Late 2021 MIRI Conversations: AMA / Discussion

Rob Bensinger

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2021 MIRI Conversations

119 Late 2021 MIRI Conversations: AMA / Discussion

by Rob Bensinger

28th Feb 2022

AI Alignment Forum

1 min read

199

119 Ω 45

With the release of Rohin Shah and Eliezer Yudkowsky's conversation, the Late 2021 MIRI Conversations sequence is now complete.

This post is intended as a generalized comment section for discussing the whole sequence, now that it's finished. Feel free to:

raise any topics that seem relevant
signal-boost particular excerpts or comments that deserve more attention
direct questions to participants

In particular, Eliezer Yudkowsky, Richard Ngo, Paul Christiano, Nate Soares, and Rohin Shah expressed active interest in receiving follow-up questions here. The Schelling time when they're likeliest to be answering questions is Wednesday March 2, though they may participate on other days too.

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Shah and Yudkowsky on alignment failures

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Mentioned in

221Catching the Eye of Sauron

211Humans provide an untapped wealth of evidence about alignment

154Emotionally Confronting a Probably-Doomed World: Against Motivation Via Dignity Points

85Shah and Yudkowsky on alignment failures

82I applied for a MIRI job in 2020. Here's what happened next.

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Late 2021 MIRI Conversations: AMA / Discussion

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[-]Vaniver3yΩ17480

This is mostly in response to stuff written by Richard, but I'm interested in everyone's read of the situation.

While I don't find Eliezer's core intuitions about intelligence too implausible, they don't seem compelling enough to do as much work as Eliezer argues they do. As in the Foom debate, I think that our object-level discussions were constrained by our different underlying attitudes towards high-level abstractions, which are hard to pin down (let alone resolve).
Given this, I think that the most productive mode of intellectual engagement with Eliezer's worldview going forward is probably not to continue debating it (since that would likely hit those same underlying disagreements), but rather to try to inhabit it deeply enough to rederive his conclusions and find new explanations of them which then lead to clearer object-level cruxes.

I'm not sure yet how to word this as a question without some introductory paragraphs. When I read Eliezer, I often feel like he has a coherent worldview that sees lots of deep connections and explains lots of things, and that he's actively trying to be coherent / explain everything. [This is what I think you're pointing to with his 'attitude toward... (read more)

[-]paulfchristiano3yΩ20382

I feel like I have a broad distribution over worlds and usually answer questions with probability distributions, that I have a complete mental universe (which feels to me like it outputs answers to a much broader set of questions than Eliezer's, albeit probabilistic ones, rather than bailing with "the future is hard to predict"). At a high level I don't think "mainline" is a great concept for describing probability distributions over the future except in certain exceptional cases (though I may not understand what "mainline" means), and that neat stories that fit everything usually don't work well (unless, or often even if, generated in hindsight).

In answer to your "why is this," I think it's a combination of moderate differences in functioning and large differences in communication style. I think Eliezer has a way of thinking about the future that is quite different from mine and I'm somewhat skeptical of and feel like Eliezer is overselling (which is what got me into this discussion), but that's probably smaller than a large difference in communication style (driven partly by different skills, different aesthetics, and different ideas about what kinds of standards discourse should aspire to).

I think I may not understand well the basic lesson / broader point, so will probably be more helpful on object level points and will mostly go answer those in the time I have.

[-]Vaniver3yΩ6180

I feel like I have a broad distribution over worlds and usually answer questions with probability distributions, that I have a complete mental universe (which feels to me like it outputs answers to a much broader set of questions than Eliezer's, albeit probabilistic ones, rather than bailing with "the future is hard to predict").

Sometimes I'll be tracking a finite number of "concrete hypotheses", where every hypothesis is 'fully fleshed out', and be doing a particle-filtering style updating process, where sometimes hypotheses gain or lose weight, sometimes they get ruled out or need to split, or so on. In those cases, I'm moderately confident that every 'hypothesis' corresponds to a 'real world', constrained by how well as I can get my imagination to correspond to reality. [A 'finite number' depends on the situation, but I think it's normally something like 2-5, unless it's an area I've built up a lot of cache about.]

Sometimes I'll be tracking a bunch of "surface-level features", where the distributions on the features don't always imply coherent underlying worlds, either on their own or in combination with other features. (For example, I might have guesses about the probability th... (read more)

[-]paulfchristiano3yΩ8130

I think my way of thinking about things is often a lot like "draw random samples," more like drawing N random samples rather than particle filtering (I guess since we aren't making observations as we go---if I notice an inconsistency the thing I do is more like backtrack and start over with N fresh samples having updated on the logical fact).

The main complexity feels like the thing you point out where it's impossible to make them fully fleshed out, so you build a bunch of intuitions about what is consistent (and could be fleshed out given enough time) and then refine those intuitions only periodically when you actually try to flesh something out and see if it makes sense. And often you go even further and just talk about relationships amongst surface level features using intuitions refined from a bunch of samples.

I feel like a distinctive feature of Eliezer's dialog w.r.t. foom / alignment difficulty is that he has a lot of views about strong regularities that should hold across all of these worlds. And then disputes about whether worlds are plausible often turn on things like "is this property of the described world likely?" which is tough because obviously everyone agrees that ev... (read more)

2Vaniver3y

Oh whoa, you don't remember your samples from before? [I guess I might not either, unless I'm concentrating on keeping them around or verbalized them or something; probably I do something more expert-iteration-like where I'm silently updating my generating distributions based on the samples and then resampling them in the future.] Yeah, this seems likely; this makes me more interested in the "selectively ignoring variables" hypothesis for why Eliezer running this strategy might have something that would naturally be called a mainline. [Like, it's very easy to predict "number of apples sold = number of apples bought" whereas it's much harder to predict the price of apples.] But maybe instead he means it in the 'startup plan' sense, where you do actually assign basically no probability to your mainline prediction, but still vastly more than any other prediction that's equally conjunctive.

[-]Rohin Shah3yΩ4110

EDIT: I wrote this before seeing Paul's response; hence a significant amount of repetition.

They often seem to emit sentences that are 'not absurd', instead of 'on their mainline', because they're mostly trying to generate sentences that pass some shallow checks instead of 'coming from their complete mental universe.'
Why is this?

Well, there are many boring cases that are explained by pedagogy / argument structure. When I say things like "in the limit of infinite oversight capacity, we could just understand everything about the AI system and reengineer it to be safe", I'm obviously not claiming that this is a realistic thing that I expect to happen, so it's not coming from my "complete mental universe"; I'm just using this as an intuition pump for the listener to establish that a sufficiently powerful oversight process would solve AI alignment.

That being said, I think there is a more interesting difference here, but that your description of it is inaccurate (at least for me).

From my perspective I am implicitly representing a probability distribution over possible futures in my head. When I say "maybe X happens", or "X is not absurd", I'm saying that my probability distribution assign... (read more)

[-]So8res3yΩ19400

In response to your last couple paragraphs: the critique, afaict, is not "a real human cannot keep multiple concrete scenarios in mind and speak probabilistically about those", but rather "a common method for representing lots of hypotheses at once, is to decompose the hypotheses into component properties that can be used to describe lots of concrete hypotheses. (toy model: instead of imagining all numbers, you note that some numbers are odd and some numbers are even, and then think of evenness and oddness). A common failure mode when attempting this is that you lose track of which properties are incompatible (toy model: you claim you can visualize a number that is both even and odd). A way to avert this failure mode is to regularly exhibit at least one concrete hypothesis that simultaneousy posseses whatever collection of properties you say you can simultaneously visualize (toy model: demonstrating that 14 is even and 7 is odd does not in fact convince me that you are correct to imagine a number that is both even and odd)."

On my understanding of Eliezer's picture (and on my own personal picture), almost nobody ever visibly tries to do this (never mind succeeding), when it comes to hopeful AGI scenarios.

Insofar as you have thought about at least one specific hopeful world in great detail, I strongly recommend, spelling it out, in all its great detail, to Eliezer, next time you two chat. In fact, I personally request that you do this! It sounds great, and I expect it to constitute some progress in the debate.

[-]habryka3yΩ14330

Relevant Feynman quote:

I had a scheme, which I still use today when somebody is explaining something that I’m trying to understand: I keep making up examples.
For instance, the mathematicians would come in with a terrific theorem, and they’re all excited. As they’re telling me the conditions of the theorem, I construct something which fits all the conditions. You know, you have a set (one ball)-- disjoint (two balls). Then the balls turn colors, grow hairs, or whatever, in my head as they put more conditions on.
Finally they state the theorem, which is some dumb thing about the ball which isn’t true for my hairy green ball thing, so I say “False!” [and] point out my counterexample.

[-]johnswentworth3yΩ11210

As I understand it, when you "talk about the mainline", you're supposed to have some low-entropy (i.e. confident) view on how the future goes, such that you can answer very different questions X, Y and Z about that particular future, that are all correlated with each other, and all get (say) > 50% probability. (Idk, as I write this down, it seems so obviously a bad way to reason that I feel like I must not be understanding it correctly.)
But to the extent this is right, I'm actually quite confused why anyone thinks "talk about the mainline" is an ideal to which to aspire. What makes you expect that?

I'll try to explain the technique and why it's useful. I'll start with a non-probabilistic version of the idea, since it's a little simpler conceptually, then talk about the corresponding idea in the presence of uncertainty.

Suppose I'm building a mathematical model of some system or class of systems. As part of the modelling process, I write down some conditions which I expect the system to satisfy - think energy conservation, or Newton's Laws, or market efficiency, depending on what kind of systems we're talking about. My hope/plan is to derive (i.e. prove) some predictions from these... (read more)

9Rohin Shah3y

Man, I would not call the technique you described "mainline prediction". It also seems kinda inconsistent with Vaniver's usage; his writing suggests that a person only has one mainline at a time which seems odd for this technique. Vaniver, is this what you meant? If so, my new answer is that I and others do in fact talk about "mainline predictions" -- for me, there was that whole section talking about natural language debate as an alignment strategy. (It ended up not being about a plausible world, but that's because (a) Eliezer wanted enough concreteness that I ended up talking about the stupidly inefficient version rather than the one I'd actually expect in the real world and (b) I was focused on demonstrating an existence proof for the technical properties, rather than also trying to include the social ones.)

6johnswentworth3y

To be clear, I do not mean to use the label "mainline prediction" for this whole technique. Mainline prediction tracking is one way of implementing this general technique, and I claim that the usefulness of the general technique is the main reason why mainline predictions are useful to track. (Also, it matches up quite well with Nate's model based on his comment here, and I expect it also matches how Eliezer wants to use the technique.)

6Rohin Shah3y

Ah, got it. I agree that: 1. The technique you described is in fact very useful 2. If your probability distribution over futures happens to be such that it has a "mainline prediction", you get significant benefits from that (similar to the benefits you get from the technique you described).

4Vaniver3y

Uh, I inherited "mainline" from Eliezer's usage in the dialogue, and am guessing that his reasoning is following a process sort of like mine and John's. My natural word for it is a 'particle', from particle filtering, as linked in various places, which I think is consistent with John's description. I'm further guessing that Eliezer's noticed more constraints / implied inconsistencies, and is somewhat better at figuring out which variables to drop, so that his cloud is narrower than mine / more generates 'mainline predictions' than 'probability distributions'. Do you feel like you do this 'sometimes', or 'basically always'? Maybe it would be productive for me to reread the dialogue (or at least part of it) and sort sections / comments by how much they feel like they're coming from this vs. some other source. As a specific thing that I have in mind, I think there's a habit of thinking / discourse that philosophy trains, which is having separate senses for "views in consideration" and "what I believe", and thinking that statements should be considered against all views in consideration, even ones that you don't believe. This seems pretty good in some respects (if you begin by disbelieving a view incorrectly, your habits nevertheless gather you lots of evidence about it, which can cause you to then correctly believe it), and pretty questionable in other respects (conversations between Alice and Bob now have to include them shadowboxing with everyone else in the broader discourse, as Alice is asking herself "what would Carol say in response to that?" to things that Bob says to her). When I imagine dialogues generated by people who are both sometimes doing the mainline thing and sometimes doing the 'represent the whole discourse' thing, they look pretty different from dialogues generated by people who are both only doing the mainline thing. [And also from dialogues generated by both people only doing the 'represent the whole discourse' thing, of course.]

5Rohin Shah3y

I don't know what "this" refers to. If the referent is "have a concrete example in mind", then I do that frequently but not always. I do it a ton when I'm not very knowledgeable and learning about a thing; I do it less as my mastery of a subject increases. (Examples: when I was initially learning addition, I used the concrete example of holding up three fingers and then counting up two more to compute 3 + 2 = 5, which I do not do any more. When I first learned recursion, I used to explicitly run through an execution trace to ensure my program would work, now I do not.) If the referent is "make statements that reflect my beliefs", then it depends on context, but in the context of these dialogues, I'm always doing that. (Whereas when I'm writing for the newsletter, I'm more often trying to represent the whole discourse, though the "opinion" sections are still entirely my beliefs.)

3Vaniver3y

I think this is roughly how I'm thinking about things sometimes, tho I'd describe the mainline as the particle with plurality weight (which is a weaker condition than >50%). [I don't know how Eliezer thinks about things; maybe it's like this? I'd be interested in hearing his description.] I think this is also a generator of disagreements about what sort of things are worth betting on; when I imagine why I would bail with "the future is hard to predict", it's because the hypotheses/particles I'm considering have clearly defined X, Y, and Z variables (often discretized into bins or ranges) but not clearly defined A, B, and C variables (tho they might have distributions over those variables), because if you also conditioned on those you would have Too Many Particles. And when I imagine trying to contrast particles on features A, B, and C, as they all make weak predictions we get at most a few bits of evidence to update their weights on, whereas when we contrast them on X, Y, and Z we get many more bits, and so it feels more fruitful to reason about. I mean, the question is which direction we want to approach Bayesianism from, given that Bayesianism is impossible (as you point out later in your comment). On the one hand, you could focus on 'updating', and have lots of distributions that aren't grounded in reality but which are easy to massage when new observations come in, and on the other hand, you could focus on 'hypotheses', and have as many models of the situation as you can ground, and then have to do something much more complicated when new observations come in. [Like, a thing I find helpful to think about here is where the motive power from Aumann's Agreement Theorem comes from, which is that when I say 40% A, you know that my private info is consistent with an update of the shared prior whose posterior is 40%, and when you take the shared prior and update on your private info and that my private info is consistent with 40% and your posterior is 60% A, then I

4Rohin Shah3y

If you define "mainline" as "particle with plurality weight", then I think I was in fact "talking on my mainline" at some points during the conversation, and basically everywhere that I was talking about worlds (instead of specific technical points or intuition pumps) I was talking about "one of my top 10 particles". I think I responded to every request for concreteness with a fairly concrete answer. Feel free to ask me for more concreteness in any particular story I told during the conversation.

3Vaniver3y

Huh, I guess I don't believe the intuition pump? Like, as the first counterexample that comes to mind, when I imagine having an AGI where I can tell everything about how it's thinking, and yet I remain a black box to myself, I can't really tell whether or not it's aligned to me. (Is me-now the one that I want it to be aligned to, or me-across-time? Which side of my internal conflicts about A vs. B / which principle for resolving such conflicts?) I can of course imagine a reasonable response to that from you--"ah, resolving philosophical difficulties is the user's problem, and not one of the things that I mean by alignment"--but I think I have some more-obviously-alignment-related counterexamples. [Tho if by 'infinite oversight ability' you do mean something like 'logical omniscience' it does become pretty difficult to find a real counterexample, in part because I can just find the future trajectory with highest expected utility and take the action I take at the start of that trajectory without having to have any sort of understanding about why that action was predictably a good idea.] But like, the thing this reminds me of is something like extrapolating tangents, instead of operating the production function? "If we had an infinitely good engine, we could make the perfect car", which seems sensible when you're used to thinking of engine improvements linearly increasing car quality and doesn't seem sensible when you're used to thinking of car quality as a product of sigmoids of the input variables. (This is a long response to a short section because I think the disagreement here is about something like "how should we reason and communicate about intuitions?", and so it's worth expanding on what I think might be the implications of otherwise minor disagreements.)

2Rohin Shah3y

That is in fact my response. (Though one of the ways in which the intuition pump isn't fully compelling to me is that, even after understanding the exact program that the AGI implements and its causal history, maybe the overseers can't correctly predict the consequences of running that program for a long time. Still feels like they'd do fine.) I do agree that if you go as far as "logical omniscience" then there are "cheating" ways of solving the problem that don't really tell us much about how hard alignment is in practice. The car analogy just doesn't seem sensible. I can tell stories of car doom even if you have infinitely good engines (e.g. the steering breaks). My point is that we struggle to tell stories of doom when imagining a very powerful oversight process that knows everything the model knows. I'm not thinking "more oversight quality --> more alignment" and then concluding "infinite oversight quality --> alignment solved". I'm starting with the intuition pump, noticing I can no longer tell a good story of doom, and concluding "infinite oversight quality --> alignment solved". So I don't think this has much to do with extrapolating tangents vs. production functions, except inasmuch as production functions encourage you to think about complements to your inputs that you can then posit don't exist in order to tell a story of doom.

2Vaniver3y

I think some of my more alignment-flavored counterexamples look like: * The 'reengineer it to be safe' step breaks down / isn't implemented thru oversight. Like, if we're positing we spin up a whole Great Reflection to evaluate every action the AI takes, this seems like it's probably not going to be competitive! * The oversight gives us as much info as we ask for, but the world is a siren world (like what Stuart points to, but a little different), where the initial information we discover about the plans from oversight is so convincing that we decide to go ahead with the AI before discovering the gotchas. * Related to the previous point, the oversight is sufficient to reveal features about the plan that are terrible, but before the 'reengineer to make it more safe' plan is executed, the code is stolen and executed by a subset of humanity which thinks the terrible plan is 'good enough', for them at least. That is, it feels to me like we benefit a lot from having 1) a constructive approach to alignment instead of rejection sampling, 2) sufficient security focus that we don't proceed on EV of known information, but actually do the 'due diligence', and 3) sufficient coordination among humans that we don't leave behind substantial swaths of current human preferences, and I don't see how we get those thru having arbitrary transparency. [I also would like to solve the problem of "AI has good outcomes" instead of the smaller problem of "AI isn't out to get us", because accidental deaths are deaths too! But I do think it makes sense to focus on that capability problem separately, at least sometimes.]

2Rohin Shah3y

I obviously do not think this is at all competitive, and I also wanted to ignore the "other people steal your code" case. I am confused what you think I was trying to do with that intuition pump. I guess I said "powerful oversight would solve alignment" which could be construed to mean that powerful oversight => great future, in which case I'd change it to "powerful oversight would deal with the particular technical problems that we call outer and inner alignment", but was it really so non-obvious that I was talking about the technical problems? Maybe your point is that there are lots of things required for a good future, just as a car needs both steering and an engine, and so the intuition pump is not interesting because it doesn't talk about all the things needed for a good future? If so, I totally agree that it does not in fact include all the things needed for a good future, and it was not meant to be saying that. This just doesn't seem plausible to me. Where did the information come from? Did the AI system optimize the information to be convincing? If yes, why didn't we notice that the AI system was doing that? Can we solve this by ensuring that we do due diligence, even if it doesn't seem necessary?

2Vaniver3y

I think I'm confused about the intuition pump too! Like, here's some options I thought up: * The 'alignment problem' is really the 'not enough oversight' problem. [But then if we solve the 'enough oversight' problem, we still have to solve the 'what we want' problem, the 'coordination' problem, the 'construct competitively' problem, etc.] * Bits of the alignment problem can be traded off against each other, most obviously coordination and 'alignment tax' (i.e. the additional amount of work you need to do to make a system aligned, or the opposite of 'competitiveness', which I didn't want to use here for ease-of-understanding-by-newbies reasons.) [But it's basically just coordination and competitiveness; like, you could imagine that oversight gives you a rejection sampling story for trading off time and understanding but I think this is basically not true because you're also optimizing for finding holes in your transparency regime.] Like, by analogy, I could imagine someone who uses an intuition pump of "if you had sufficient money, you could solve any problem", but I wouldn't use that intuition pump because I don't believe it. [Sure, 'by definition' if the amount of money doesn't solve the problem, it's not sufficient. But why are we implicitly positing that there exists a sufficient amount of money instead of thinking about what money cannot buy?] (After reading the rest of your comment, it seems pretty clear to me that you mean the first bullet, as you say here:) I both 1) didn't think it was obvious (sorry if I'm being slow on following the change in usage of 'alignment' here) and 2) don't think realistically powerful oversight solves either of those two on its own (outer alignment because of "rejection sampling can get you siren worlds" problem, inner alignment because "rejection sampling isn't competitive", but I find that one not very compelling and suspect I'll eventually develop a better objection). [EDIT: I note that I also might be doing another unf

2Rohin Shah3y

I mean, maybe we should just drop this point about the intuition pump, it was a throwaway reference in the original comment. I normally use it to argue against a specific mentality I sometimes see in people, and I guess it doesn't make sense outside of that context. (The mentality is "it doesn't matter what oversight process you use, there's always a malicious superintelligence that can game it, therefore everyone dies".)

[-]Vaniver3yΩ5100

The most recent post has a related exchange between Eliezer and Rohin:

Eliezer: I think the critical insight - though it has a format that basically nobody except me ever visibly invokes in those terms, and I worry maybe it can only be taught by a kind of life experience that's very hard to obtain - is the realization that any consistent reasonable story about underlying mechanisms will give you less optimistic forecasts than the ones you get by freely combining surface desiderata
Rohin: Yeah, I think I do not in fact understand why that is true for any consistent reasonable story.

If I'm being locally nitpicky, I argue that Eliezer's thing is a very mild overstatement (it should be "≤" instead of "<") but given that we're talking about forecasts, we're talking about uncertainty, and so we should expect "less" optimism instead of just "not more" optimism, and so I think Eliezer's statement stands as a general principle about engineering design.

This also feels to me like the sort of thing that I somehow want to direct attention towards. Either this principle is right and relevant (and it would be good for the field if all the AI safety thinkers held it!), or there's some deep confusion of mine that I'd like cleared up.

6A. Mensch3y

Question to Eliezer: would you agree with the gist of the following? And if not, any thoughts on what lead to a strong sense of 'coherence in your worldview' as Vaniver put it? Vaniver, I feel like you're pointing at something that I've noticed as well and am interested in too (the coherence of Eliezer's worldview as you put it). I wonder if has something to do with not going to uni but building his whole worldview all by him self. In my experience uni often tends towards to cramming lots of facts which are easily testable on exams, with less emphasis on understanding underlying principles (which is harder to test with multiple choice questions). Personally I feel like I had to spend my years after uni trying to make sense, a coherent whole if you like, of all the separate things I've learned while in uni where things were mostly just kind of put out there without constantly integrating things. Perhaps if you start out thinking much more about underlying principles earlier on it's easier to integrate all the separate facts into a coherent whole as you go along. Not sure if Eliezer would agree with this. Maybe it's even much more basic and he just always had a very strong sense of dissatisfaction if he couldn't make things cohere into a whole and this urge for things to make sense was much more important than self studying or thinking about underlying principles before and then during the learning of new knowledge... I would like to point out a section in the latest Shay/Yudkowsky dialogue where Eliezer says some things about this topic, does this feel like it's the same thing you are talking about Vaniver?

4Rohin Shah3y

Note that my first response was: and my immediately preceding message was I think I was responding to the version of the argument where "freely combining surface desiderata" was swapped out with "arguments about what you're selecting for". I probably should have noted that I agreed with the basic abstract point as Eliezer stated it; I just don't think it's very relevant to the actual disagreement. I think my complaints in the context of the discussion are: * It's a very weak statement. If you freely combine the most optimistic surface desiderata, you get ~0% chance of doom. My estimate is way higher (in odds-space) than ~0%, and the statement "p(doom) >= ~0%" is not that interesting and not a justification of "doom is near-inevitable". * Relatedly, I am not just "freely combining surface desiderata". I am doing something like "predicting what properties AI systems would have by reasoning about what properties we selected for during training". I think you could reasonably ask how that compares against "predicting what properties AI systems would have by reasoning about what mechanistic algorithms could produce the behavior we observed during training". I was under the impression that this was what Eliezer was pointing at (because that's how I framed it in the message immediately prior to the one you quoted) but I'm less confident of that now.

[-]Vaniver3yΩ10220

Sorry, I probably should have been more clear about the "this is a quote from a longer dialogue, the missing context is important." I do think that the disagreement about "how relevant is this to 'actual disagreement'?" is basically the live thing, not whether or not you agree with the basic abstract point.

My current sense is that you're right that the thing you're doing is more specific than the general case (and one of the ways you can tell is the line of argumentation you give about chance of doom), and also Eliezer can still be correctly observing that you have too many free parameters (even if the number of free parameters is two instead of arbitrarily large). I think arguments about what you're selecting for either cash out in mechanistic algorithms, or they can deceive you in this particular way.

Or, to put this somewhat differently, in my view the basic abstract point implies that having one extra free parameter allows you to believe in a 5% chance of doom when in fact there's 100% chance of doom, and so in order to get estimations like that right this needs to be one of the basic principles shaping your thoughts, tho ofc your prior should come from many examples instead of ... (read more)

2Rohin Shah3y

I agree that if you have a choice about whether to have more or fewer free parameters, all else equal you should prefer the model with fewer free parameters. (Obviously, all else is not equal; in particular I do not think that Eliezer's model is tracking reality as well as mine.) When Alice uses a model with more free parameters, you need to posit a bias before you can predict a systematic direction in which Alice will make mistakes. So this only bites you if you have a bias towards optimism. I know Eliezer thinks I have such a bias. I disagree with him. I agree that this is true in some platonic sense. Either the argument gives me a correct answer, in which case I have true statements that could be cashed out in terms of mechanistic algorithms, or the argument gives me a wrong answer, in which case it wouldn't be derivable from mechanistic algorithms, because the mechanistic algorithms are the "ground truth". Quoting myself from the dialogue:

4Vaniver3y

That is, when I give Optimistic Alice fewer constraints, she can more easily imagine a solution, and when I give Pessimistic Bob fewer constraints, he can more easily imagine that no solution is possible? I think... this feels true as a matter of human psychology of problem-solving, or something, and not as a matter of math. Like, the way Bob fails to find a solution mostly looks like "not actually considering the space", or "wasting consideration on easily-known-bad parts of the space", and more constraints could help with both of those. But, as math, removing constraints can't lower the volume of the implied space and so can't make it less likely that a viable solution exists. I think Eliezer thinks nearly all humans have such a bias by default, and so without clear evidence to the contrary it's a reasonable suspicion for anyone. [I think there's a thing Eliezer does a lot, which I have mixed feelings about, which is matching people's statements to patterns and then responding to the generator of the pattern in Eliezer's head, which only sometimes corresponds to the generator in the other person's head.] Cool, makes sense. [I continue to think we disagree about how true this is in a practical sense, where I read you as thinking "yeah, this is a minor consideration, we have to think with the tools we have access to, which could be wrong in either direction and so are useful as a point estimate" and me as thinking "huh, this really seems like the tools we have access to are going to give us overly optimistic answers, and we should focus more on how to get tools that will give us more robust answers."]

[-]Raemon3yΩ8150

[I think there's a thing Eliezer does a lot, which I have mixed feelings about, which is matching people's statements to patterns and then responding to the generator of the pattern in Eliezer's head, which only sometimes corresponds to the generator in the other person's head.]

I want to add an additional meta-pattern – there was a once a person who thought I had a particular bias. They'd go around telling me "Ray, you're exhibiting that bias right now. Whatever rationalization you're coming up with right now, it's not the real reason you're arguing X." And I was like "c'mon man. I have a ton of introspective access to myself and I can tell that this 'rationalization' is actually a pretty good reason to believe X and I trust that my reasoning process is real."

But... eventually I realized I just actually had two motivations going on. When I introspected, I was running a check for a positive result on "is Ray displaying rational thought?". When they extrospected me (i.e. reading my facial expressions), they were checking for a positive result on "does Ray seem biased in this particular way?".

And both checks totally returned 'true', and that was an accurate assessment.

The partic... (read more)

4Rohin Shah3y

I think we're imagining different toy mathematical models. Your model, according to me: 1. There is a space of possible approaches, that we are searching over to find a solution. (E.g. the space of all possible programs.) 2. We put a layer of abstraction on top of this space, characterizing approaches by N different "features" (e.g. "is it goal-directed", "is it an oracle", "is it capable of destroying the world") 3. Because we're bounded agents, we then treat the features as independent, and search for some combination of features that would comprise a solution. I agree that this procedure has a systematic error in claiming that there is a solution when none exists (and doesn't have the opposite error), and that if this were an accurate model of how I was reasoning I should be way more worried about correcting for that problem. My model: 1. There is a probability distribution over "ways the world could be". 2. We put a layer of abstraction on top of this space, characterizing "ways the world could be" by N different "features" (e.g. "can you get human-level intelligence out of a pile of heuristics", "what are the returns to specialization", "how different will AI ontologies be from human ontologies"). We estimate the marginal probability of each of those features. 3. Because we're bounded agents, when we need the joint probability of two or more features, we treat them as independent and just multiply. 4. Given a proposed solution, we estimate its probability of working by identifying which features need to be true of the world for the solution to work, and then estimate the probability of those features (using the method above). I claim that this procedure doesn't have a systematic error in the direction of optimism (at least until you add some additional details), and that this procedure more accurately reflects the sort of reasoning that I am doing.

5Vaniver3y

Huh, why doesn't that procedure have that systematic error? Like, when I try to naively run your steps 1-4 on "probability of there existing a number that's both even and odd", I get that about 25% of numbers should be both even and odd, so it seems pretty likely that it'll work out given that there are at least 4 numbers. But I can't easily construct an argument at a similar level of sophistication that gives me an underestimate. [Like, "probability of there existing a number that's both odd and prime" gives the wrong conclusion if you buy that the probability that a natural number is prime is 0, but this is because you evaluated your limits in the wrong order, not because of a problem with dropping all the covariance data from your joint distribution.] My first guess is that you think I'm doing the "ways the world could be" thing wrong--like, I'm looking at predicates over numbers and trying to evaluate a predicate over all numbers, but instead I should just have a probability on "universe contains a number that is both even and odd" and its complement, as those are the two relevant ways the world can be. My second guess is that you've got a different distribution over target predicates; like, we can just take the complement of my overestimate ("probability of there existing no numbers that are both even and odd") and call it an underestimate. But I think I'm more interested in 'overestimating existence' than 'underestimating non-existence'. [Is this an example of the 'additional details' you're talking about?] Also maybe you can just exhibit a simple example that has an underestimate, and then we need to think harder about how likely overestimates and underestimates are to see if there's a net bias.

2Rohin Shah3y

It's the first guess. I think if you have a particular number then I'm like "yup, it's fair to notice that we overestimate the probability that x is even and odd by saying it's 25%", and then I'd say "notice that we underestimate the probability that x is even and divisible by 4 by saying it's 12.5%". I agree that if you estimate a probability, and then "perform search" / "optimize" / "run n copies of the estimate" (so that you estimate the probability as 1 - (1 - P(event))^n), then you're going to have systematic errors. I don't think I'm doing anything that's analogous to that. I definitely don't go around thinking "well, it seems 10% likely that such and such feature of the world holds, and so each alignment scheme I think of that depends on this feature has a 10% chance of working, therefore if I think of 10 alignment schemes I've solved the problem". (I suspect this is not the sort of mistake you imagine me doing but I don't think I know what you do imagine me doing.)

4Vaniver3y

Cool, I like this example. I think the thing I'm interested in is "what are our estimates of the output of search processes?". The question we're ultimately trying to answer with a model here is something like "are humans, when they consider a problem that could have attempted solutions of many different forms, overly optimistic about how solvable those problems are because they hypothesize a solution with inconsistent features?" The example of "a number divisible by 2 and a number divisible by 4" is an example of where the consistency of your solution helps you--anything that satisfies the second condition is already satisfying the first condition. But importantly the best you can do here is ignore superfluous conditions; they can't increase the volume of the solution space. I think this is where the systematic bias is coming from (that the joint probability of two conditions can't be higher than the maximum of those two conditions, where the joint probability can be lower than the minimum of the two, and so the product isn't an unbiased estimator of the joint). For example, consider this recent analysis of cultured meat, which seems to me to point out a fundamental inconsistency of this type in people's plans for creating cultured meat. Basically, the bigger you make a bioreactor, the better it looks on criteria ABC, and the smaller you make a bioreactor, the better it looks on criteria DEF, and projections seem to suggest that massive progress will be made on all of those criteria simultaneously because progress can be made on them individually. But this necessitates making bioreactors that are simultaneously much bigger and much smaller! [Sometimes this is possible, because actually one is based on volume and the other is based on surface area, and so when you make something like a zeolite you can combine massive surface area with tiny volume. But if you need massive volume and tiny surface area, that's not possible. Anyway, in this case, my read is that

5Rohin Shah3y

Re: cultured meat example: If you give me examples in which you know the features are actually inconsistent, my method is going to look optimistic when it doesn't know about that inconsistency. So yeah, assuming your description of the cultured meat example is correct, my toy model would reproduce that problem. To give a different example, consider OpenAI Five. One would think that to beat Dota, you need to have an algorithm that allows you to do hierarchical planning, state estimation from partial observability, coordination with team members, understanding of causality, compression of the giant action space, etc. Everyone looked at this giant list of necessary features and thought "it's highly improbable for an algorithm to demonstrate all of these features". My understanding is that even OpenAI, the most optimistic of everyone, thought they would need to do some sort of hierarchical RL to get this to work. In the end, it turned out that vanilla PPO with reward shaping and domain randomization was enough. It turns out that all of these many different capabilities / features were very consistent with each other and easier to achieve simultaneously than we thought. Tbc, I don't want to claim "unbiased estimator" in the mathematical sense of the phrase. To even make such a claim you need to choose some underlying probability distribution which gives rise to our features, which we don't have. I'm more saying that the direction of the bias depends on whether your features are positively vs. negatively correlated with each other and so a priori I don't expect the bias to be in a predictable direction. They definitely have that problem. I'm not sure how you don't have that problem; you're always going to have some amount of abstraction and some amount of inconsistency; the future is hard to predict for bounded humans, and you can't "fully populate the details" as an embedded agent. If you're asking how you notice any inconsistencies at all (rather than all of the inc

7Richard_Ngo3y

To me it seems like this is what you should expect other people to look like both when other people know less about a domain than you do, and also when you're overconfident about your understanding of that domain. So I don't think it helps distinguish those two cases. (Also, to me it seems like a similar thing happens, but with the positions reversed, when Paul and Eliezer try to forecast concrete progress in ML over the next decade. Does that seem right to you?) I believe this was discussed further at some point - I argued that Eliezer-style political history books also exclude statements like "and then we survived the cold war" or "most countries still don't have nuclear energy".

6Vaniver3y

It feels similar but clearly distinct? Like, in that situation Eliezer often seems to say things that I parse as "I don't have any special knowledge here", which seems like a different thing than "I can't easily sample from my distribution over how things go right", and I also have the sense of Paul being willing to 'go specific' and Eliezer not being willing to 'go specific'. You're thinking of this bit of the conversation, starting with: (Or maybe a bit earlier and later, but that was my best guess for where to start the context.) The main quotes from the middle that seems relevant: and ending with: Rereading that section, my sense is that it reads like a sort of mirror of the Eliezer->Paul "I don't know how to operate your view" section; like, Eliezer can say "I think nukes are less worrying for reasons ABC, also you can observe me being not worried about other things-people-are-concerned-by XYZ", but I wouldn't have expected you (or the reader who hasn't picked up Eliezer-thinking from elsewhere) to have been able to come away from that with why you trying to be Eliezer from 1930s would have thought 'and then it turned out okay' would have been a political-history-book-sentence, or the relative magnitudes of the surprise. [Like, I think my 1930s-Eliezer puts like 3-30% on "and then it turned out okay" for nukes, and my 2020s-Eliezer puts like 0.03-3% on that for AGI? But it'd be nice to hear if Eliezer thinks AGI turning out as well as nukes is like 10x the surprise of nukes turning out this well conditioned on pre-1930s, or more like 1000x the surprise.]

6dxu3y

This is a very interesting point! I will chip in by pointing out a very similar remark from Rohin just earlier today: That is all. (Obviously there's a kinda superficial resemblance here to the phenomenon of "calling out" somebody else; I want to state outright that this is not the intention, it's just that I saw your comment right after seeing Rohin's comment, in such a way that my memory of his remark was still salient enough that the connection jumped out at me. Since salient observations tend to fade over time, I wanted to put this down before that happened.)

6Vaniver3y

Yeah, I'm also interested in the question of "how do we distinguish 'sentences-on-mainline' from 'shoring-up-edge-cases'?", or which conversational moves most develop shared knowledge, or something similar. Like I think it's often good to point out edge cases, especially when you're trying to formalize an argument or look for designs that get us out of this trap. In another comment in this thread, I note that there's a thing Eliezer said that I think is very important and accurate, and also think there's an edge case that's not obviously handled correctly. But also my sense is that there's some deep benefit from "having mainlines" and conversations that are mostly 'sentences-on-mainline'? Or, like, there's some value to more people thinking thru / shooting down their own edge cases (like I do in the mentioned comment), instead of pushing the work to Eliezer. I'm pretty worried that there are deeply general reasons to expect AI alignment to be extremely difficult, people aren't updating on the meta-level point and continue to attempt 'rolling their own crypto', asking if Eliezer can poke the hole in this new procedure, and if Eliezer ever decides to just write serial online fiction until the world explodes humanity hasn't developed enough capacity to replace him.

[-]Rohin Shah3yΩ13230

(For object-level responses, see comments on parallel threads.)

I want to push back on an implicit framing in lines like:

there's some value to more people thinking thru / shooting down their own edge cases [...], instead of pushing the work to Eliezer.
people aren't updating on the meta-level point and continue to attempt 'rolling their own crypto', asking if Eliezer can poke the hole in this new procedure

This makes it sound like the rest of us don't try to break our proposals, push the work to Eliezer, agree with Eliezer when he finds a problem, and then not update that maybe future proposals will have problems.

Whereas in reality, I try to break my proposals, don't agree with Eliezer's diagnoses of the problems, and usually don't ask Eliezer because I don't expect his answer to be useful to me (and previously didn't expect him to respond). I expect this is true of others (like Paul and Richard) as well.

7Vaniver3y

Yeah, sorry about not owning that more, and for the frame being muddled. I don't endorse the "asking Eliezer" or "agreeing with Eliezer" bits, but I do basically think he's right about many object-level problems he identifies (and thus people disagreeing with him about that is not a feature) and think 'security mindset' is the right orientation to have towards AGI alignment. That hypothesis is a 'worry' primarily because asymmetric costs means it's more worth investigating than the raw probability would suggest. [Tho the raw probability of components of it do feel pretty substantial to me.] [EDIT: I should say I think ARC's approach to ELK seems like a great example of "people breaking their own proposals". As additional data to update on, I'd be interested in seeing, like, a graph of people's optimism about ELK over time, or something similar.]

[-]Rob Bensinger3yΩ7140

But also my sense is that there's some deep benefit from "having mainlines" and conversations that are mostly 'sentences-on-mainline'?

I agree with this. Or, if you feel ~evenly split between two options, have two mainlines and focus a bunch on those (including picking at cruxes and revising your mainline view over time).

But:

Like, it feels to me like Eliezer was generating sentences on his mainline, and Richard was responding with 'since you're being overly pessimistic, I will be overly optimistic to balance', with no attempt to have his response match his own mainline.

I do note that there are some situations where rushing to tell a 'mainline story' might be the wrong move:

Maybe your beliefs feel wildly unstable day-to-day -- because you're learning a lot quickly, or because it's just hard to know how to assign weight to the dozens of different considerations that bear on these questions. Then trying to take a quick snapshot of your current view might feel beside the point.
- It might even feel actively counterproductive, like rushing too quickly to impose meaning/structure on data when step one is to make sure you have the data properly loaded up in your head.
Maybe there are many scen

... (read more)

2[comment deleted]3y

[-]Peter Wildeford3yΩ13330

These conversations are great and I really admire the transparency. It's really nice to see discussions that normally happen in private happen instead in public where everyone can reflect, give feedback, and improve their own thoughts. On the other hand, the combined conversations combined to a decent-sized novel - LW says 198,846 words! Is anyone considering investing heavily in summarizing the content for people to get involved without having to read all that content?

[-]Rob Bensinger3yΩ7150

Echoing that I loved these conversations and I'm super grateful to everyone who participated — especially Richard, Paul, Eliezer, Nate, Ajeya, Carl, Rohin, and Jaan, who contributed a lot.

I don't plan to try to summarize the discussions or distill key take-aways myself (other than the extremely cursory job I did on https://intelligence.org/late-2021-miri-conversations/), but I'm very keen on seeing others attempt that, especially as part of a process to figure out their own models and do some evaluative work.

I think I'd rather see partial summaries/responses that go deep, instead of a more exhaustive but shallow summary; and I'd rather see summaries that center the author's own view (what's your personal take-away? what are your objections? which things were small versus large updates? etc.) over something that tries to be maximally objective and impersonal. But all the options seem good to me.

7Ben Pace3y

I chatted briefly the other day with Rob Bensinger about me turning them into a little book. My guess is I'd want to do something to compress especially the long Paul/Eliezer bet hashing out, that felt super long to me and not all worth the reading. Interested in other suggestions for compression. (This is not a commitment to do this, I probably won't.)

5Kenoubi3y

I wish you (or someone) would make a little book of this.

3Gyrodiot3y

The compression idea evokes Kaj Sotala's summary/analysis of the AI-Foom Debate (which I found quite useful at the time). I support the idea, especially given it has taken a while for the participants to settle on things cruxy enough to discuss and so on. Though I would also be interested in "look, these two disagree on that, but look at all the very fundamental things about AI alignment they agree on".

6Daniel Kokotajlo3y

Here is a heavily condensed summary of the takeoff speeds thread of the conversation, incorporating earlier points made by Hanson, Grace, etc. https://objection.lol/objection/3262835 :) (kudos to Ben Goldhaber for pointing me to it)

[-]So8res3yΩ16310

Question for Richard, Paul, and/or Rohin: What's a story, full of implausibly concrete details but nevertheless a member of some largish plausible-to-you cluster of possible outcomes, in which things go well? (Paying particular attention to how early AGI systems are deployed and to what purposes, or how catastrophic deployments are otherwise forstalled.)

[-]Rohin Shah3yΩ10130

I wrote this doc a couple of years ago (while I was at CHAI). It's got many rough edges (I think I wrote it in one sitting and never bothered to rewrite it to make it better), but I still endorse the general gist, if we're talking about what systems are being deployed to do and what happens amongst organizations. It doesn't totally answer your question (it's more focused on what happens before we get systems that could kill everyone), but it seems pretty related.

(I haven't brought it up before because it seems to me like the disagreement is much more in the "mechanisms underlying intelligence", which that doc barely talks about, and the stuff it does say feels pretty outdated; I'd say different things now.)

4[anonymous]3y

If I didn't miss anything and I'm understanding the scenario correctly, then for this part: I'd expect that interpretability tools, if they work, would tell you "yup, this AI is planning to kill you as soon as it possibly can", without giving you a way to fix that (that's robust to capability gains). Ie this story still seems to rely on an unexplained step that goes "... and a miracle occurs where we fundamentally figure out how to align AI just in the nick of time".

3Rohin Shah3y

Totally agreed that the doc does not address that argument. Quoting from my original comment:

[-]Ben Pace3yΩ9250

Eliezer and Nate, my guess is that most of your perspective on the alignment problem for the past several years has come from the thinking and explorations you've personally done, rather than reading work done by others.

But, if you have read interesting work by others that's changed your mind or given you helpful insights, what has it been? Some old CS textbook? Random Gwern articles? An economics textbook? Playing around yourself with ML systems?

[-]Donald Hobson3yΩ11220

One thing in the posts I found surprising was Eliezers assertion that you needed a dangerous superintelligence to get nanotech. If the AI is expected to do everything itself, including inventing the concept of nanotech, I agree that this is dangerously superintelligent.

However, suppose Alpha Quantum can reliably approximate the behaviour of almost any particle configuration. Not literally any, it can't run a quantum computer factorizing large numbers better than factoring algorithms, but enough to design a nanomachine. (It has been trained to approximate the ground truth of quantum mechanics equations, and it does this very well.)

For example, you could use IDA, start training to imitate a simulation of a handful of particles, then compose several smaller nets into one large one.

Add a nice user interface and we can drag and drop atoms.

You can add optimization, gradient descent trying to maximize the efficiency of a motor, or minimize the size of a logic gate. All of this is optimised to fit a simple equation, so assuming you don't have smart general mesaoptimizers forming, and deducing how to manipulate humans based on very little info about humans, you shoul... (read more)

3VojtaKovarik3y

(Not very sure I understood your description right, but here is my take:) * I think your proposal is not explaining some crucial steps, which are in fact hard. In particular, I understood it as "you have AI which can give you blueprints for nano sized machines". But I think we already have some blueprints, this isn't an issue. How we assemble them is an issue. * I expect that there will be more issues like this that you would find if you tried writing the plan in more detail. However, I share the general sentiment behind your post --- I also don't understand why you can't get some pivotal act by combining human intelligence with some narrow AI. I expect that Eliezer have tried to come up with such combinations and came away with some general takeaways on this being not realistic. But I haven't done this exercise, so it seems not obvious to me. Perhaps it would be beneficial if many more people tried doing the exercise and then communicated the takeaways.

2Rob Bensinger3y

I think it would be!

2Vaniver3y

Uh, how big do you think contemporary chips are?

1Donald Hobson3y

Like 10s of atoms across. So you aren't scaling down that much. (Most of your performance gains are in being able to stack your chips or whatever.

1Gram Stone3y

I got the impression Eliezer's claiming that a dangerous superintelligence is merely sufficient for nanotech. How would you save us with nanotech? It had better be good given all the hardware progress you just caused!

4Rob Bensinger3y

No, I'm pretty confident Eliezer thinks AGI is both necessary and sufficient for nanotech. (Realistically/probabilistically speaking, given plausible levels of future investment into each tech. Obviously it's not logically necessary or sufficient.) Cf. my summary of Nate's view in Nate's reply to Joe Carlsmith: (I read "sphexish" here as a special case of "narrow AI" / "shallow cognition", doing more things as a matter of pre-programmed reflex rather than as a matter of strategic choice.)

[-]Steven Byrnes3yΩ9190

I wrote Consequentialism & Corrigibility shortly after and partly in response to the first (Ngo-Yudkowsky) discussion. If anyone has an argument or belief that the general architecture / approach I have in mind (see the “My corrigibility proposal sketch” section) is fundamentally doomed as a path to corrigibility and capability—as opposed to merely “reliant on solving lots of hard-but-not-necessarily-impossible open problems”—I'd be interested to hear it. Thanks in advance. :)

[-]dxu3y110

After reading some of the newer MIRI dialogues, I'm less convinced than I once was that I know what "corrigibility" actually is. Could you say a few words about what kind of behavior you concretely expect to see from a "corrigible" agent, followed by how [you expect] those behaviors [to] fit into the "trajectory-constraining" framework you propose in your post?

EDIT: This is not purely a question for Steven, incidentally (or at least, the first half isn't); anyone else who wants to take a shot at answering should feel free to do so. In particular I'd be interested in hearing answers from Eliezer or anyone else historically involved in the invention of the term.

6Ben Pace3y

My understanding: a corrigible paperclip-maximizer does all the paperclip-maximizing, but then when you realize it's gonna end the world, you go to turn it off, and it doesn't stop you. It's corrigible!

3Algon3y

There are a bunch of different definitions, but if you're asking for Eliezer's version, then the arbital expoisition is quite good. N.B. we don't have a model for this sort of corrigibility. EDIT: Be warned, these are rough summaries of the defs. I'd ammend the CHAI def I cited to "the AI obeys more when it knows less, models you as more rational, and the downsides of disobedience are lesser". But people at CHAI have diverse views, so this is not the definitive CHAI take. Other definitions include some people at CHAI's definition (the AI obeys you whilst it doesn't know what its utility function is), the definition used in the reward tampering paper (near the same as EY's original def, barring the honesty clause, and formalised in a causal diagram setting), Stuart Armstrong's many definitions which most notably includes Utility Indifference (note the agent is NOT a standard R-maximiser) so it accepts having its utility function changed at a later time as you're going to compensate it for its loss in utility. So it is indiferent to the change (this doesn't mean it won't kill you for spare parts though). And TurnTrout has what looks like some interesting thoughts on the topic here but I haven't read those yet. Edit2: Paul thinks corrigibility has a simpler core than alignment, but is quite messy, and we won't get a crisp algorithm for it. But the intuition is the same as what Eliezer was pointing to, namely that the AI knows it should defer to the human, and will seek to preserve that deference in it and its offspring. Plus being honest and helpful. Here is a post where he rambles about it.

1awenonian3y

I'm a little confused what it hopes to accomplish. I mean, to start I'm a little confused by your example of "preferences not about future states" (i.e. 'the pizza shop employee is running around frantically, and I am laughing' is a future state). But to me, I'm not sure what the mixing of "paperclips" vs "humans remain in control" accomplishes. On the one hand, I think if you can specify "humans remain in control" safely, you've solved the alignment problem already. On another, I wouldn't want that to seize the future: There are potentially much better futures where humans are not in control, but still alive/free/whatever. (e.g. the Sophotechs in the Golden Oecumene are very much in control). On a third, I would definitely, a lot, very much, prefer a 3 star 'paperclips' and 5 star 'humans in control' to a 5 star 'paperclips' and a 3 star 'humans in control', even though both would average 4 stars?

2Steven Byrnes3y

In my post I wrote: “To be more concrete, if I’m deciding between two possible courses of action, A and B, “preference over future states” would make the decision based on the state of the world after I finish the course of action—or more centrally, long after I finish the course of action. By contrast, “other kinds of preferences” would allow the decision to depend on anything, even including what happens during the course-of-action.” So “the humans will ultimately wind up in control” would be a preference-over-future-states, and this preference would allow (indeed encourage) the AGI to disempower and later re-empower humans. By contrast, “the humans will remain in control” is not a pure preference-over-future-states, and relatedly does not encourage the AGI to disempower and later re-empower humans. If we knew exactly what long-term future we wanted, and we knew how to build an AGI that definitely also wanted that exact same long-term future, then we should certainly do that, instead of making a corrigible AGI. Unfortunately, we don't know those things right now, so under the circumstances, knowing how to make a corrigible AGI would be a useful thing to know how to do. Also, this is not a hyper-specific corrigibility proposal; it's really a general AGI-motivation-sculpting proposal, applied to corrigibility. So even if you're totally opposed to corrigibility, you can still take an interest in the question of whether or not my proposal is fundamentally doomed. Because I think everyone agrees that AGI-motivation-sculpting is necessary. It could be a weighted average. It could be a weighted average plus a nonlinear acceptability threshold on “humans in control”. It could be other things. I don't know; this is one of many important open questions. See discussion under “Objection 1” in my post.

3awenonian3y

Am I correct after reading this that this post is heavily related to embedded agency? I may have misunderstood the general attitudes, but I thought of "future states" as "future to now" not "future to my action." It seems like you couldn't possibly create a thing that works on the last one, unless you intend it to set everything in motion and then terminate. In the embedded agency sequence, they point out that embedded agents don't have well defined i/o channels. One way is that "action" is not a well defined term, and is often not atomic. It also sounds like you're trying to suggest that we should be judging trajectories, not states? I just want to note that this is, as far as I can tell, the plan: https://www.lesswrong.com/posts/K4aGvLnHvYgX9pZHS/the-fun-theory-sequence From the synopsis of High Challenge I'm not sure I interpret corrigibility as exactly the same as "preferring the humans remain in control" (I see you suggest this yourself in Objection 1, I wrote this before I reread that, but I'm going to leave it as is) and if you programmed that preference into a non-corrigible AI, it would still seize the future into states where the humans have to remain in control. Better than doom, but not ideal if we can avoid it with actual corrigibility. But I think I miscommunicated, because, besides the above, I agree with everything else in those two paragraphs. I think I maintain that this feels like it doesn't solve much. Much of the discussion in the Yudkowsky conversations was that there's a concern on how to point powerful systems in any direction. Your response to objection 1 admits you don't claim this solves that, but that's most of the problem. If we do solve the problem of how to point a system at some abstract concept, why would we choose "the humans remain in control" and not "pursue humanity's CEV"? Do you expect "the humans remain in control" (or the combination of concepts you propose as an alternative) to be easier to define? Easier enough to de

[-]Zack_M_Davis3y190

Question for anyone, but particularly interested in hearing from Christiano, Shah, or Ngo: any thoughts on what happens when alignment schemes that worked in lower-capability regimes fail to generalize to higher-capability regimes?

For example, you could imagine a spectrum of outcomes from "no generalization" (illustrative example: galaxies tiled with paperclips) to "some generalization" (illustrative example: galaxies tiled with "hedonium" human-ish happiness-brainware) to "enough generalization that existing humans recognizably survive, but something still went wrong from our current perspective" (illustrative examples: "Failed Utopia #4-2", Friendship Is Optimal, "With Folded Hands"). Given that not every biological civilization solves the problem, what does the rest of the multiverse look like? (How is measure distributed on something like my example spectrum, or whatever I should have typed instead?)

(Previous work: Yudkowsky 2009 "Value Is Fragile", Christiano 2018 "When Is Unaligned AI Morally Valuable?", Grace 2019 "But Exactly How Complex and Fragile?".)

[-]paulfchristiano3y350

When alignment schemes fail to scale, I think it typically means that they work while the system is unable to overpower/outsmart the oversight process, and then break down when the system becomes able to do so. I think that this usually results in the AI shifting from behavior that is mostly constrained by the training process to behavior that is mostly unconstrained (once they effectively disempower humans).

I think the results are relatively unlikely to be good in virtue of "the AI internalized something about our values, just not everything", and I'm pretty skeptical of recognizable "near miss" scenarios rather than AI gradually careening in very hard-to-predict directions with minimal connection with the surface features of the training process.

Overall I think that the most likely outcome is a universe that is orthogonal to anything we directly care about, maybe with a vaguely similar flavor owing to convergence depending on how AI motivations shake out. (But likely not close enough to feel great, and quite plausibly with almost no visible relation. Probably much more different from us than we are from aliens.)

I think it's fairly plausible that the results are OK just beca... (read more)

3Anirandis3y

Also, wouldn't you expect s-risks from this to be very unlikely by virtue of (1) civilizations like this being very unlikely to have substantial measure over the universe's resources, (2) transparency making bargaining far easier, and (3) few technologically advanced civilizations would care about humans suffering in particular as opposed to e.g. an adversary running emulations of their own species?

[-]Rohin Shah3y170

Basically agree with Paul, and I especially want to note that I've barely thought about it and so this would likely change a ton with more information. To put some numbers of my own:

"No generalization": 65%
"Some generalization": 5% (I don't actually have stories where this is an outcome; this is more like model uncertainty)
"Lots of generalization, but something went wrong": 30%

These are from my own perspective of what these categories mean, which I expect are pretty different from yours -- e.g. maybe I'm at ~2% that upon reflection I'd decide that hedonium is great and so that's actually perfect generalization; in the last category I include lots of worlds that I wouldn't describe as "existing humans recognizably survive", e.g. we decide to become digital uploads, then get lots of cognitive enhancements, throw away a bunch of evolutionary baggage, but also we never expand to the stars because AI has taken control of it and given us only Earth.

I think the biggest avenues for improving the answers would be to reflect more on the kindness + cooperation and acausal trade stories Paul mentions, as well as the possibility that a few AIs end up generalizing close to correctly and working ... (read more)

4Lukas_Gloor3y

This doesn't contradict anything you're saying but there's arguably a wager for thinking that we're on the knife-edge – our actions are more impactful if we are. [Edit to add point:] The degree to which any particular training approach generalizes is of course likely a fixed fact (like in the Lesswrong post you link to about fire). But different civilizations could try different training approaches, which produces heterogeneity for the multiverse.

[-]Gyrodiot3y170

I finished reading all the conversations a few hours ago. I have no follow-up questions (except maybe "now what?"), I'm still updating from all those words.

One except in particular, from the latest post, jumped at me (from Eliezer Yudkowsky, emphasis mine):

This is not aimed particularly at you, but I hope the reader may understand something of why Eliezer Yudkowsky goes about sounding so gloomy all the time about other people's prospects for noticing what will kill them, by themselves, without Eliezer constantly hovering over their shoulder every minute prompting them with almost all of the answer.

The past years or reading about alignment have left me with an intense initial distrust of any alignment research agenda. Maybe it's ordinary paranoia, maybe something more. I've not come up with any new ideas myself, and I'm not particularly confident in my ability to find flaws in someone else's proposal (what if I'm not smart enough to understand them properly? What if I make things even more confused and waste everyone's time?)

After thousands and thousands of lengthy conversations where it takes everyone ages to understand where threat models disagree, why some avenue of research is p... (read more)

[-]Signer3y150

Not sure if it's a right place to ask, instead of just googling it, but anyway: does anyone know what's the current state of AI security practices at DeepMind, OpenAI and other such places? Like, did they estimate probability of GPT-3 killing everyone before turning it on, do they have procedures for not turning something on, did they test these procedures by someone impersonating unaligned GPT and trying to manipulate researchers, things like that?

8Rohin Shah3y

No, I very strongly predict they did not do things like that. I expect they (perhaps implicitly) predicted with high confidence that GPT-3 would not have the capabilities needed to kill everyone.

3Signer3y

Do they have plans to do something in the future?

4Rohin Shah3y

I would assume that the safety teams plan to do this (certainly I plan to). It's less clear what the opinions are outside of the safety teams.

[-]Ben Pace3yΩ4140

Questions about the standard-university-textbook from the future that tells us how to build an AGI. I'll take answers on any of these!

Where is ML in this textbook? Is it under a section called "god-forsaken approaches" or does it play a key role? Follow-up: Where is logical induction?
If running superintelligent AGIs didn't kill you and death was cancelled in general, how long would it take you to write the textbook?
Is there anything else you can share about this textbook? Do you know any of the other chapter names?

[-]Vanessa Kosoy3yΩ15300

I'm going to try and write a table of contents for the textbook, just because it seems like a fun exercise.

Epistemic status: unbridled speculation

Volume I: Foundation

Preface [mentioning, ofc, the infamous incident of 2041]
Chapter 0: Introduction

Part I: Statistical Learning Theory

Chapter 1: Offline Learning [VC theory and Watanabe's singular learning theory are both special cases of what's in this chapter]
Chapter 2: Online Learning [infra-Bayesianism is introduced here, Garrabrant induction too]
Chapter 3: Reinforcement Learning
Chapter 4: Lifelong Learning [this chapter deals with traps, unobservable rewards and long-term planning]

Part II: Computational Learning Theory

Chapter 5: Algebraic Classes [the theory of SVMs is a special case of what's explained here]
Chapter 6: Circuits [learning various class of circuits]
Chapter 7: Neural Networks
Chapter 8: ???
Chapter 9: Reflective Learning [some version of Turing reinforcement learning comes here]

Part III: Universal Priors

Chapter 10: Solomonoff's Prior [including regret analysis using algorithmic statistics]
Chapter 11: Bounded Simplicity Priors
Chapter 12: ??? [might involve: causality, time hierarchies, logical langu

... (read more)

[-]paulfchristiano3yΩ9110

I don't think there is an "AGI textbook" any more than there is an "industrialization textbook." There are lots of books about general principles and useful kinds of machines. That said, if I had to make wild guesses about roughly what that future understanding would look like:

There is a recognizable concept of "learning" meaning something like "search for policies that perform well in past or simulated situations." That plays a large role, comparably important to planning or Bayesian inference. Logical induction is likely an elaboration of Bayesian inference that receives relatively little airtime except in specialized discussions.
This one is tougher given that I don't know what "the textbook" is. And I guess in the story all other humans are magically disappeared? If I was stuck with a single AWS cluster from 2022 and given unlimited time, I'd wildly guess that it would take me something between 1e4 and 1e8 years to create an autopoetic AI that obsoleted my own contributions (mostly because serial time is extremely valuable and I have a lot of compute). Writing the textbook does not seem like very much work after having done the deed?
I'd roughly guess big sections on learning, inference, planning, alignment, and clever algorithms for all of the above. I'd guess maybe 50% of content is smart versions of stuff we know now and 50% is stuff we didn't figure out at the time, but it depends a lot on how you define this textbook.

3Rohin Shah3y

I'm mostly going to answer assuming that there's not some incredibly different paradigm (i.e. something as different from ML as ML is from expert systems). I do think the probability of "incredibly different paradigm" is low. I'm also going to answer about the textbook at, idk, the point at which GDP doubles every 8 years. (To avoid talking about the post-Singularity textbook that explains how to build a superintelligence with clearly understood "intelligence algorithms" that can run easily on one of today's laptops, which I know very little about.) I think I roughly agree with Paul if you are talking about the textbook that tells us how to build the best systems for the tasks that we want to do. (Analogy: today's textbook for self-driving cars.) That being said, I think that much of the improvement over time will be driven by improvements specifically in ML. (Analogy: today's textbook for deep learning.) So we can talk about that textbook as well. 1. It's a textbook that's entirely about "finding good programs through a large, efficient search with a stringent goal", which we currently call ML. The content may be primarily some new approach for achieving this, with neural nets being a historical footnote, or it might be entirely about neural nets (though presumably with new architectures or other changes from today). Logical induction doesn't appear in the textbook. 2. Jeez, who knows. If I intuitively query my brain here, it mostly doesn't have an answer; a thousand vs. million vs. billion years don't really change my intuitive predictions about what I'd get done. So we can instead back it out from other estimates. Given timelines of 10^1 - 10^2 years, and, idk, ~10^6 humans working on the problem near the end, seems like I'm implicitly predicting ~10^7 human-years of effort in our actual world. Then you have to adjust for a ton of factors, e.g. my quality relative to the average, the importance of serial thinking time, the benefit that real-world humans get

2Richard_Ngo3y

1. Where is ML in this textbook? Is it under a section called "god-forsaken approaches" or does it play a key role? Follow-up: Where is logical induction? Key role, but most current ML is in the "applied" section, where the "theory" section instead explains the principles by which neural nets (or future architectures) work on the inside. Logical induction is a sidebar at some point explaining the theoretical ideal we're working towards, like I assume AIXI is in some textbooks. 1. Is there anything else you can share about this textbook? Do you know any of the other chapter names? Planning, Abstraction, Reasoning, Self-awareness.

[-]Noah Topper3y140

Eliezer, do you have any advice for someone wanting to enter this research space at (from your perspective) the eleventh hour? I’ve just finished a BS in math and am starting a PhD in CS, but I still don’t feel like I have the technical skills to grapple with these issues, and probably won’t for a few years. What are the most plausible routes for someone like me to make a difference in alignment, if any?

[-]Eliezer Yudkowsky3y160

I don't have any such advice at the moment. It's not clear to me what makes a difference at this point.

[-]rank-biserial3y230

What do you think about unironically hiring Terry Tao?

[-]Eliezer Yudkowsky3y590

We'd absolutely pay him if he showed up and said he wanted to work on the problem. Every time I've asked about trying anything like this, all the advisors claim that you cannot pay people at the Terry Tao level to work on problems that don't interest them. We have already extensively verified that it doesn't particularly work for eg university professors.

[-]Tomás B.3y310

Every time I've asked about trying anything like this, all the advisors claim that you cannot pay people at the Terry Tao level to work on problems that don't interest them.

As I am sure you would agree, Neumann/Tao-level people are a very different breed from even very, very, very good professors. It is plausible they are significantly more sane than the average genius.

Given the enormous glut of money in EA trying to help here and the terrifying thing where a lot of the people who matter have really short timelines, I think it is worth testing this empirically with Tao himself and Tao-level people.

It is worth noting that Neumann occasionally did contract work for extraordinary sums.

-5TAG3y

[-]25Hour3y230

I'm not sure whether the unspoken context of this comment is "We tried to hire Terry Tao and he declined, citing lack of interest in AI alignment" vs "we assume, based on not having been contacted by Terry Tao, that he is not interested in AI alignment."

If the latter: the implicit assumption seems to be that if Terry Tao would find AI alignment to be an interesting project, we should strongly expect him to both know about it and have approached MIRI regarding it, neither which seems particularly likely given the low public profile of both AI alignment in general and MIRI in particular.

If the former: bummer.

[-]WilliamKiely3y18