All of abramdemski's Comments + Replies

An Orthodox Case Against Utility Functions

(I don't follow it all, for instance I don't recall why it's important that the former view assumes that utility is computable.)

Partly because the "reductive utility" view is made a bit more extreme than it absolutely had to be. Partly because I think it's extremely natural, in the "LessWrong circa 2014 view", to say sentences like "I don't even know what it would mean for humans to have uncomputable utility functions -- unless you think the brain is uncomputable". (I think there is, or at least was, a big overlap between the LW crowd and the set of people... (read more)

Are limited-horizon agents a good heuristic for the off-switch problem?

I think we could get a GPT-like model to do this if we inserted other random sequences, in the same way, in the training data; it should learn a pattern like "non-word-like sequences that repeat at least twice tend to repeat a few more times" or something like that.

GPT-3 itself may or may not get the idea, since it does have some significant breadth of getting-the-idea-of-local-patterns-its-never-seen-before.

So I don't currently see what your experiment has to do with the planning-ahead question.

I would say that the GPT training process has no "inherent" p... (read more)

There is essentially one best-validated theory of cognition.

I think maybe our disagreement is about how good/useful of an overarching model ACT-R is? It's definitely not like in physics, where some overarching theories are widely accepted (e.g. the standard model) even by people working on much more narrow topics -- and many of the ones that aren't (e.g. string theory) are still widely known about and commonly taught. The situation in cog sci (in my view, and I think in many people's views?) is much more that we don't have an overarching model of the mind in anywhere close to the level of detail/mechanistic specifi

... (read more)
There is essentially one best-validated theory of cognition.

I think my post (at least the title!) is essentially wrong if there are other overarching theories of cognition out there which have similar track records of matching data. Are there?

By "overarching theory" I mean a theory which is roughly as comprehensive as ACT-R in terms of breadth of brain regions and breadth of cognitive phenomena.

As someone who has also done grad school in cog-sci research (but in a computer science department, not a psychology department, so my knowledge is more AI focused), my impression is that most psychology research isn't about... (read more)

Thanks for the thoughtful response, that perspective makes sense. I take your point that ACT-R is unique in the ways you're describing, and that most cognitive scientists are not working on overarching models of the mind like that. I think maybe our disagreement is about how good/useful of an overarching model ACT-R is? It's definitely not like in physics, where some overarching theories are widely accepted (e.g. the standard model) even by people working on much more narrow topics -- and many of the ones that aren't (e.g. string theory) are still widely k... (read more)

There is essentially one best-validated theory of cognition.

This lines up fairly well with how I've seen psychology people geek out over ACT-R. That is: I had a psychology professor who was enamored with the ability to line up programming stuff with neuroanatomy. (She didn't use it in class or anything, she just talked about it like it was the most mind blowing stuff she ever saw as a research psychologist, since normally you just get these isolated little theories about specific things.)

And, yeah, important to view it as a programming language which can model a bunch of stuff, but requires fairly extensive user in... (read more)

There is essentially one best-validated theory of cognition.

I think that's not quite fair. ACT-R has a lot to say about what kinds of processing are happening, as well. Although, for example, it does not have a theory of vision (to my limited understanding anyway), or of how the full motor control stack works, etc. So in that sense I think you are right.

What it does have more to say about is how the working memory associated with each modality works: how you process information in the various working memories, including various important cognitive mechanisms that you might not otherwise think about. In this sense, it's not just about interconnection like you said.

0Jon Garcia1moSo essentially, which types of information get routed for processing to which areas during the performance of some behavioral or cognitive algorithm, and what sort of processing each module performs?
Are limited-horizon agents a good heuristic for the off-switch problem?

We also know how to implement it today. 

I would argue that inner alignment problems mean we do not know how to do this today. We know how to limit the planning horizon for parts of a system which are doing explicit planning, but this doesn't bar other parts of the system from doing planning. For example, GPT-3 has a time horizon of effectively one token (it is only trying to predict one token at a time). However, it probably learns to internally plan ahead anyway, just because thinking about the rest of the current sentence (at least) is useful for th... (read more)

4davidad1moI’m curious to dig into your example. * Here’s an experiment that I could imagine uncovering such internal planning: * make sure the corpus has no instances of a token “jrzxd”, then * insert long sequences of “jrzxd jrzxd jrzxd … jrzxd” at random locations in the middle of sentences (sort of like introns), * then observe whether the trained model predicts “jrzxd” with greater likelihood than its base rate (which we’d presume is because it’s planning to take some loss now in exchange for confidently predicting more “jrzxd”s to follow). * I think this sort of behavior could be coaxed out of an actor-critic model (with hyperparameter tuning, etc.), but not GPT-3. GPT-3 doesn’t have any pressure towards a Bellman-equation-satisfying model, where future reward influences current output probabilities. * I’m curious if you agree or disagree and what you think I’m missing.
Are limited-horizon agents a good heuristic for the off-switch problem?

Imagine a spectrum of time horizons (and/or discounting rates), from very long to very short.

Now, if the agent is aligned, things are best with an infinite time horizon (or, really, the convergently-endorsed human discounting function; or if that's not a well-defined thing, whatever theoretical object replaces it in a better alignment theory). As you reduce the time horizon, things get worse and worse: the AGI willingly destroys lots of resources for short-term prosperity.

At some point, this trend starts to turn itself around: the AGI becomes so shortsight... (read more)

Knowledge is not just mutual information

Recently I have been thinking that we should in fact use "really basic" definitions, EG "knowledge is just mutual information", and also other things with a general theme of "don't make agency so complicated".  The hope is to eventually be able to build up to complicated types of knowledge (such as the definition you seek here), but starting with really basic forms. Let me see if I can explain.

First, an ontology is just an agents way of organizing information about the world. These can take lots of forms and I'm not going to constrain it to any partic... (read more)

1Adam Shimi2moSo, I'm trying to interpret your proposal from an epistemic strategy perspective — asking how are you trying to produce knowledge. It sounds to me like you're proposing to start with very general formalization with simple mathematical objects (like objectivity being a sort of function, and participating in a goal increasing the measure on the states satisfying the predicate). Then, when you reach situations where the definitions are not constraining enough, like what Alex describes, you add further constraints on these objects? I have trouble understanding how different it is from the "standard way" Alex is using of proposing a simple definition, finding where it breaks, and then trying to refine it and break it again. Rince and repeat. Could you help me with what you feel are the main differences?
2Alex Flint2moYep I'm with you here Yeah I very much agree with justifying the use of 3rd person perspectives on practical grounds. Well if we are choosing to work with third-person perspectives then maybe we don't need first person perspectives at all. We can describe gravity and entropy without any first person perspectives at all, for example. I'm not against first person perspectives, but if we're working with third person perspectives then we might start by sticking to third person perspectives exclusively. Yeah right. A screw that fits into a hole does have mutual information with the hole. I like the idea that knowledge is about the capacity to harmonize within a particular environment because it might avoid the need to define goal-directedness. The only problem is that now we have to say what a goal predicate is. Do you have a sense of how to do that? I have also come to the conclusion that knowledge has a lot to do with being useful in service of a goal, and that then requires some way to talk about goals and usefulness. I very much resonate with keeping it as simple as possible, especially when doing this kind of conceptual engineering, which can become so lost. I have been grounding my thinking in wanting to know whether or not a certain entity in the world has an understanding of a certain phenomenon, in order to use that to overcome the deceptive misalignment problem. Do you also have go-to practical problems against which to test these kinds of definitions?
Troll Bridge

Suppose instead the crossing counterfactual results in a utility greater than -10 utility. This seems very strange. By assumption, it's provable using the AI's proof system that . And the AI's counterfactual environment is supposed to line up with reality.

Right. This is precisely the sacrifice I'm making in order to solve Troll Bridge. Something like this seems to be necessary for any solution, because we already know that if your expectations of consequences entirely respect entailment, you'll fall prey to the Troll Bridge! In fact, y... (read more)

Troll Bridge

I'll talk about some ways I thought of potentially formalizing, "stop thinking if it's bad".

If your point is that there are a lot of things to try, I readily accept this point, and do not mean to argue with it. I only intended to point out that, for your proposal to work, you would have to solve another hard problem.

One simple way to try to do so is to have an agent using regular evidential decision theory but have a special, "stop thinking about this thing" action that it can take. Every so often, the agent considers taking this action using regular evide

... (read more)
Troll Bridge

You say that a "bad reason" is one such that the agents the procedure would think is bad.

To elaborate a little, one way we could think about this would be that "in a broad variety of situations" the agent would think this property sounded pretty bad.

For example, the hypothetical "PA proves " would be evaluated as pretty bad by a proof-based agent, in many situations; it would not expect its future self to make decisions well, so, it would often have pretty poor performance bounds for its future self (eg the lowest utility available in the given scena... (read more)

1Chantiel3moOh, I'm sorry; you're right. I messed up on step two of my proposed proof that your technique would be vulnerable to the same problem. However, it still seems to me that agents using your technique would also be concerning likely to fail to cross, or otherwise suffer from other problems. Like last time, suppose ⊢(A=′Cross′⟹U=−10) and that A=′Cross′. So if the agent decides to cross, it's either because of the chicken rule, because not crossing counterfactually results in utility ≤ -10, or because crossing counterfactually results in utility greater than -10. If the agent crosses because of the chicken rule, then this is a bad reason, so the bridge will blow up. I had already assumed that not crossing counterfactually results in utility greater than -10, so it can't be the middle case. Suppose instead the crossing counterfactual results in a utility greater than -10 utility. This seems very strange. By assumption, it's provable using the AI's proof system that (A=′Cross⟹U=−10). And the AI's counterfactual environment is supposed to line up with reality. So, in other words, the AI has decided to cross and has already proven that crossing entails it will get -10 utility. And if the counterfactual environment assigns greater than -10 utility, then that counterfactual environment provably, within the agent's proof system, doesn't line up with reality. So how do you get an AI to both believe it will cross, believe crossing entails -10 utility, and still counterfactually thinks that crossing will result in greater than -10 utility? In this situation, the AI can prove, within its own proof system, that the counterfactual environment of getting > -10 utility is wrong. So I guess we need an agent that allows itself to use a certain counterfactual environment even though the AI already proved that it's wrong. I'm concerned about the functionality of such an agent. If it already ignores clear evidence that it's counterfactual environment is wrong in reality, then that wou
Troll Bridge

Ok. This threw me for a loop briefly. It seems like I hadn't considered your proposed definition of "bad reasoning" (ie "it's bad if the agent crosses despite it being provably bad to do so") -- or had forgotten about that case.

I'm not sure I endorse the idea of defining "bad" first and then considering the space of agents who pass/fail according to that notion of "bad"; how this is supposed to work is, rather, that we critique a particular decision theory by proposing a notion of "bad" tailored to that particular decision theory. For example, if a specifi... (read more)

3Chantiel3moI'm concerned that may not realize that your own current take on counterfactuals respects logical to some extent, and that, if I'm reasoning correctly, could result in agents using it to fail the troll bridge problem. You said in "My current take on counterfactuals", that counterfactual should line up with reality. That is, the action the agent actually takes should in the utility it was said to have in its counterfactual environment. You say that a "bad reason" is one such that the agents the procedure would think is bad. The counterfactuals in your approach are supposed to line up with reality, so if an AI's counterfactuals don't line up in reality, then this seems like this is a "bad" reason according to the definition you gave. Now, if you let your agent think "I'll get < -10 utility if I don't cross", then it could potentially cross and not get blown up. But this seems like a very unintuitive and seemingly ridiculous counterfactual environment. Because of this, I'm pretty worried it could result in an AI with such counterfactual environments malfunctioning somehow. So I'll assume the AI doesn't have such a counterfactual environment. Suppose acting using a counterfactual environment that doesn't line up with reality counts as a "bad" reason for agents using your counterfactuals. Also suppose that in the counterfactual environment in which the agent doesn't cross, the agent counterfactually gets more than -10 utility. Then: 1. Suppose ⊢A=′Cross′⟹U=−10 2. Suppose A=′Cross′. Then if the agent crosses it must be because either it used the chicken rule or because its counterfactual environment doesn't line up with reality in this case. Either way, this is a bad reason for crossing, so the bridge gets blown up. Thus, the AI gets -10 utility. 3. Thus, ⊢(⊢A=′Cross′⟹U=−10)⟹U=−10 4. Thus, by Lob's theorem, ⊢A=′Cross′⟹U=−10 Thus, either the agent doesn't cross the bridge or it does and the bridge explodes. You might just decide to get around this by s
Refactoring Alignment (attempt #2)

Seems fair. I'm similarly conflicted. In truth, both the generalization-focused path and the objective-focused path look a bit doomed to me.

Re-Define Intent Alignment?

Great, I feel pretty resolved about this conversation now.

Re-Define Intent Alignment?

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.

1Edouard Harris6moYep, strongly agree. And a good first step to doing this is to actually build as robust a simplification as you can, and then see where it breaks. (Working on it.)
Re-Define Intent Alignment?

(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)

1Edouard Harris6moOh for sure. I wouldn't recommend having a Cartesian boundary assumption as the fulcrum of your alignment strategy, for example. But what could be interesting would be to look at an isolated dynamical system, draw one boundary, investigate possible objective functions in the context of that boundary; then erase that first boundary, draw a second boundary, investigate that; etc. And then see whether any patterns emerge that might fit an intuitive notion of agency. But the only fundamentally real object here is always going to be the whole system, absolutely. As I understand, something like AIXI forces you to draw one particular boundary because of the way the setting is constructed (infinite on one side, finite on the other). So I'd agree that sort of thing is more fragile. The multiagent setting is interesting though, because it gets you into the game of carving up your universe into more than 2 pieces. Again it would be neat to investigate a setting like this with different choices of boundaries and see if some choices have more interesting properties than others.
Re-Define Intent Alignment?

Right, exactly. (I should probably have just referred to that, but I was trying to avoid reference-dumping.)

Refactoring Alignment (attempt #2)

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

... (read more)
3Jack Koch6moThis sounds reasonable and similar to the kinds of ideas for understanding agents' goals as cognitively implemented that I've been exploring recently. The funny thing is I am actually very unsatisfied with a purely behavioral notion of a model's objective, since a deceptive model would obviously externally appear to be a non-deceptive model in training. I just don't think there will be one part of the network we can point to and clearly interpret as being some objective function that the rest of the system's activity is optimizing. Even though I am partial to the generalization focused approach (in part because it kind of widens the goal posts with the "acceptability" vs. "give the model exactly the correct goal" thing), I still would like to have a more cognitive understanding of a system's "goals" because that seems like one of the best ways to make good predictions about how the system will generalize under distributional shift. I'm not against assuming some kind of explicit representation of goal content within a system (for sufficiently powerful systems); I'm just against assuming that that content will look like a mesa-objective as originally defined.
Refactoring Alignment (attempt #2)

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)

Refactoring Alignment (attempt #2)

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)

Re-Define Intent Alignment?

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)

1Jack Koch6moIs this related to your post An Orthodox Case Against Utility Functions [] ? It's been on my to-read list for a while; I'll be sure to give it a look now.
Re-Define Intent Alignment?

They can't? Why not?

Answer 1

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)

2Rohin Shah6moSorry, I meant that that was my central complaint about existing theoretical work that is trying to explain neural net generalization. (I was mostly thinking of work outside of the alignment community.) I wasn't trying to make a claim about all theoretical work. It's my central complaint because we ~know that such an assumption is necessary (since the same neural net that generalizes well on real MNIST can also memorize a randomly labeled MNIST where it will obviously fail to generalize). I feel pretty convinced by this :) In particular the assumption on the real world could be something like "there exists a partial model that describes the real world well enough that we can prove a regret bound that is not vacuous" or something like that. And I agree this seems like a reasonable assumption. Tbc I would see this as a success. I am interested! I listed it [] as one of the topics I saw as allowing us to make claims about objective robustness. I'm just saying that the current work doesn't seem to be making much progress (I agree now though that InfraBayes is plausibly on a path where it could eventually help). Fwiw I don't feel the force of this intuition, they seem about equally surprising (but I agree with you that it doesn't seem cruxy).
Re-Define Intent Alignment?

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.

Refactoring Alignment (attempt #2)

Great! I feel like we're making progress on these basic definitions.

Re-Define Intent Alignment?

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)

2Rohin Shah6moThey can't? Why not? Maybe the "usefully" part is doing a lot of work here -- can all worlds be described (perhaps not usefully) by partial models? If so, I think I have the same objection, since it doesn't seem like any of the technical results in InfraBayes depend on some notion of "usefulness". (I think it's pretty likely I'm just flat out wrong about something here, given how little I've thought about InfraBayesianism, but if so I'd like to know how I'm wrong.)
Refactoring Alignment (attempt #2)

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)

2Rohin Shah6moYeah, strong +1.
Re-Define Intent Alignment?

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.)

3Rohin Shah6moMy central complaint about existing theoretical work is that it doesn't seem to be trying to explain why neural nets learn good programs that generalize well, even when they have enough parameters to overfit and can fit a randomly labeled dataset. It seems like you need to make some assumption about the real world (i.e. an assumption about your dataset, or the training process that generated it), which people seem loathe to do. I don't currently see how any of the alignment community's tools address that complaint; for example I don't think the InfraBayes work so far is making an interesting assumption about reality. Perhaps future work will address this though?
Re-Define Intent Alignment?

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:

  1. It requires approximately the same "magic transparency tech" as we need to extract mesa-objectives.
  2. Even with magical transparency tech, it requires additional insight as to which reasoni
... (read more)
3Rohin Shah6moI don't hope this; I expect to use a version of "acceptable" that uses intent. I'm happy with "acceptable" = "trying to do what we want". I'm pessimistic about mesa-objectives existing in actual systems, based on how people normally seem to use the term "mesa-objective". If you instead just say that a "mesa objective" is "whatever the system is trying to do", without attempting to cash it out as some simple utility function that is being maximized, or the output of a particular neuron in the neural net, etc, then that seems fine to me. One other way in which "acceptability" is better is that rather than require it of all inputs, you can require it of all inputs that are reasonably likely to occur in practice, or something along those lines. (And this is what I expect we'll have to do in practice given that I don't expect to fully mechanistically understand a large neural network; the "all inputs" should really be thought of as a goal we're striving towards.) Whereas I don't see how you do this with a mesa-objective (as the term is normally used); it seems like a mesa-objective must apply on any input, or else it isn't a mesa-objective. I'm mostly not trying to make claims about which one is easier to do; rather I'm saying "we're using the wrong concepts; these concepts won't apply to the systems we actually build; here are some other concepts that will work".
Re-Define Intent Alignment?

(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.

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:

  • Talk at CHAI saying something like "daemons are just distributional shift" in August 2018, I think. (I remember Scott attending it.)
  • Talk at FHI in February 2020 that emphasized a risk model where objectives generalize but capabilities don't.
  • Talk at SERI conference a few months ago that explicitly argued for a focus on
... (read more)
Discussion: Objective Robustness and Inner Alignment Terminology

If there were a "curated posts" system on the alignment forum, I would nominate this for curation. I think it's a great post.

My Current Take on Counterfactuals

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

... (read more)
My Current Take on Counterfactuals

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)

1gjm6moOK, I get it. (Or at least I think I do.) And, duh, indeed it turns out (as you were too polite to say in so many words) that I was distinctly confused. So: Using ordinary conditionals in planning your actions commits you to reasoning like "If (here in the actual world it turns out that) I choose to smoke this cigarette, then that makes it more likely that I have the weird genetic anomaly that causes both desire-to-smoke and lung cancer, so I'm more likely to die prematurely and horribly of lung cancer, so I shouldn't smoke it", which makes wrong decisions. So you want to use some sort of conditional that doesn't work that way and rather says something more like "suppose everything about the world up to now is exactly as it is in the actual world, but magically-but-without-the-existence-of-magic-having-consequences I decide to do X; what then?". And this is what you're calling decision-theoretic counterfactuals, and the question is exactly what they should be; EDT says no, just use ordinary conditionals, CDT says pretty much what I just said, etc. The "smoking lesion" shows that EDT can give implausible results; "Death in Damascus" shows that CDT can give implausible results; etc. 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.) It still feels to me as if your proof-based agents are unrealistically narrow. Sure, they can incorporate whatever beliefs they have about the real world as axioms for their proofs -- but only if those axioms end up being consistent, which means having perfectly consistent beliefs. The beliefs may of course be probabilistic, but then that means that all those beliefs have to hav
Decision Theory

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)

Decision Theory

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)

Decision Theory

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".

My Current Take on Counterfactuals

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:

  1. "No" because if we change chemistry, everything changes.
  2. "Yes" because counterfactuals keep everything the same as much as possible, e
... (read more)

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)

Decision Theory

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 , but rather just outputs  if there's a valid assignment with , and  otherwise. Only  fulfills the condition, so it outputs .

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)

3Ian Televan6moWhile 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. I thought of a few ways to interpret it, but I'm not sure which one, if any, was the intended interpretation: a) The algorithm is defined to compute argmax, but it doesn't output argmax because of false antecedents. - but I would say that it's not actually defined to compute argmax, therefore the fact that it doesn't output argmax is not a problem. b) Regardless of the output, the algorithm uses reasoning from false antecedents, which seems nonsensical from the perspective of someone who uses intuitive conditionals, which impedes its reasoning. - it may indeed seem nonsensical, but if 'seeming nonsensical' doesn't actually impede its ability to select actions wich highest utility (when it's actually defined to compute argmax), then I would say that it's also not a problem. Furthermore, wouldn't MUDT be perfectly satisfied with the tuplep1:(x=0,y=10,A() =10,U()=10)? It also uses 'nonsensical' reasoning 'A()=5 => U()=0' but still outputs action with highest utility. c) Even when the use of false antecedents doesn't impede its reasoning, the way it arrives at its conclusions is counterintuitive to humans, which means that we're more likely to make a catastrophic mistake when reasoning about how the agent reasons. - Maybe? I don't have access to other people's intuitions, but when I read the example, I didn't have any intuitive feeling of what the algorithm would do, so instead I just calculated all assignments(x,y)∈{0,5,10}2, eliminated all inconsistent ones and proceeded from there. And this issue wouldn't be unique to false antecedents, there are other perfectly valid pieces of logic that might nonetheless seem counterintuitive to humans, for example the puzzle with islanders and blue eyes []. --------------------------------------------------
0TAG6moNo, it's contradictory assumptions. False but consistent assumptions are dual to consistent-and-true you can only infer a mutually consistent set of propositions from either. To put it another way, a formal system has no way of knowing what would be true or false for reasons outside itself, so it has no way of reacting to a merely false statement. But a contradiction is definable within a formal system. To.put it yet another way... contradiction in, contradiction out
My Current Take on Counterfactuals

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)

An Intuitive Guide to Garrabrant Induction

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.

The Credit Assignment Problem
  • In between … well … in between, we're navigating treacherous waters …

Right, I basically agree with this picture. I might revise it a little:

  • Early, the AGI is too dumb to hack its epistemics (provided we don't give it easy ways to do so!).
  • In the middle, there's a danger zone.
  • When the AGI is pretty smart, it sees why one should be cautious about such things, and it also sees why any modifications should probably be in pursuit of truthfulness (because true beliefs are a convergent instrumental goal) as opposed to other reasons.
  • When the AGI is really smart, it
... (read more)
My Current Take on Counterfactuals

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)

My Current Take on Counterfactuals

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

... (read more)
1Vanessa Kosoy7moI would be convinced if you had a theory of rationality that is a Pareto improvement on IB (i.e. has all the good properties of IB + a more general class of utility functions). However, LI doesn't provide this AFAICT. That said, I would be interested to see some rigorous theorem about LIDT solving procrastination-like problems. As to philosophical deliberation, I feel some appeal in this point of view, but I can also easily entertain a different point of view: namely, that human values are more or less fixed and well-defined whereas philosophical deliberation is just a "show" for game theory reasons. 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.
My Current Take on Counterfactuals

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! 

Speculations against GPT-n writing alignment papers

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?

An Intuitive Guide to Garrabrant Induction

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)

2Vladimir Slepnev8moInteresting! Can you write up the WLIC, here or in a separate post?
My AGI Threat Model: Misaligned Model-Based RL Agent

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)

1Steve Byrnes8moYup! This was a state-the-problem-not-solve-it post. (The companion solving-the-problem post is this brain dump [] , I guess.) In particular, just like prosaic AGI alignment, my starting point is not "Building this kind of AGI is a great idea", but rather "This is a way to build AGI that could really actually work capabilities-wise (especially insofar as I'm correct that the human brain works along these lines), and that people are actively working on (in both ML and neuroscience), and we should assume there's some chance they'll succeed whether we like it or not." Thanks, that's helpful. One way I think I would frame the problem differently than you here is: I'm happy to talk about outer and inner alignment for pedagogical purposes, but I think it's overly constraining as a framework for solving the problem. For example, (Paul-style) corrigibility is I think an attempt to cut through outer and inner alignment simultaneously, as is interpretability perhaps. And like you say, rewards don't need to be the only type of feedback. We can also set up the AGI to NOOP when the expected value of some action is <0, rather than having it always take the least bad action. (...And then don't use it in time-sensitive situations! But that's fine for working with humans to build better-aligned AGIs.) So then the goal would be something like "every catastrophic action has expected value <0 as assessed by the AGI (and also, the AGI will not be motivated to self-modify or create successors, at least not in a way that undermines that property) (and also, the AGI is sufficiently capable that it can do alignment research etc., as opposed to it sitting around NOOPing all day)". So then this could look like a pretty weirdly misaligned AGI but it has a really effective "may-lead-to-catastrophe (directly or indirectly) predictor circuit" attached. (The circuit asks "Does it pattern-match
My Current Take on Counterfactuals

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:

  1. The optimization target is approximate.
  2. The optimization itself gives only approximate maxima.

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)

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