We are now using a new definition of s-risks. I've edited this post to reflect the change.
S-risks are risks of events that bring about suffering in cosmically significant amounts. By “significant”, we mean significant relative to expected future suffering.
Note that it may turn out that the amount of suffering that we can influence is dwarfed by suffering that we can’t influence. By “expectation of suffering in the future” we mean “expectation of action-relevant suffering in the future”.
Ok, thanks for that. I’d guess then that I’m more uncertain than you about whether human leadership would delegate to systems who would fail to accurately forecast catastrophe.
It’s possible that human leadership just reasons poorly about whether their systems are competent in this domain. For instance, they may observe that their systems perform well in lots of other domains, and incorrectly reason that “well, these systems are better than us in many domains, so they must be better in this one, too”. Eagerness to deploy before a more thorough investigation...
The US and China might well wreck the world by knowingly taking gargantuan risks even if both had aligned AI advisors, although I think they likely wouldn't.
But what I'm saying is really hard to do is to make the scenarios in the OP (with competition among individual corporate boards and the like) occur without extreme failure of 1-to-1 alignment
I'm not sure I understand yet. For example, here’s a version of Flash War that happens seemingly without either the principals knowingly taking gargantuan risks or extreme intent-alignment failure.
Yeah I agree the details aren’t clear. Hopefully your conditional commitment can be made flexible enough that it leaves you open to being convinced by agents who have good reasons for refusing to do this world-model agreement thing. It’s certainly not clear to me how one could do this. If you had some trusted “deliberation module”, which engages in open-ended generation and scrutiny of arguments, then maybe you could make a commitment of the form “use this protocol, unless my counterpart provides reasons which cause my deliberation module to be convinced o...
It seems like we can kind of separate the problem of equilibrium selection from the problem of “thinking more”, if “thinking more” just means refining one’s world models and credences over them. One can make conditional commitments of the form: “When I encounter future bargaining partners, we will (based on our models at that time) agree on a world-model according to some protocol and apply some solution concept (e.g. Nash or Kalai-Smorodinsky) to it in order to arrive at an agreement.”
The set of solution concepts you commit to regarding as acceptable stil...
Nice post! I’m excited to see more attention being paid to multi-agent stuff recently.
A few miscellaneous points:
I get the impression that the added complexity of multi- relative to single-agent systems has not been adequately factored into folks’ thinking about timelines / the difficulty of making AGI that is competent in a multipolar world. But I’m not confident in that.
I think it’s possible that conflict / bargaining failure is a considerable source of existential risk, in addition to suffering risk. I don’t really have a view on how it compares t
Neat post, I think this is an important distinction. It seems right that more homogeneity means less risk of bargaining failure, though I’m not sure yet how much.
Cooperation and coordination between different AIs is likely to be very easy as they are likely to be very structurally similar to each other if not share basically all of the same weights
In what ways does having similar architectures or weights help with cooperation between agents with different goals? A few things that come to mind:
Makes sense. Though you could have deliberate coordinated training even after deployment. For instance, I'm particularly interested in the question of "how will agents learn to interact in high stakes circumstances which they will rarely encounter?" One could imagine the overseers of AI systems coordinating to fine-tune their systems in simulations of such encounters even after deployment. Not sure how plausible that is though.
The new summary looks good =) Although I second Michael Dennis' comment below, that the infinite regress of priors is avoided in standard game theory by specifying a common prior. Indeed the specification of this prior leads to a prior selection problem.
The formality of "priors / equilibria" doesn't have any benefit in this case (there aren't any theorems to be proven)
I’m not sure if you mean “there aren’t any theorems to be proven” or “any theorem that’s proven in this framework would be useless”. The former is false, e.g. there are things to prove ab...
both players want to optimize the welfare function (making it a collaborative game)
The game is collaborative in the sense that a welfare function is optimized in equilibrium, but the principals will in general have different terminal goals (reward functions) and the equilibrium will be enforced with punishments (cf. tit-for-tat).
the issue is primarily that in a collaborative game, the optimal thing for you to do depends strongly on who your partner is, but you may not have a good understanding of who your partner is, and if you're wrong you can do arb
Chimpanzees, crows, and dolphins are capable of impressive feats of higher intelligence, and I don’t think there’s any particular reason to think that Neanderthals are capable of doing anything qualitatively more impressive
This seems like a pretty cursory treatment of what seems like quite a complicated and contentious subject. A few possible counterexamples jump to mind. These are just things I remember coming across when browsing cognitive science sources over the years.
In model-free RL, policy-based methods choose policies by optimizing a noisy estimate of the policy's value. This is analogous to optimizing a noisy estimate of prediction accuracy (i.e., accuracy on the training data) to choose a predictive model. So we often need to trade variance for bias in the policy-learning case (i.e., shrink towards simpler policies) just as in the predictive modeling case.
Maybe pedantic but, couldn't we just look at the decision process as a sequence of episodes from the POMDP, and formulate the problem in terms of the regret incurred by our learning algorithm in this decision process? In particular, if catastrophic outcomes (i.e., ones which dominate the total regret) are possible, then a low-regret learning algorithm will have to be safe while still gathering some information that helps in future episodes. (On this view, the goal of safe exploration research is the same as the goal of learning generally: design low-regret...
Do you think focusing on s-risks leads to meaningfully different technical goals than focusing on other considerations?
I think it definitely leads to a difference in prioritization among the things one could study under the broad heading of AI safety. Hopefully this will be clear in the body of the agenda. And, some considerations around possible downsides of certain alignment work might be more salient to those focused on s-risk; the possibility that attempts at alignment with human values could lead to very bad “near misses” is an example. (I think so...
A few thoughts on this part:... (read more)