Mathematical Logic grad student, doing AI Safety research for ethical reasons.
Working on conceptual alignment, decision theory, cooperative AI and cause prioritization.
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Martín Soto
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[Question] Which one of these two academic routes should I take to end up in AI Safety?
Okay, now it is clear that you were not presupposing the consistency of the logical system, but its soundness (if Rob proves something, then it is true of the world).
I still get the feeling that embracing hypothetical absurdity is how a logical system of this kind will work by default, but I might be missing something, I will look into Adam’s papers.
Alignment being impossible might be better than it being really difficult
Thank you! Really nice enunciation of the capabilities ceilings.
Regarding the question, I certainly haven’t included that nuance in my brief exposition, and it should be accounted for as you mention. This will probably have non-continuous (or at least non-smooth) consequences for the risks graph.
TL;DR: Most of our risk comes from not alignign our first AGI (discontinuity), and immediately after that an increase in difficulty will almost only penalize said AGI, so its capabilities and our risk will decrease (the AI might be able to solve some forms of alignment and not others). I think this alters the risk distribution but not the red quantity. If anything it points at comparing the risk of impossible difficulty to the risk of exactly that difficulty which allows us to solve alignment (and not an unspecified “very difficult” difficulty), which could already be deduced from the setting (even if I didn’t explicitly mention it).
Detailed explanation:
Say humans can correctly align with their objectives agents of complexity up to (or more accurately, below this complexity the alignment will be completely safe or almost harmless with a high probability). And analogously for the superintelligence we are considering (or even for any superintelligence whatsoever if some very hard alignment problems are physically unsolvable, and thus this quantity is finite).
For humans, whether is slightly above or below the complexity of the first AGI we ever build will have vast consequences for our existential risks (and we know for socio-political reasons that this AGI will likely be built, etc.). But for the superintelligence, it is to be expected that its capabilities and power will be continuous in (it has no socio-political reasons due to which failing to solve a certain alignment problem will end its capabilities).
The (direct) dependence I mention in the post between its capabilities and human existential risk can be expected to be continuous as well (even if maybe close to constant because “a less capable unaligned superintelligence is already bad enough”). Since both and are expected to depend continuously (or at least approximately continuously at macroscopic levels) and inversely on the difficulty of solving alignment, we’d have a very steep increase in risk at the point where humans fail to solve their alignment problem (where risk depends inversely and non-continuously on ), and no similar steep decrease as AGI capabilities lower (where risk depends directly and continuously on ).
You might have already talked about this in the meeting (I couldn’t attend), but here goes
. This is around where I have problems. I just can’t quite manage to get myself to see how this quantity is the “slice of marginal utility that coalition promises to player i”, so let me know in the comments if anyone manages to pull it off.
Let’s reason this out for a coalition of 3 members, the simplest case that is not readily understandable (as in your “Alice and Bob” example). We have . We can interpret as the strategic gain obtained (for 1) thanks to this 3 member coalition, that is a direct product of this exact coalition’s capability for coordination and leverage, that is, that doesn’t stem from the player’s own potentials () neither was already present from subcoalitions (like ). The only way to calculate this exact strategic gain in terms of the is to subtract from all these other gains that were already present. In our case, when we rewrite , we’re only saying that is the supplementary gain missing from the sum if we only took into account the gain from the 1-2 coalition plus the further marginal gain added by being in a coalition with 3 as well, and didn’t consider the further strategic benefits that the 3 member coalition could offer. Or expressed otherwise, if we took into account the base potential and added the two marginal gains and .
Of course, this is really just saying that , which is justified by your (and Harsanyi’s) previous reasoning, so this might seem like a trivial rearrangement which hasn’t provided new explanatory power. One might hope, as you seem to imply, that we can get a different kind of justification for the formula, by for instance appealing to bargaining equilibria inside the coalition. But I feel like this is nowhere to be found. After all, you have just introduced/justified/defined , and this is completely equivalent to . It’s an uneventful numerical-set-theoretic rearrangement. Not only that, but this last equality is only true in virtue of the “nice coherence properties” justification/definition you have provided for the previous one, and would not necessarily be true in general. So it is evident that any justification for it will be a completely equivalent reformulation of your previous argument. We will be treading water and ultimately need to resource back to your previous justification. We wouldn’t expect a qualitatively different justification for than for , so we shouldn’t either expect one in this situation (although here the trivial rearrangement is slightly less obvious than subtracting , because to prove the equivalence we need to know those equalities hold for every and ).
Of course, the same can be said of the expression for , which is an equivalent rearrangement of that for the : any of its justifications will ultimately stem from the same initial ideas, and applying definitions. It will be the disagreement point for a certain subgame because we have defined it/justified its expression just like that (and then trivially rearranged).
Please do let me know if I have misinterpreted your intentions in some way. After all, you probably weren’t expecting the controversial LessWrong tradition of dissolving the question :-)
I’d like to hear more about how the boundaries framework can be applied to Resistance from AI Labs to yield new insights or at least a more convenient framework. More concretely, I’m not exactly sure which boundaries you refer to here:
There are many reasons why individual institutions might not take it on as their job to make the whole world safe, but I posit that a major contributing factor is that sense that it would violate a lot of boundaries.
My main issue is I for now agree with Noosphere89′s comment: the main reason is just commonsense “not willing to sacrifice profit”. And this can certainly be conceptualized as “not willing to cross certain boundaries” (extralimiting the objectives of a usual business, reallocating boundaries of internal organization, etc.), but I don’t see how these can shed any more light than the already commonsense considerations.
To be clear, I know you discuss this in more depth in your posts on pivotal acts / processes, but I’m curious as to how explicitly applying the boundaries framework could clarify things.
One is that set of possible fallback points will, in general, not be a single point
Thinking out loud: Might you be able to iterate the bargaining process between the agents to decide which fallback point to choose? This of course will yield infinite regress if no one of the iterations yields a single fallback point. But might it be that the set of fallback points will in some sense become smaller with each iteration? (For instance have smaller worst-case consequences for each players’ utilities) (Or at least this might happen in most real-world situations, even if there are fringe theoretical counter-examples) If that were the case, at a certain finite iteration one of the players would be willing to let the other decide the fallback point (or let it be randomly decided), since the cost of further computations might be higher than the benefit of adjusting more finely the fallback point.
On a more general note, maybe considerations about a real agent’s bounded computation can pragmatically resolve some of these issues. Don’t get me wrong: I get that you’re searching for theoretical groundings, and this would a priori not be the stage in which to drop simplifications. But maybe dropping this one will dissolve some of the apparent grounding under-specifications (because real decisions don’t need to be as fine-grained as theoretical abstraction can make it seem).
General advice for transitioning into Theoretical AI Safety
An issue with MacAskill’s Evidentialist’s Wager
Why does accepting acausal trade (or EDT) provide evidence about an infinite universe? Could you elaborate on that? And of course, not all kinds of infinite universes imply there’s the same amount of Good Twins and Evil Twins.
Oh, of course, I see! I had understood you meant acausal trade was the source of this evidence. Thanks for your clarification!
[Question] Enriching Youtube content recommendations
Thank you for your comment, Sylvester!
As it turns out, you’re right! Yesterday I discussed this issue with Caspar Oesterheld (one of the authors). Indeed, his answer to this objection is that they believe there probably are more positively than negatively correlated agents. Some arguments for that are evolutionary pressures and the correlation between decision theory and values you mention. In this post, I was implicitly relying on digital minds being crazy enough as for a big fraction of them to be negatively correlated to us. This could plausibly be the case in extortion/malevolent actors scenarios, but I don’t have any arguments for that being probable enough.
In fact, I had already come up with a different objection to my argument. And the concept of negatively correlated agents is generally problematic for other reasons. I’ll write another post presenting these and other considerations when I have the time (probably the end of this month). I’ll also go over Greaves [2016], thank you for that resource!
Nice, thank you! I will delve into that one as well when I have the time :-)
Further considerations on the Evidentialist’s Wager
I’m not sure I understand your comment. It is true that in their framing of the Moral Newcomb problem you can at most get 10 cures (because the predictor is perfectly reliable). But what you care about (your utility to maximize) is not only how many cures you personally receive, but how many such cures people similar to you (in other parts of the universe) receive (because allegedly you care about maximizing happiness or people not dying, and obtaining your 10 cures is only instrumental for that). And of course that utility is not necessarily bounded by the 10 cures you personally receive, and can be way bigger if your action provides evidence that many such cures are being obtained across the universe. The authors explain this in page 4:
This means that the simple state-consequence matrix above does not in fact capture everything that is relevant to the decision problem: we have to refine the state space so that it also describes whether or not correlated agents face boxes with cures in both. By taking one box, you gain evidence not only that you will obtain more doses of the cure, but also that these other agents will achieve good outcomes too. Therefore, the existence of correlated agents has the effect of increasing the stakes for EDT.
Thank you for your comment! I hadn’t had the time to read de se choice but am looking forward to it! Thank you also for the other recommendations.
Yep! That was also my intuition behind “all meta-updates (hopes and fears) balancing out” above.
I don’t think this is possible.
If you mean it’s not possible to ensure all your correlates are Good, I don’t see how doing more research about the question doesn’t get you ever closer to that (even if you never reach the ideal limit of literally all correlates being Good).
If you mean no one would want to do that, it might seem like you’d be happy to be uncorrelated from your Evil Twins. But this might again be a naïve view that breaks upon considering meta-reasoning.
Vanessa Kosoy’s PreDCA, distilled
I think your concern is a special case of this paragraph:
How might this protocol solve Inner Alignment? The only way to change our AGI’s actions is by changing its world model, because of its strict architecture that completely pins down a utility function to maximize (and the actions that maximize it) given a world model. So, allegedly, the only possible mesa-optimizers will take the form of acausal attackers (that is, simulation hypotheses), or at least something that can be very naturally modelled as an acausal attack (any false hypothesis about the world that changes the precursor that is chosen as the user, or a property relevant to actions maximizing their utility). And also allegedly, the methods implemented against radical acausal attacks will be sufficient to avoid this (and other less radical wrong hypotheses will be naturally dealt with by our AGI converging on the right physical world model).
We need to prevent our agent from developing false hypotheses because of adversarial inputs (through its sensors). You mention the particular case in which the false hypotheses are about the past (a part of physical reality), and adversarial input is provided as certain arrangements of present physical reality (which our AGI perceives through its sensors). These can be understood as very basic causal attacks. I guess all these cases are supposed to be dealt with by our AGI being capable enough (at modeling physical reality and updating its beliefs) so as to end up noticing the real past events. That is, given the messiness/inter-connectedness of physical reality (partaking in such procedures as “erasure of information” or “fake evidence” actually leave much physical traces that an intelligent enough agent could identify), these issues would probably fall on the side of “less radical wrong hypotheses”, and they are supposed to “be naturally dealt with by our AGI converging on the right physical world model”.
I feel like this is tightly linked (or could be rephrased as an application of) Gödel’s second incompleteness theorem (a system can’t prove its own consistency). Let me explain:
But of course, Rob won’t require Rob to be consistent inside his hypothetical. That is, Rob doesn’t “know” (prove) that Rob is consistent, and so it can’t use this assumption on his proofs (to complete the Löbian reasoning).
Even more concretely in your text:
But Rob can’t assume its own consistency. So Rob wouldn’t be able to conclude this.
In other words, you are already assuming that inside Rob’s system Proof(Blows)→Blows but you need the assumption Con(Rob) to prove this, which isn’t available inside Rob’s system.
We get stuck in a kind of infinite regress. To prove Blows, we need Proof(Blows), and for that Proof(Proof(Blows)) etc. and so the actual conditional proof never takes flight. (Or equivalently, we need Proof(Con(Rob)), and for that Proof(Proof(Con(Rob))), etc.)
This seems to point at embracing hypothetical absurdity as not only a desirable property, but a necessary property of these kinds of systems.
Please do point out anything I might have overlooked. Formalizing the proofs will help clarify the whole issue, so I will look into Adam’s papers when I have more time in case he has gone in that direction.