Seems fair. I'm similarly conflicted. In truth, both the generalization-focused path and the objective-focused path look a bit doomed to me.
Great, I feel pretty resolved about this conversation now.
I would further add that looking for difficulties created by the simplification seems very intellectually productive. (Solving "embedded agency problems" seems to genuinely allow you to do new things, rather than just soothing philosophical worries.) But yeah, I would agree that if we're defining mesa-objective anyway, we're already in the business of assuming some agent/environment boundary.
(see the unidentifiability in IRL paper)
Ah, I wasn't aware of this!
Btw, if you're aware of any counterpoints to this — in particular anything like a clearly worked-out counterexample showing that one can't carve up a world, or recover a consistent utility function through this sort of process — please let me know. I'm directly working on a generalization of this problem at the moment, and anything like that could significantly accelerate my execution.
I'm not sure what would constitute a clearly-worked counterexample. To me, a high reliance on an agent/worl... (read more)
Right, exactly. (I should probably have just referred to that, but I was trying to avoid reference-dumping.)
I pretty strongly endorse the new diagram with the pseudo-equivalences, with one caveat (much the same comment as on your last post)... I think it's a mistake to think of only mesa-optimizers as having "intent" or being "goal-oriented" unless we start to be more inclusive about what we mean by "mesa-optimizer" and "mesa-objective." I don't think those terms as defined in RFLO actually capture humans, but I definitely want to say that we're "goal-oriented" and have "intent."But the graph structure makes perfect sense, I just am doing the mental substitution
I pretty strongly endorse the new diagram with the pseudo-equivalences, with one caveat (much the same comment as on your last post)... I think it's a mistake to think of only mesa-optimizers as having "intent" or being "goal-oriented" unless we start to be more inclusive about what we mean by "mesa-optimizer" and "mesa-objective." I don't think those terms as defined in RFLO actually capture humans, but I definitely want to say that we're "goal-oriented" and have "intent."
But the graph structure makes perfect sense, I just am doing the mental substitution
Maybe a very practical question about the diagram: is there a REASON for there to be no "sufficient together" linkage from "Intent Alignment" and "Robustness" up to "Behavioral Alignment"?
Leaning hard on my technical definitions:
Robustness: Performing well on the base objective in a wide range of circumstances.Intent Alignment: A model is intent-aligned if it has a mesa-objective, and that mesa-objective is aligned with humans. (Again, I don't want to get into exactly what "alignment" means.)
These two together do not quite imply behavioral alignment, becau... (read more)
I think there's another reason why factorization can be useful here, which is the articulation of sub-problems to try.
For example, in the process leading up to inventing logical induction, Scott came up with a bunch of smaller properties to try for. He invented systems which got desirable properties individually, then growing combinations of desirable properties, and finally, figured out how to get everything at once. However, logical induction doesn't have parts corresponding to those different subproblems.
It can be very useful to individually achieve, sa... (read more)
I agree that we need a notion of "intent" that doesn't require a purely behavioral notion of a model's objectives, but I think it should also not be limited strictly to mesa-optimizers, which neither Rohin nor I expect to appear in practice. (Mesa-optimizers appear to me to be the formalization of the idea "what if ML systems, which by default are not well-described as EU maximizers, learned to be EU maximizers?" I suspect MIRI people have some unshared intuitions about why we might expect this, but I currently don't have a good reason to believe this.)
For... (read more)
They can't? Why not?
I meant to invoke a no-free-lunch type intuition; we can always construct worlds where some particular tool isn't useful.
My go-to would be "a world that checks what an InfraBayesian would expect, and does the opposite". This is enough for the narrow point I was trying to make (that InfraBayes does express some kind of regularity assumption about the world), but it's not very illustrative or compelling for my broader point (that InfraBayes plausibly addresses your concerns about learning theory). So I'll try to tell a better stor... (read more)
No such thing is possible in reality, as an agent cannot exist without its environment, so why shouldn't we talk about the mesa-objective being over a perturbation set, too, just that it has to be some function of the model's internal features?
This makes some sense, but I don't generally trust some "perturbation set" to in fact capture the distributional shift which will be important in the real world. There has to at least be some statement that the perturbation set is actually quite broad. But I get the feeling that if we could make the right statement there, we would understand the problem in enough detail that we might have a very different framing. So, I'm not sure what to do here.
Great! I feel like we're making progress on these basic definitions.
InfraBayes doesn't look for the regularity in reality that NNs are taking advantage of, agreed. But InfraBayes is exactly about "what kind of regularity assumptions can we realistically make about reality?" You can think of it as a reaction to the unrealistic nature of the regularity assumptions which Solomonoff induction makes. So it offers an answer to the question "what useful+realistic regularity assumptions could we make?"
The InfraBayesian answer is "partial models". IE, the idea that even if reality cannot be completely described by usable models, pe... (read more)
I like the addition of the pseudo-equivalences; the graph seems a lot more accurate as a representation of my views once that's done.
But it seems to me that there's something missing in terms of acceptability.
The definition of "objective robustness" I used says "aligns with the base objective" (including off-distribution). But I think this isn't an appropriate representation of your approach. Rather, "objective robustness" has to be defined something like "generalizes acceptably". Then, ideas like adversarial training and checks and balances make sense as ... (read more)
All of that made perfect sense once I thought through it, and I tend to agree with most it. I think my biggest disagreement with you is that (in your talk) you said you don't expect formal learning theory work to be relevant. I agree with your points about classical learning theory, but the alignment community has been developing basically-classical-learning-theory tools which go beyond those limitations. I'm optimistic that stuff like Vanessa's InfraBayes could help here.
Granted, there's a big question of whether that kind of thing can be competitive. (Although there could potentially be a hybrid approach.)
I've watched your talk at SERI now.
One question I have is how you hope to define a good notion of "acceptable" without a notion of intent. In your talk, you mention looking at why the model does what it does, in addition to just looking at what it does. This makes sense to me (I talk about similar things), but, it seems just about as fraught as the notion of mesa-objective:
(Meta: was this meant to be a question?)
I originally conceived of it as such, but in hindsight, it doesn't seem right.
In contrast, the generalization-focused approach puts less emphasis on the assumption that the worst catastrophes are intentional.I don't think this is actually a con of the generalization-focused approach.
In contrast, the generalization-focused approach puts less emphasis on the assumption that the worst catastrophes are intentional.
I don't think this is actually a con of the generalization-focused approach.
By no means did I intend it to be a con. I'll try to edit to clarify. I think it is a real pro of the generalization-focused approach that it does not rely on models having mesa-objectives (putting it in Evan's terms, there is a real poss... (read more)
Are you the historical origin of the robustness-centric approach?
Idk, probably? It's always hard for me to tell; so much of what I do is just read what other people say and make the ideas sound sane to me. But stuff I've done that's relevant:
If there were a "curated posts" system on the alignment forum, I would nominate this for curation. I think it's a great post.
All of which I really should have remembered, since it's all stuff I have known in the past, but I am a doofus. My apologies.(But my error wasn't being too mired in EDT, or at least I don't think it was; I think EDT is wrong. My error was having the term "counterfactual" too strongly tied in my head to what you call linguistic counterfactuals. Plus not thinking clearly about any of the actual decision theory.)
All of which I really should have remembered, since it's all stuff I have known in the past, but I am a doofus. My apologies.
(But my error wasn't being too mired in EDT, or at least I don't think it was; I think EDT is wrong. My error was having the term "counterfactual" too strongly tied in my head to what you call linguistic counterfactuals. Plus not thinking clearly about any of the actual decision theory.)
I'm glad I pointed out the difference between linguistic and DT counterfactuals, then!
It still feels to me as if your proof-based agents are unrealis
It's obvious how ordinary conditionals are important for planning and acting (you design a bridge so that it won't fall down if someone drives a heavy lorry across it; you don't cross a bridge because you think the troll underneath will eat you if you cross), but counterfactuals? I mean, obviously you can put them in to a particular problem
All the various reasoning behind a decision could involve material conditionals, probabilistic conditionals, logical implication, linguistic conditionals (whatever those are), linguistic counterfactuals, decision-theoret... (read more)
Agreed. The asymmetry needs to come from the source code for the agent.
In the simple version I gave, the asymmetry comes from the fact that the agent checks for a proof that x>y before checking for a proof that y>x. If this was reversed, then as you said, the Lobian reasoning would make the agent take the 10, instead of the 5.
In a less simple version, this could be implicit in the proof search procedure. For example, the agent could wait for any proof of the conclusion x>y or y>x, and make a decision based on whichever happened first. Then ther... (read more)
While I agree that the algorithm might output 5, I don't share the intuition that it's something that wasn't 'supposed' to happen, so I'm not sure what problem it was meant to demonstrate.
OK, this makes sense to me. Instead of your (A) and (B), I would offer the following two useful interpretations:
1: From a design perspective, the algorithm chooses 5 when 10 is better. I'm not saying it has "computed argmax incorrectly" (as in your A); an agent design isn't supposed to compute argmax (argmax would be insufficient to solve this problem, because we're not g... (read more)
Yep, agreed. I used the language "false antecedents" mainly because I was copying the language in the comment I replied to, but I really had in mind "demonstrably false antecedents".
Yeah, interesting. I don't share your intuition that nested counterfactuals seem funny. The example you give doesn't seem ill-defined due to the nesting of counterfactuals. Rather, the antecedent doesn't seem very related to the consequent, which generally has a tendency to make counterfactuals ambiguous. If you ask "if calcium were always ionic, would Nixon have been elected president?" then I'm torn between three responses:
I agree that much of what's problematic about the example I gave is that the "inner" counterfactuals are themselves unclear. I was thinking that this makes the nested counterfactual harder to make sense of (exactly because it's unclear what connection there might be between them) but on reflection I think you're right that this isn't really about counterfactual nesting and that if we picked other poorly-defined (non-counterfactual) propositions we'd get a similar effect: "If it were morally wrong to eat shellfish, would humans Really Truly Have Free Will?"... (read more)
Hmm. I'm not following. It seems like you follow the chain of reasoning and agree with the conclusion:
The algorithm doesn't try to select an assignment with largest U(), but rather just outputs 5 if there's a valid assignment with x>y, and 10 otherwise. Only p2 fulfills the condition, so it outputs 5.
This is exactly the point: it outputs 5. That's bad! But the agent as written will look perfectly reasonable to anyone who has not thought about the spurious proof problem. So, we want general tools to avoid t... (read more)
Ah, I wasn't strongly differentiating between the two, and was actually leaning toward your proposal in my mind. The reason I was not differentiating between the two was that the probability of C(A|B) behaves a lot like the probabilistic value of Prc(A|B). I wasn't thinking of nearby-world semantics or anything like that (and would contrast my proposal with such a proposal), so I'm not sure whether the C(A|B) notation carries any important baggage beyond that. However, I admit it could be an important distinction; C(A|B) is itself a proposition, which can ... (read more)
I never found Stalnaker's thesis at all plausible, not because I'd thought of the ingenious little calculation you give but because it just seems obviously wrong intuitively. But I suppose if you don't have any presuppositions about what sort of notion an implication is allowed to be, you don't get to reject it on those grounds. So I wasn't really entitled to say "Pr(A|B) is not the same thing as Pr(B=>A) for any particular notion of implication", since I hadn't thought of that calculation.
Anyway, I have just the same sense of obvious wrongness about th... (read more)
I should! But I've got a lot of things to write up!
It also needs a better name, as there have been several things termed "weak logical induction" over time.
In between … well … in between, we're navigating treacherous waters …
Right, I basically agree with this picture. I might revise it a little:
I don't believe that LI provides such a Pareto improvement, but I suspect that there's a broader theory which contains the two.
Overall, I place much less weight on arguments that revolve around the presumed nature of human values compared to arguments grounded in abstract reasoning about rational agents.
Ah. I was going for the human-values argument because I thought you might not appreciate the rational-agent argument. After all, who cares what general rational agents can value, if human values happen to be well-represented by infrabayes?
But for general ra... (read more)
I agree inasmuch as we actually can model this sort of preferences, for a sufficiently strong meaning of "model". I feel that it's much harder to be confident about any detailed claim about human values than about the validity of a generic theory of rationality. Therefore, if the ultimate generic theory of rationality imposes some conditions on utility functions (while still leaving a very rich space of different utility functions), that will lead me to try formalizing human values within those constraints. Of course, given a candidate theory, we should po
If PA is consistent, then the agent cannot prove U = -10 (or anything else inconsistent) under the assumption that the agent already crossed, and therefore Löb's theorem fails to apply. In this case, there is no weird certainty that crossing is doomed.
I think this is the wrong step. Why do you think this? Just because PA is consistent doesn't mean you can't prove weird things under assumption. Look at the structure of the proof. You're objecting to an assumption. ("Suppose PA proves that crossing -> U=-10") That's a pretty weird way to object to a proof. I'm allowed to make any assumptions I like.
My guess is that you are wrestling with Lobs theorem itself. Lobs theorem is pretty weird!
It seems to me that the last paragraph should update you to thinking that this plan is no worse than the default. IE: yes, this plan creates additional risk because there are complicated pathways a malign gpt-n could use to get arbitrary code run on a big computer. But if people are giving it that chance anyway, it does seem like a small increase in risk with a large potential gain. (Small, not zero, for the chance that your specific gpt-n instance somehow becomes malign when others are safe, eg if something about the task actually activated a subtle malignancy not present during other tasks).
So for me a crux would be, if it's not malign, how good could we expect the papers to actually be?
First, I'm not sure exactly why you think this is bad. Care to say more? My guess is that it just doesn't fit the intuitive notion that updates should be heading toward some state of maximal knowledge. But we do fit this intuition in other ways; specifically, logical inductors eventually trust their future opinions more than their present opinions.
Personally, I found this result puzzling but far from damning.
Second, I've actually done some unpublished work on this. There is a variation of the logical induction criterion which is more relaxed (admits more t... (read more)
So it's still in the observation-utility paradigm I think, or at least it seems to me that it doesn't have an automatic incentive to wirehead. It could want to wirehead, if the value function winds up seeing wireheading as desirable for any reason, but it doesn't have to. In the human example, some people are hedonists, but others aren't.
All sounds perfectly reasonable. I just hope you recognize that it's all a big mess (because it's difficult to see how to provide evidence in a way which will, at least eventually, rule out the wireheading hypothesis or an... (read more)
OK, so, here is a question.
The abstract theory of InfraBayes (like the abstract theory of Bayes) elides computational concerns.
In reality, all of ML can more or less be thought of as using a big search for good models, where "good" means something approximately like MAP, although we can also consider more sophisticated variational targets. This introduces two different types of approximation:
What we want out of InfraBayes is a bounded regret guarantee (in settings ... (read more)
My hope is that we will eventually have computationally feasible algorithms that satisfy provable (or at least conjectured) infra-Bayesian regret bounds for some sufficiently rich hypothesis space. Currently, even in the Bayesian case, we only have such algorithms for poor hypothesis spaces, such as MDPs with a small number of states. We can also rule out such algorithms for some large hypothesis spaces, such as short programs with a fixed polynomial-time bound. In between, there should be some hypothesis space which is small enough to be feasible and rich... (read more)
What I'm referring to is that LI given a notion of rational uncertain expectation for the procrastination paradox -- so, less a positive result, more a framework for thinking about what behavior is reasonable.
However, I also think LIDT solves the problem in practical terms:
Just want to note that although it's been a week this is still in my thoughts, and I intend to get around to continuing this conversation... but possibly not for another two weeks.
I think let's step back for a second, though. Suppose you were in the epistemic position "yes, this works in theory, with the realizability assumption, with no computational slowdown over MAP, but having spent 2-10 hours trying to figure out how to distill a neural network's epistemic uncertainty/submodel-mismatch, and having come up blank..." what's the conclusion here? I don't think it's "my main guess is that there's no way to apply this in practice".
A couple of separate points:
The continuity property is really important.
Thanks for the extensive reply, and sorry for not getting around to it as quickly as I replied to some other things!
I am sorry for the critical framing, in that it would have been more awesome to get a thought-dumb of ideas for research directions from you, rather than a detailed defense of your existing work. But of course existing work must be judged, and I felt I had remained quiet about my disagreement with you for too long.
Comparing the consensus algorithm with (pure, idealized) MAP, 1) it is no slower, and 2) the various corners that can be cut for M
No, not prosaic, that particular comment was referring to the "brain-like AGI" story in my head...
Ah, ok. It sounds like I have been systematically mis-perceiving you in this respect.
By contrast, I haven't written quite as much about the ways that my (current) brain-like AGI story is non-prosaic. And a big one is that I'm thinking that there would be a hardcoded (by humans) inference algorithm that looks like (some more complicated cousin of) PGM belief propagation.
I would have been much more interested in your posts in the past if you had emphasized this ... (read more)
What I mean is that when I think about inner alignment issues, I actually think of learned goal-directed models instead of learned inner optimizers. In that context, the former includes the latter. But I also expect that relatively powerful goal-directed systems can exist without a powerful simple structure like inner optimization, and that we should also worry about those.That's one way in which I expect deconfusing goal-directedness to help here: by replacing a weirdly-defined subset of the models we should worry about by what I expect to be the full set
What I mean is that when I think about inner alignment issues, I actually think of learned goal-directed models instead of learned inner optimizers. In that context, the former includes the latter. But I also expect that relatively powerful goal-directed systems can exist without a powerful simple structure like inner optimization, and that we should also worry about those.
That's one way in which I expect deconfusing goal-directedness to help here: by replacing a weirdly-defined subset of the models we should worry about by what I expect to be the full set
Your examples in the other comment do feel closely related to your ideas on learning normativity, whereas inner agency problems do not feel particularly related to that (or at least not any more so than anything else is related to normativity).
Could you elaborate on that? I do think that learning-normativity is more about outer alignment. However, some ideas might cross-apply.
It feels like "optimization under uncertainty" is not quite the right name for the thing you're trying to point to with that phrase, and I think your explanations would make more sens
Now I have another question: how does logical induction arbitrage against contradiction? The bet on a pays $1 if a is proved. The bet on ~a pays $1 if not-a is proved. But the bet on ~a isn't "settled" when a is proved - why can't the market just go on believing its .7? (Likely this is related to my confusion with the paper).
Again, my view may have drifted a bit from the LI paper, but the way I think about this is that the market maker looks at the minimum amount of money a trader has "in any world" (in the sense described in my other comment). This exclud... (read more)
On each day, the reasoner receives 50¢ from T, but after day t, the reasoner must pay $1 every day thereafter.
Hm. It's a bit complicated and there are several possible ways to set things up. Reading that paragraph, I'm not sure about this sentence either.
In the version I was trying to explain, where traders are "forced to sell" every morning before the day of trading begins, the reasoner would receive 50¢ from the trader every day, but would return that money next morning. Also, in the version I was describing, the reasoner is forced to set the price to $1... (read more)
I'm also sceptical of optimality results. When you're doing subjective probability, any method you come up with will be proven optimal relative to its own prior - the difference between different subjective methods is only in their ontology, and the optimality results don't protect you against mistakes there. Also, when you're doing subjectivism, and it turns out the methods required to reach some optimality condition aren't subjectively optimal, you say "Don't be a stupid frequentist and do the subjectively optimal thing instead". So, your bottom line is
What makes you think that theres a "right" prior? You want a "good" learning mechanism for counterfactuals. To be good, such a mechanism would have to learn to make the inferences we consider good, at least with the "right" prior. But we can't pinpoint any wrong inference in Troll Bridge. It doesn't seem like whats stopping us from pinpointing the mistake in Troll Bridge is a lack of empirical data. So, a good mechanism would have to learn to be susceptible to Troll Bridge, especially with the "right" prior. I just don't see what would be a good reason for
To me, the post as written seems like enough to spell out my optimism... there multiple directions for formal work which seem under-explored to me. Well, I suppose I didn't focus on explaining why things seem under-explored. Hopefully the writeup-to-come will make that clear.
I agree with much of this. I over-sold the "absence of negative story" story; of course there has to be some positive story in order to be worried in the first place. I guess a more nuanced version would be that I am pretty concerned about the broadest positive story, "mesa-optimizers are in the search space and would achieve high scores in the training set, so why wouldn't we expect to see them?" -- and think more specific positive stories are mostly of illustrative value, rather than really pointing to gears that I expect to be important. (With the excep... (read more)