I’m not sure. I was trying to disagree with your top level comment :P
FWICT, both of your points are actually responses to be point (3).
RE “re: #2”, see: https://en.wikipedia.org/wiki/Value_of_information#Characteristics
RE “re: #3”, my point was that it doesn’t seem like VoI is the correct way for one agent to think about informing ANOTHER agent. You could just look at the change in expected utility for the receiver after updating on some information, but I don’t like that way of defining it.
I think it is rivalrous.
Xrisk mitigation isn’t the resource; risky behavior is the resource. If you engage in more risky behavior, then I can’t engage in as much risky behavior without pushing us over into a socially unacceptable level of total risky behavior.
If there is a cost to reducing Xrisk (which I think is a reasonable assumption), then there will be an incentive to defect, i.e. to underinvest in reducing Xrisk. There’s still *some* incentive to prevent Xrisk, but to some people everyone dying is not much worse than just them dying.
1) Yep, independence.
2) Seems right as well.
3) I think it’s important to consider “risk per second”, because
(i) I think many AI systems could eventually become dangerous, just not on reasonable time-scales.
(ii) I think we might want to run AI systems which have the potential to become dangerous for limited periods of time.
(iii) If most of the risk is far in the future, we can hope to become more prepared in the meanwhile
Whether or not this happens depends on the learning algorithm. Let’s assume an IID setting. Then an algorithm that evaluates many random parameter settings and choses the one that gives the best performance would have this effect. But a gradient-based learning algorithm wouldn’t necessarily, since it only aims to improve its predictions locally (so what you say in the ETA is more accurate, **in this case**, I think).
Also, I just wanted to mention that Stuart Armstrong’s paper “Good and safe uses of AI oracles” discusses self-fulfilling prophecies as well; Stuart provides a way of training a predictor that won’t be victim to such effects (just don’t reveal its predictions when training). But then it also fails to account for the effect its predictions actually have, which can be a source of irreducible error… The example is a (future) stock-price predictor: making its predictions public makes them self-refuting to some extent, as they influence market actors decisions.
I dunno… I think describing them as a tragedy of the commons can help people understand why the problems are challenging and deserving of attention.
RE Sarah: Longer timelines don’t change the picture that much, in my mind. I don’t find this article to be addressing the core concerns. Can you recommend one that’s more focused on “why AI-Xrisk isn’t the most important thing in the world”?
RE Robin Hanson: I don’t really know much of what he thinks, but IIRC his “urgency of AI depends on FOOM” was not compelling.
What I’ve noticed is that critics are often working from very different starting points, e.g. being unwilling to estimate probabilities of future events, using common-sense rather than consequentialist ethics, etc.
IMO, VoI is also not a sufficient criteria for defining manipulation… I’ll list a few problems I have with it, OTTMH:
1) It seems to reduce it to “providing misinformation, or providing information to another agent that is not maximally/sufficiently useful for them (in terms of their expected utility)”. An example (due to Mati Roy) of why this doesn’t seem to match our intuition is: what if I tell someone something true and informative that serves (only) to make them sadder? That doesn’t really seem like manipulation (although you could make a case for it).
2) I don’t like the “maximally/sufficiently” part; maybe my intuition is misleading, but manipulation seems like a qualitative thing to me. Maybe we should just constrain VoI to be positive?
3) Actually, it seems weird to talk about VoI here; VoI is prospective and subjective… it treats an agent’s beliefs as real and asks how much value they should expect to get from samples or perfect knowledge, assuming these samples or the ground truth would be distributed according to their beliefs; this makes VoI strictly non-negative. But when we’re considering whether to inform an agent of something, we might recognize that certain information we’d provide would actually be net negative (see my top level comment for an example). Not sure what to make of that ATM...
Agree, good point. I’d say it’s aleatoric risk is necessary to produce compounding, but not sufficient, but maybe I’m still looking at this the wrong way.
Haha no not at all ;)
I’m not actually trying to recruit people to work on that, just trying to make people aware of the idea of doing such projects. I’d suggest it to pretty much anyone who wants to work on AI-Xrisk without diving deep into math or ML.
So I want to emphasize that I’m only saying it’s *plausible* that *there exists* a specification of “manipulation”. This is my default position on all human concepts. I also think it’s plausible that there does not exist such a specification, or that the specification is too complex to grok, or that there end up being multiple conflicting notions we conflate under the heading of “manipulation”. See this post for more.
Overall, I understand and appreciate the issues you’re raising, but I think all this post does is show that naive attempts to specify “manipulation” fail; I think it’s quite difficult to argue compellingly that no such specification exists ;)
“It seems that the only difference between manipulation and explanation is whether we end up with a better understanding of the situation at the end. And measuring understanding is very subtle.”
^ Actually, I think “ending up with a better understanding” (in the sense I’m reading it)is probably not sufficient to rule out manipulation; what I mean is that I can do something which actually improves your model of the world, but leads you to follow a policy with worse expected returns. A simple example would be if you are doing Bayesian updating and your prior over returns for two bandit arms is P(r|a_1) = N(1,1), P(r|a_2) = N(2, 1), while the true returns are 1⁄2 and 2⁄3 (respectively). So your current estimates are optimistic, but they are ordered correctly, and so induce the optimal (greedy) policy.
Now if I give you a bunch of observations of a_2, I will be giving you true information, that will lead you to learn, correctly and with high confidence, that the expected reward for a_2 is ~2/3, improving your model of the world. But since you haven’t updated your estimate for a_1, you will now prefer a_1 to a_2 (if acting greedily), which is suboptimal. So overall I’ve informed you with true information, but disadvantaged you nonetheless. I’d argue that if I did this intentionally, it should count as a form of manipulation.
I don’t think I’d put it that way (although I’m not saying it’s inaccurate). See my comments RE “safety via myopia” and “inner optimizers”.
Yes, maybe? Elaborating...
I’m not sure how well this fits into the category of “inner optimizers”; I’m still organizing my thoughts on that (aiming to finish doing so within the week...). I’m also not sure that people are thinking about inner optimizers in the right way.
Also, note that the thing being imitated doesn’t have to be a human.
OTTMH, I’d say:
This seems more general in the sense that it isn’t some “subprocess” of the whole system that becomes a dangerous planning process.
This seems more specific in the sense that the boldest argument for inner optimizers is, I think, that they should appear in effectively any optimization problem when there’s enough optimization pressure.
See the clarifying note in the OP. I don’t think this is about imitating humans, per se.
The more general framing I’d use is WRT “safety via myopia” (something I’ve been working on in the past year). There is an intuition that supervised learning (e.g. via SGD as is common practice in current ML) is quite safe, because it doesn’t have any built-in incentive to influence the world (resulting in instrumental goals); it just seeks to yield good performance on the training data, learning in a myopic sense to improve it’s performance on the present input. I think this intuition has some validity, but also might lead to a false sense of confidence that such systems are safe, when in fact they may end up behaving as if they *do* seek to influence the world, depending on the task they are trained on (ETA: and other details of the learning algorithm, e.g. outer-loop optimization and model choice).
Aha, OK. So I either misunderstand or disagree with that.
I think SHF (at least most examples) have the human as “CEO” with AIs as “advisers”, and thus the human can chose to ignore all of the advice and make the decision unaided.