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.
This is not what I meant by “the same values”, but the comment points towards an interesting point.
When I say “the same values”, I mean the same utility function, as a function over the state of the world (and the states of “R is having sex” and “H is having sex” are different).
The interesting point is that states need to be inferred from observations, and it seems like there are some fundamentally hard issues around doing that in a satisfying way.
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.
Hey, David here!
Just writing to give some context… The point of this session was to discuss an issue I see with “super-human feedback (SHF)” schemes (e.g. debate, amplification, recursive reward modelling) that use helper AIs to inform human judgments. I guess there was more of an inferential gap going into the session than I expected, so for background: let’s consider the complexity theory viewpoint in feedback (as discussed in section 2.2 of “AI safety via debate”). This implicitly assumes that we have access to a trusted (e.g. human) decision making process (TDMP), sweeping the issues that Stuart mentions under the rug.
Under this view, the goal of SHF is to efficiently emulate the TDMP, accelerating the decision-making. For example, we’d like an agent trained with SHF to be able to quickly (e.g. in a matter of seconds) make decisions that would take the TDMP billions of years to decide. But we don’t aim to change the decisions.
Now, the issue I mentioned is: there doesn’t seem to be any way to evaluate whether the SHF-trained agent is faithfully emulating the TDMP’s decisions on such problems. It seems like, naively, the best we can do is train on problems where the TDMP can make decisions quickly, so that we can use its decisions as ground truth; then we just hope that it generalizes appropriately to the decisions that take TDMP billions of years. And the point of the session was to see if people have ideas for how to do less naive experiments that would allow us to increase our confidence that a SHF-scheme would yield safe generalization to these more difficult decisions.
Imagine there are 2 copies of me, A and B. A makes a decision with some helper AIs, and independently, B makes a decision without their help. A and B make different decisions. Who do we trust? I’m more ready to trust B, since I’m worried about the helper AIs having an undesirable influence on A’s decision-making.
...So questions of how to define human preferences or values seem mostly orthogonal to this question, which is why I want to assume them away. However, our discussion did make me consider more that I was making an implicit assumption (and this seems hard to avoid), that there was some idealized decision-making process that is assumed to be “what we want”. I’m relatively comfortable with trusting idealized versions of “behavioral cloning/imitation/supervised learning” (P) or “(myopic) reinforcement learning/preference learning” (NP), compared with the SHF-schemes (PSPACE).
One insight I gleaned from our discussion is the usefulness of disentangling:
an idealized process for *defining* “what we want” (HCH was mentioned as potentially a better model of this than “a single human given as long as they want to think about the decision” (which was what I proposed using, for the purposes of the discussion)).
a means of *approximating* that definition.
From this perspective, the discussion topic was: how can we gain empirical evidence for/against this question: “Assuming that the output of a human’s indefinite deliberation is a good definition of ‘what they want’, do SHF-schemes do a good/safe job of approximating that?”
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.
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.
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).
I don’t think I was very clear; let me try to explain.
I mean different things by “intentions” and “terminal values” (and I think you do too?)
By “terminal values” I’m thinking of something like a reward function. If we literally just program an AI to have a particular reward function, then we know that it’s terminal values are whatever that reward function expresses.
Whereas “trying to do what H wants it to do” I think encompasses a broader range of things, such as when R has uncertainty about the reward function, but “wants to learn the right one”, or really just any case where R could reasonably be described as “trying to do what H wants it to do”.
Talking about a “black box system” was probably a red herring.
Comparing with articles from a year ago, e.g. http://www.popsci.com/bill-gates-fears-ai-ai-researchers-know-better, this represents significant progress.
I’m a PhD student in Yoshua’s lab. I’ve spoken with him about this issue several times, and he has moved on this issue, as have Yann and Andrew. From my perspective following this issue, there was tremendous progress in the ML community’s attitude towards Xrisk.
I’m quite optimistic that such progress with continue, although pessimistic that it will be fast enough and that the ML community’s attitude will be anything like sufficient for a positive outcome.
Which transhumanist ideas are “not even wrong”?
And do you mean simply ‘not well specified enough’? Or more like ‘unfalsifiable’?
You also seem to be implying that scientists cannot discuss topics outside of their field, or even outside its current reach.
My philosophy on language is that people can generally discuss anything. For any words that we have heard (and indeed, many we haven’t), we have some clues as to their meaning, e.g. based on the context in which they’ve been used and similarity to other words.
Also, would you consider being cautious an inherently good thing?
Finally, from my experience as a Masters student in AI, many people are happy to give opinions on transhumanism, it’s just that many of those opinions are negative.
I found it interesting that he doesn’t think we should stop or slow down, but associates his position with Bill Joy, the author of “Why the Future Doesn’t Need Us” (2000), which argued for halting research in genetics, nanotech and robotics.
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.