, I believe time-inconsistent preferences are something that many would say seem irrational (prima face). But


Let's talk about "Convergent Rationality"
Perhaps you mean grey-goo type scenarios where we wouldn't call the replicator "intelligent", but it's nonetheless a good replicator? Are you worried about AI systems of that form? Why?

Yes, I'm worried about systems of that form (in some sense). The reason is: I think intelligence is just one salient feature of what makes a life-form or individual able to out-compete others. I think intelligence, and fitness even more so, are multifaceted characteristics. And there are probably many possible AIs with different profiles of cognitive and physical capabilities that would pose an Xrisk for humans.

For instance, any appreciable quantity of a *hypothetical* grey goo that could use any matter on earth to replicate (i.e. duplicate itself) once per minute would almost certainly consume the earth in less than one day (I guess modulo some important problems around transportation and/or its initial distribution over the earth, but you probably get the point).

More realistically, it seems likely that we will have AI systems that have some significant flaws but are highly competent at strategically relevant cognitive skills, able to think much faster than humans, and have very different (probably larger but a bit more limited) arrays of sensors and actuators than humans, which may pose some Xrisk.

The point is just that intelligence and rationality are import traits for Xrisk, but we should certainly not make the mistake of believing one/either/both are the only traits that matter. And we should also recognize that they are both abstractions and simplifications that we believe are often useful but rarely, if ever, sufficient for thorough and effective reasoning about AI-Xrisk.

Sure, I more meant competently goal-directed.

This is still, I think, not the important distinction. By "significantly restricted", I don't necessarily mean that it is limiting performance below a level of "competence". It could be highly competent, super-human, etc., but still be significantly restricted.

Maybe a good example (although maybe departing from the "restricted hypothesis space" type of example) would be an AI system that has a finite horizon of 1,000,000 years, but no other restrictions. There may be a sense in which this system is irrational (e.g. having time-inconsistent preferences), but it may still be extremely competently goal-directed.


Let's talk about "Convergent Rationality"

At a meta-level: this post might be a bit to under-developed to be worth trying to summarize in the newsletter; I'm not sure.

RE the summary:

  • I wouldn't say I'm introducing a single thesis here; I think there are probable a few versions that should be pulled apart, and I haven't done that work yet (nor has anyone else, FWICT).
  • I think the use of "must" in your summary is too strong. I would phrase it more like "unbounded increases the capabilities of an AI system drive an unbounded increase in the agenty-ness or rationality of the system".
  • The purported failure of capability control I'm imagining isn't because the AI subverts capability controls; that would be putting the cart before the horse. The idea is that an AI that doesn't conceptualize itself as an agent would begin to do so, and that very event is a failure of a form of "capability control", specifically the "don't build an agent" form. (N.B.: some people have been confused by my calling that a form of capability control...)
  • My point is stronger than this: "we could still have AI systems that are far more 'rational' than us, even if they still have some biases that they do not seek to correct, and this could still lead to x-risk." I claim that a system doesn't need to be very "rational" at all in order to pose significant Xrisk. It can just be a very powerful replicator/optimizer.

RE the opinion:

  • See my edit to the comment about "convergent goal-directedness", we might have some misunderstanding... To clarify my position a bit:
    • I think goal-directedness seems like a likely component of rationality, but we're still working on deconfusing rationality itself, so it's hard to say for sure
    • I think it's only a component and not the same thing, since I would consider an RL agent that has a significantly restricted hypothesis space to be goal-directed, but probably not highly rational. CRT would predict that (given a sufficient amount of compute and interaction) such an agent would have a tendency to expand its (effective) hypothesis space to address inadequacies. This might happen via recruiting resources in the environment and eventually engaging in self-modification.
  • I think CRT is not well-formulated or specified enough (yet) to be something that one can agree/disagree with, without being a bit more specific.
What I talk about when I talk about AI x-risk: 3 core claims I want machine learning researchers to address.

I'm definitely interested in hearing other ways of splitting it up! This is one of the points of making this post. I'm also interested in what you think of the ways I've done the breakdown! Since you proposed an alternative, I guess you might have some thoughts on why it could be better :)

I see your points as being directed more at increasing ML researchers respect for AI x-risk work and their likelihood of doing relevant work. Maybe that should in fact be the goal. It seems to be a more common goal.

I would describe my goal (with this post, at least, and probably with most conversations I have with ML people about Xrisk) as something more like: "get them to understand the AI safety mindset, and where I'm coming from; get them to really think about the problem and engage with it". I expect a lot of people here would reason in a very narrow and myopic consequentialist way that this is not as good a goal, but I'm unconvinced.

A list of good heuristics that the case for AI x-risk fails

Another important improvement I should make: rephrase these to have the type signature of "heuristic"!

A list of good heuristics that the case for AI x-risk fails

Oh sure, in some special cases. I don't this this experience was particularly representative.

A list of good heuristics that the case for AI x-risk fails

Yeah I've had conversations with people who shot down a long list of concerned experts, e.g.:

  • Stuart Russell is GOFAI ==> out-of-touch
  • Shane Legg doesn't do DL, does he even do research? ==> out-of-touch
  • Ilya Sutskever (and everyone at OpenAI) is crazy, they think AGI is 5 years away ==> out-of-touch
  • Anyone at DeepMind is just marketing their B.S. "AGI" story or drank the koolaid ==> out-of-touch

But then, even the big 5 of deep learning have all said things that can be used to support the case....

So it kind of seems like there should be a compendium of quotes somewhere, or something.

Clarifying some key hypotheses in AI alignment

Nice chart!

A few questions and comments:

  • Why the arrow from "agentive AI" to "humans are economically outcompeted"? The explanation makes it sounds like it should point to "target loading fails"??
  • Suggestion: make the blue boxes without parents more apparent? e.g. a different shade of blue? Or all sitting above the other ones? (e.g. "broad basin of corrigibility" could be moved up and left).
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