Text whose primary goal is conveying information (as opposed to emotion, experience or aesthetics) should be skimming friendly. Time is expensive, words are cheap. Skimming is a vital mode of engaging with text, either to evaluate whether it deserves a deeper read or to extract just the information you need. As a reader, you should nurture your skimming skills. As a writer, you should treat skimmers as a legitimate and important part of your target audience. Among other things it means:
Good title and TLDR/abstract
Clear and useful division into sections
Putting the high-level picture and conclusions first, the technicalities and detailed arguments later. Never leave the reader clueless about where you’re going with something for a long time.
Visually emphasize the central points and make them as self-contained as possible. For example, in the statement of mathematical theorems avoid terminology whose definition is hidden somewhere in the bulk of the text.
Stronger: as a writer you should assume your modal reader is a skimmer, both because they are, and because even non skimmers are only going to remember about the same number of things that the good skimmer does.
I propose to call metacosmology the hypothetical field of study which would be concerned with the following questions:
Studying the space of simple mathematical laws which produce counterfactual universes with intelligent life.
Studying the distribution over utility-function-space (and, more generally, mindspace) of those counterfactual minds.
Studying the distribution of the amount of resources available to the counterfactual civilizations, and broad features of their development trajectories.
Using all of the above to produce a distribution over concretized simulation hypotheses.
This concept is of potential interest for several reasons:
It can be beneficial to actually research metacosmology, in order to draw practical conclusions. However, knowledge of metacosmology can pose an infohazard, and we would need to precommit not to accept blackmail from potential simulators.
The metacosmology knowledge of a superintelligent AI determines the extent to which it poses risk via the influence of potential simulators.
In principle, we might be able to use knowledge of metacosmology in order to engineer an “atheist prior” for the AI that would exclude simulation hypotheses. However, this might be very difficult in practice.
An AI progress scenario which seems possible and which I haven’t seen discussed: an imitation plateau.
The key observation is, imitation learning algorithms[1] might produce close-to-human-level intelligence even if they are missing important ingredients of general intelligence that humans have. That’s because imitation might be a qualitatively easier task than general RL. For example, given enough computing power, a human mind becomes realizable from the perspective of the learning algorithm, while the world-at-large is still far from realizable. So, an algorithm that only performs well in the realizable setting can learn to imitate a human mind, and thereby indirectly produce reasoning that works in non-realizable settings as well. Of course, literally emulating a human brain is still computationally formidable, but there might be middle scenarios where the learning algorithm is able to produce a good-enough-in-practice imitation of systems that are not too complex.
This opens the possibility that close-to-human-level AI will arrive while we’re still missing key algorithmic insights to produce general intelligence directly. Such AI would not be easily scalable to superhuman. Nevertheless, some superhuman performance might be produced by sped-up simulation, reducing noise in human behavior and controlling the initial conditions (e.g. simulating a human on a good day). As a result, we will have some period of time during which AGI is already here, automation is in full swing, but there’s little or no further escalation. At the end of this period, the missing ingredients will be assembled (maybe with the help of AI researchers) and superhuman AI (possibly a fast takeoff) begins.
It’s interesting to try and work out the consequences of such a scenario, and the implications on AI strategy.
This seems similar to gaining uploads prior to AGI, and opens up all those superorg upload-city amplification/distillation constructions which should get past human level shortly after. In other words, the limitations of the dataset can be solved by amplification as soon as the AIs are good enough to be used as building blocks for meaningful amplification, and something human-level-ish seems good enough for that. Maybe even GPT-n is good enough for that.
That is similar to gaining uploads (borrowing terminology from Egan, we can call them “sideloads”), but it’s not obvious amplification/distillation will work. In the model based on realizability, the distillation step can fail because the system you’re distilling is too computationally complex (hence, too unrealizable). You can deal with it by upscaling the compute of the learning algorithm, but that’s not better than plain speedup.
To me this seems to be essentially another limitation of the human Internet archive dataset: reasoning is presented in an opaque way (most slow/deliberative thoughts are not in the dataset), so it’s necessary to do a lot of guesswork to figure out how it works. A better dataset both explains and summarizes the reasoning (not to mention gets rid of the incoherent nonsense, but even GPT-3 can do that to an extent by roleplaying Feynman).
Any algorithm can be represented by a habit of thought (Turing machine style if you must), and if those are in the dataset, they can be learned. The habits of thought that are simple enough to summarize get summarized and end up requiring fewer steps. My guess is that the human faculties needed for AGI can be both represented by sequences of thoughts (probably just text, stream of consciousness style) and easily learned with current ML. So right now the main obstruction is that it’s not feasible to build a dataset with those faculties represented explicitly that’s good enough and large enough for current sample-inefficient ML to grok. More compute in the learning algorithm is only relevant for this to the extent that we get a better dataset generator that can work on the tasks before it more reliably.
I don’t see any strong argument why this path will produce superintelligence. You can have a stream of thought that cannot be accelerated without investing a proportional amount of compute, while a completely different algorithm would produce a far superior “stream of thought”. In particular, such an approach cannot differentiate between features of the stream of thought that are important (meaning that they advance towards the goal) and features of the stream of though that are unimportant (e.g. different ways to phrase the same idea). This forces you to solve a task that is potentially much more difficult than just achieving the goal.
I was arguing that near human level babblers (including the imitation plateau you were talking about) should quickly lead to human level AGIs by amplification via stream of consciousness datasets, which doesn’t pose new ML difficulties other than design of the dataset. Superintelligence follows from that by any of the same arguments as for uploads leading to AGI (much faster technological progress; if amplification/distillation of uploads is useful straight away, we get there faster, but it’s not necessary). And amplified babblers should be stronger than vanilla uploads (at least implausibly well-educated, well-coordinated, high IQ humans).
For your scenario to be stable, it needs to be impossible (in the near term) to run the AGIs (amplified babblers) faster than humans, and for the AGIs to remain less effective than very high IQ humans. Otherwise you get acceleration of technological progress, including ML. So my point is that feasibility of imitation plateau depends on absence of compute overhang, not on ML failing to capture some of the ingredients of human general intelligence.
The imitation plateau can definitely be rather short. I also agree that computational overhang is the major factor here. However, a failure to capture some of the ingredients can be a cause of low computational overhead, whereas a success to capture all of the ingredients is a cause of high computational overhang, because the compute necessary to reach superintelligence might be very different in those two cases. Using sideloads to accelerate progress might still require years, whereas an “intrinsic” AGI might lead to the classical “foom” scenario.
EDIT: Although, since training is typically much more computationally expensive than deployment, it is likely that the first human-level imitators will already be significantly sped-up compared to humans, implying that accelerating progress will be relatively easy. It might still take some time from the first prototype until such an accelerate-the-progress project, but probably not much longer than deploying lots of automation.
I agree. But GPT-3 seems to me like a good estimate for how much compute it takes to run stream of consciousness imitation learning sideloads (assuming that learning is done in batches on datasets carefully prepared by non-learning sideloads, so the cost of learning is less important). And with that estimate we already have enough compute overhang to accelerate technological progress as soon as the first amplified babbler AGIs are developed, which, as I argued above, should happen shortly after babblers actually useful for automation of human jobs are developed (because generation of stream of consciousness datasets is a special case of such a job).
So the key things to make imitation plateau last for years are either sideloads requiring more compute than it looks like (to me) they require, or amplification of competent babblers into similarly competent AGIs being a hard problem that takes a long time to solve.
Another thing that might happen is a data bottleneck.
Maybe there will be a good enough dataset to produce a sideload that simulates an “average” person, and that will be enough to automate many jobs, but for a simulation of a competent AI researcher you would need a more specialized dataset that will take more time to produce (since there are a lot less competent AI researchers than people in general).
Moreover, it might be that the sample complexity grows with the duration of coherent thought that you require. That’s because, unless you’re training directly on brain inputs/outputs, non-realizable (computationally complex) environment influences contaminate the data, and in order to converge you need to have enough data to average them out, which scales with the length of your “episodes”. Indeed, all convergence results for Bayesian algorithms we have in the non-realizable setting require ergodicity, and therefore the time of convergence (= sample complexity) scales with mixing time, which in our case is determined by episode length.
In such a case, we might discover that many tasks can be automated by sideloads with short coherence time, but AI research might require substantially longer coherence times. And, simulating progress requires by design going off-distribution along certain dimensions which might make things worse.
Another way to describe the same (or similar) plateau: we could think about GPT-n as GLUT with approximation between prerecorded answers: it can produce intelligent products similar to the ones which were created by humans in the past and are presented in its training dataset – but not above the human intelligence level, as there is no superintelligent examples in the dataset.
I propose a new formal desideratum for alignment: the Hippocratic principle. Informally the principle says: an AI shouldn’t make things worse compared to letting the user handle them on their own, in expectation w.r.t. the user’s beliefs. This is similar to the dangerousness bound I talked about before, and is also related to corrigibility. This principle can be motivated as follows. Suppose your options are (i) run a Hippocratic AI you already have and (ii) continue thinking about other AI designs. Then, by the principle itself, (i) is at least as good as (ii) (from your subjective perspective).
More formally, we consider a (some extension of) delegative IRL setting (i.e. there is a single set of input/output channels the control of which can be toggled between the user and the AI by the AI). Let πυu be the the user’s policy in universe υ and πa the AI policy. Let T be some event that designates when we measure the outcome / terminate the experiment, which is supposed to happen with probability 1 for any policy. Let Vυ be the value of a state from the user’s subjective POV, in universe υ. Let μυ be the environment in universe υ. Finally, let ζ be the AI’s prior over universes and ϵ some sufficiently small bound. We require
∀T:Eυ∼ζ[(Eπυuμυ[Vυ(T)]−Eπaμυ[Vυ(T)])+]≤ϵ
Here, V(T) designates the value after event T happens, and (x)+ is defined to be 0 for x<0 and x otherwise.
I now outline an example of a Hippocratic protocol, which I call Hippocratic timeline-driven learning (HTDL).
Suppose, for simplicity, that the goal is accomplishing some binary task (i.e. we can either succeed or fail). We can imagine the task to be something like “construct a defense system against malign AI”. At any given moment, the user reports their subjective timeline: i.e. for, any t, the probabilities of success and failure after time t, assuming the user will continue working on the task by themselves. We assume the AI can learn a good model of the user’s policy and the user’s nominal report, but this nominal report might deviate from the true timeline because the system can be corrupted (i.e. the user manipulated to enter false information or the input channel hacked). Moreover, we assume that such corruption counts as failing the task.
The AI operates by maximizing the AI-expectation of the user’s (unknown) utility function Eυ∼ζ[Eπμυ[Uυ]], subject to the Hippocratic principle as a constraint. Since we’re working in a delegative setting, the AI can trivially achieve the constraint by always delegating. However, it can do much better than that, by short-term quantilizing w.r.t. the user’s reported success probability (with the user’s policy serving as baseline). When quantilizing the short-term policy, we can upper bound the probability of corruption via the user’s reported probability of short-term failure (which we assume to be low, i.e. we assume the malign AI is not imminent). This allows the AI to find parameters under which quantilization is guaranteed to improve things in expectation. Then, after completing this short-term maneuver, the AI can do another short-term quantilization, and continue in this vein. Essentially, the AI is gradually bending the user’s trajectory towards success.
Of course, we can improve the guarantees of such a protocol either by adding more information or by using stronger assumptions (e.g. assume the user is quasi-rational). But timeline-driven short-term quantilization serves as a useful feasibility proof.
(Update: I don’t think this was 100% right, see here for a better version.)
Attempted summary for morons like me: AI is trying to help the human H. They share access to a single output channel, e.g. a computer keyboard, so that the actions that H can take are exactly the same as the actions AI can take. Every step, AI can either take an action, or delegate to H to take an action. Also, every step, H reports her current assessment of the timeline / probability distribution for whether she’ll succeed at the task, and if so, how soon.
At first, AI will probably delegate to H a lot, and by watching H work, AI will gradually learn both the human policy (i.e. what H tends to do in different situations), and how different actions tend to turn out in hindsight from H’s own perspective (e.g., maybe whenever H takes action 17, she tends to declare shortly afterwards that probability of success now seems much higher than before—so really H should probably be taking action 17 more often!).
Presumably the AI, being a super duper fancy AI algorithm, learns to anticipate how different actions will turn out from H’s perspective much better than H herself. In other words, maybe it delegates to H, and H takes action 41, and the AI is watching this and shaking its head and thinking to itself “gee you dunce you’re gonna regret that”, and shortly thereafter the AI is proven correct.
OK, so now what? The naive answer would be: the AI should gradually stop delegating and start just doing the thing that leads to H feeling maximally optimistic later on.
But we don’t want to do that naive thing. There are two problems:
The first problem is “traps” (a.k.a. catastrophes). Let’s say action 0 is Press The History Eraser Button. H never takes that action. The AI shouldn’t either. What happens is: AI has no idea (wide confidence interval) about what the consequence of action 0 would be, so it doesn’t take it. This is the delegative RL thing—in the explore/exploit dilemma, the AI kinda sits back and lets H do the exploring, at least when a trap might be involved.
But here we want to go further. Let’s say that there’s some action which H might do, where the AI can predict the consequence pretty well, and the consequence is “H will eventually feel more optimistic about success in the task”. Then the AI can go do that action, right? Not necessarily! The action might still be bad, because it involved corrupting the feedback channel.
So let’s say “action 789 = H will eat a pot brownie”. Taking that action makes H both much more optimistic about success and much less likely to actually succeed in the task. (It makes her assessments inaccurate.)
If the AI has no idea what pot brownies do, it won’t take action 789 because of the previous thing—maybe that action is a trap. But what if the AI has (indirectly) figured out, with 100% confidence, exactly what the consequence of action 789 would be?
In that case, since the AI’s decision criterion is all about H’s future reported odds of success, a naive algorithm would have the AI take action 789. To avoid that, we have this quantilization thing. How does that work?
Well, let’s say that H right now assigns 99% chance of eventual success. Therefore—assuming that H is well-calibrated and knows the consequences of action 789—it follows that H’s policy is to take that action with ≤1% probability. Basically, H is optimistic about success in the task, partly because H knows that she’s almost definitely not going to eat that pot brownie. I mean, she might eat it, if she gets really bored and frustrated and hungry. But probably not! So she’s feeling very optimistic.
OK, now the AI quantilizes by drawing 5 random samples from (its understanding of) H’s policy, and taking the action that (it believes) will lead to the highest future-optimism-about-success. Assume its beliefs are accurate. Compared to just doing what H would have done, this has downsides and upsides. The downside is, since action 789 scores really well by the AI’s decision criterion, the probability of action 789 is no longer ≤1%, but rather ≤5%. The upside is, if we’re not taking action 789, i.e. the other ≥95% of the probability mass, we’re probably doing something quite a bit better than what H would have done.
These could plausibly balance out such that the expected probability of success strictly increases, compared to what H would have done.
Please correct me if I’m misunderstanding anything.
Notice that typically we use the AI for tasks which are hard for H. This means that without the AI’s help, H’s probability of success will usually be low. Quantilization-wise, this is a problem: the AI will be able to eliminate those paths for which H will report failure, but maybe most of the probability mass among apparent-success paths is still on failure (i.e. the success report is corrupt). This is why the timeline part is important.
On a typical task, H expects to fail eventually but they don’t expect to fail soon. Therefore, the AI can safely consider a policies of the form “in the short-term, do something H would do with marginal probability, in the long-term go back to H’s policy”. If by the end of the short-term maneuver H reports an improved prognosis, this can imply that the improvement is genuine (since the AI knows H is probably uncorrupted at this point). Moreover, it’s possible that in the new prognosis H still doesn’t expect to fail soon. This allows performing another maneuver of the same type. This way, the AI can iteratively steer the trajectory towards true success.
Yes, there is some similarity! You could say that a Hippocratic AI needs to be continuously non-obstructive w.r.t. the set of utility functions and priors the user could plausibly have, given what the AI knows. Where, by “continuously” I mean that we are allowed to compare keeping the AI on or turning off at any given moment.
“Corrigibility” is usually defined as the property of AIs who don’t resist modifications by their designers. Why would we want to perform such modifications? Mainly it’s because we made errors in the initial implementation, and in particular the initial implementation is not aligned. But, this leads to a paradox: if we assume our initial implementation to be flawed in a way that destroys alignment, why wouldn’t it also be flawed in a way that destroys corrigibility?
In order to stop passing the recursive buck, we must assume some dimensions along which our initial implementation is not allowed to be flawed. Therefore, corrigibility is only a well-posed notion in the context of a particular such assumption. Seen through this lens, the Hippocratic principle becomes a particular crystallization of corrigibility. Specifically, the Hippocratic principle assumes the agent has access to some reliable information about the user’s policy and preferences (be it through timelines, revealed preferences or anything else).
Importantly, this information can be incomplete, which can motivate altering the agent along the way. And, the agent will not resist this alteration! Indeed, resisting the alteration is ruled out unless the AI can conclude with high confidence (and not just in expectation) that such resistance is harmless. Since we assumed the information is reliable, and the alteration is beneficial, the AI cannot reach such a conclusion.
For example, consider an HDTL agent getting upgraded to “Hippocratic CIRL” (assuming some sophisticated model of relationship between human behavior and human preferences). In order to resist the modification, the agent would need a resistance strategy that (i) doesn’t deviate too much from the human baseline and (ii) ends with the user submitting a favorable report. Such a strategy is quite unlikely to exist.
if we assume our initial implementation to be flawed in a way that destroys alignment, why wouldn’t it also be flawed in a way that destroys corrigibility?
I think the people most interested in corrigibility are imagining a situation where we know what we’re doing with corrigibility (e.g. we have some grab-bag of simple properties we want satisfied), but don’t even know what we want from alignment, and then they imagine building an unaligned slightly-sub-human AGI and poking at it while we “figure out alignment.”
Maybe this is a strawman, because the thing I’m describing doesn’t make strategic sense, but I think it does have some model of why we might end up with something unaligned but corrigible (for at least a short period).
The concept of corrigibility was introduced by MIRI, and I don’t think that’s their motivation? On my model of MIRI’s model, we won’t have time to poke at a slightly subhuman AI, we need to have at least a fairly good notion of what to do with a superhuman AI upfront. Maybe what you meant is “we won’t know how to construct perfect-utopia-AI, so we will just construct a prevent-unaligned-AIs-AI and run it so that we can figure out perfect-utopia-AI in our leisure”. Which, sure, but I don’t see what it has to do with corrigibility.
Corrigibility is neither necessary nor sufficient for safety. It’s not strictly necessary because in theory an AI can resist modifications in some scenarios while always doing the right thing (although in practice resisting modifications is an enormous red flag), and it’s not sufficient since an AI can be “corrigible” but cause catastrophic harm before someone notices and fixes it.
What we’re supposed to gain from corrigibility is having some margin of error around alignment, in which case we can decompose alignment as corrigibility + approximate alignment. But it is underspecified if we don’t say along which dimensions or how big the margin is. If it’s infinite margin along all dimensions then corrigibility and alignment are just isomorphic and there’s no reason to talk about the former.
Very interesting—I’m sad I saw this 6 months late.
After thinking a bit, I’m still not sure if I want this desideratum. It seems to require a sort of monotonicity, where we can get superhuman performance just by going through states that humans recognize as good, and not by going through states that humans would think are weird or scary or unevaluable.
One case where this might come up is in competitive games. Chess AI beats humans in part because it makes moves that many humans evaluate as bad, but are actually good. But maybe this example actually supports your proposal—it seems entirely plausible to make a chess engine that only makes moves that some given population of humans recognize as good, but is better than any human from that population.
On the other hand, the humans might be wrong about the reason the move is good, so that the game is made of a bunch of moves that seem good to humans, but where the humans are actually wrong about why they’re good (from the human perspective, this looks like regularly having “happy surprises”). We might hope that such human misevaluations are rare enough that quantilization would lead to moves on average being well-evaluated by humans, but for chess I think that might be false! Computers are so much better than humans at chess that a very large chunk of the best moves according to both humans and the computer will be ones that humans misevaluate.
Maybe that’s more a criticism of quantilizers, not a criticism of this desideratum. So maybe the chess example supports this being a good thing to want? But let me keep critiquing quantilizers then :P
If what a powerful AI thinks is best (by an exponential amount) is to turn off the stars until the universe is colder, but humans think it’s scary and ban the AI from doing scary things, the AI will still try to turn off the stars in one of the edge-case ways that humans wouldn’t find scary. And if we think being manipulated like that is bad and quantilize over actions to make the optimization milder, turning off the stars is still so important that a big chunk of the best moves according to both humans and the computer are going to be ones that humans misevaluate, and the computer knows will lead to a “happy surprise” of turning off the stars not being scary. Quantilization avoids policies that precisely exploit tiny features of the world, and it avoids off-distribution behavior, but it still lets the AI get what it wants if it totally outsmarts the humans.
The other thing this makes me think of is Lagrange multipliers. I bet there’s a duality between applying this constraint to the optimization process, and adding a bias (I mean, a useful prior) to the AI’s process for modeling U.
When I’m deciding whether to run an AI, I should be maximizing the expectation of my utility function w.r.t. my belief state. This is just what it means to act rationally. You can then ask, how is this compatible with trusting another agent smarter than myself?
One potentially useful model is: I’m good at evaluating and bad at searching (after all, P≠NP). I can therefore delegate searching to another agent. But, as you point out, this doesn’t account for situations in which I seem to be bad at evaluating. Moreover, if the AI prior takes an intentional stance towards the user (in order to help learning their preferences), then the user must be regarded as good at searching.
A better model is: I’m good at both evaluating and searching, but the AI can access actions and observations that I cannot. For example, having additional information can allow it to evaluate better. An important special case is: the AI is connected to an external computer (Turing RL) which we can think of as an “oracle”. This allows the AI to have additional information which is purely “logical”. We need infra-Bayesianism to formalize this: the user has Knightian uncertainty over the oracle’s outputs entangled with other beliefs about the universe.
For instance, in the chess example, if I know that a move was produced by exhaustive game-tree search then I know it’s a good move, even without having the skill to understand why the move is good in any more detail.
Now let’s examine short-term quantilization for chess. On each cycle, the AI finds a short-term strategy leading to a position that the user evaluates as good, but that the user would require luck to manage on their own. This is repeated again and again throughout the game, leading to overall play substantially superior to the user’s. On the other hand, this play is not as good as the AI would achieve if it just optimized for winning at chess without any constrains. So, our AI might not be competitive with an unconstrained unaligned AI. But, this might be good enough.
I’m not sure what you’re saying in the “turning off the stars example”. If the probability for the user to autonomously decide to turn off the stars is much lower than the quantilization fraction, then the probability that quantilization will decide to turn off the stars is low. And, the quantilization fraction is automatically selected like this.
Agree with the first section, though I would like to register my sentiment that although “good at selecting but missing logical facts” is a better model, it’s still not one I’d want an AI to use when inferring my values.
I’m not sure what you’re saying in the “turning off the stars example”. If the probability for the user to autonomously decide to turn off the stars is much lower than the quantilization fraction, then the probability that quantilization will decide to turn off the stars is low. And, the quantilization fraction is automatically selected like this.
I think my point is if “turn off the stars” is not a primitive action, but is a set of states of the world that the AI would overwhelming like to go to, then the actual primitive actions will get evaluated based on how well they end up going to that goal state. And since the AI is better at evaluating than us, we’re probably going there.
Another way of looking at this claim is that I’m telling a story about why the safety bound on quantilizers gets worse when quantilization is iterated. Iterated quantilization has much worse bounds than quantilizing over the iterated game, which makes sense if we think of games where the AI evaluates many actions better than the human.
I think you misunderstood how the iterated quantilization works. It does not work by the AI setting a long-term goal and then charting a path towards that goal s.t. it doesn’t deviate too much from the baseline over every short interval. Instead, every short-term quantilization is optimizing for the user’s evaluation in the end of this short-term interval.
Ah. I indeed misunderstood, thanks :) I’d read “short-term quantilization” as quantilizing over short-term policies evaluated according to their expected utility. My story doesn’t make sense if the AI is only trying to push up the reported value estimates (though that puts a lot of weight on these estimates).
However, it can do much better than that, by short-term quantilizing w.r.t. the user’s reported success probability (with the user’s policy serving as baseline). When quantilizing the short-term policy, we can upper bound the probability of corruption via the user’s reported probability of short-term failure (which we assume to be low, i.e. we assume the malign AI is not imminent). This allows the AI to find parameters under which quantilization is guaranteed to improve things in expectation.
I don’t understand what you mean here by quantilizing. The meaning I know is to take a random action over the top \alpha actions, on a given base distribution. But I don’t see a distribution here, or even a clear ordering over actions (given that we don’t have access to the utility function).
I’m probably missing something obvious, but more details would really help.
The distribution is the user’s policy, and the utility function for this purpose is the eventual success probability estimated by the user (as part of the timeline report), in the end of the “maneuver”. More precisely, the original quantilization formalism was for the one-shot setting, but you can easily generalize it, for example I did it for MDPs.
So is the general idea that we quantilize such that we’re choosing in expectation an action that doesn’t have corrupted utility (by intuitively having something like more than twice as many actions in the quantilization than we expect to be corrupted), so that we guarantee the probability of following the manipulation of the learned user report is small?
I also wonder if using the user policy to sample actions isn’t limiting, because then we can only take actions that the user would take. Or do you assume by default that the support of the user policy is the full action space, so every action is possible for the AI?
So is the general idea that we quantilize such that we’re choosing in expectation an action that doesn’t have corrupted utility (by intuitively having something like more than twice as many actions in the quantilization than we expect to be corrupted), so that we guarantee the probability of following the manipulation of the learned user report is small?
Yes, although you probably want much more than twice. Basically, if the probability of corruption following the user policy is ϵ and your quantilization fraction is ϕ then the AI’s probability of corruption is bounded by ϵϕ.
I also wonder if using the user policy to sample actions isn’t limiting, because then we can only take actions that the user would take. Or do you assume by default that the support of the user policy is the full action space, so every action is possible for the AI?
Obviously it is limiting, but this is the price of safety. Notice, however, that the quantilization strategy is only an existence proof. In principle, there might be better strategies, depending on the prior (for example, the AI might be able to exploit an assumption that the user is quasi-rational). I didn’t specify the AI by quantilization, I specified it by maximizing EU subject to the Hippocratic constraint. Also, the support is not really the important part: even if the support is the full action space, some sequences of actions are possible but so unlikely that the quantilization will never follow them.
I like this because it’s simple and obviously correct. Also I can see at least one way you could implement it:
a. Suppose the AI is ‘shadowing’ a human worker doing a critical task. Say it is ‘shadowing’ a human physician.
b. Each time the AI observes the same patient, it regresses between [data from the patient] and [predicted decision a ‘good’ physician would make, predicted outcome for the ‘good’ decision]. Once the physician makes a decision and communicates it, the AI regresses between [decision the physician made] and [predicted outcome for that decision].
c. The machine also must have a confidence or this won’t work.
With large numbers and outright errors made by the physician, it’s then possible to detect all the cases where the [decision the physician made] has a substantially worse outcome than the [predicted decision a ‘good’ physician would make], and when the AI has a high confidence of this [requiring many observations of similar situations] and it’s time to call for a second opinion.
In the long run, of course, there will be a point where the [predicted decision a ‘good’ physician would make] is better than the [information gain from a second human opinion] and you really would do best by firing the physician and having the AI make the decisions from then on, trusting for it to call for a second opinion when it is not confident.
(as an example, alpha go zero likely doesn’t benefit from asking another master go player for a ‘second opinion’ when it sees the player it is advising make a bad call)
This idea was inspired by a correspondence with Adam Shimi.
It seem very interesting and important to understand to what extent a purely “behaviorist” view on goal-directed intelligence is viable. That is, given a certain behavior (policy), is it possible to tell whether the behavior is goal-directed and what are its goals, without any additional information?
Consider a general reinforcement learning settings: we have a set of actions A, a set of observations O, a policy is a mapping π:(A×O)∗→ΔA, a reward function is a mapping r:(A×O)∗→[0,1], the utility function is a time discounted sum of rewards. (Alternatively, we could use instrumental reward functions.)
The simplest attempt at defining “goal-directed intelligence” is requiring that the policy π in question is optimal for some prior and utility function. However, this condition is vacuous: the reward function can artificially reward only behavior that follows π, or the prior can believe that behavior not according to π leads to some terrible outcome.
The next natural attempt is bounding the description complexity of the prior and reward function, in order to avoid priors and reward functions that are “contrived”. However, description complexity is only naturally well-defined up to an additive constant. So, if we want to have a crisp concept, we need to consider an asymptotic in which the complexity of something goes to infinity. Indeed, it seems natural to ask that the complexity of the policy should be much higher than the complexity of the prior and the reward function: in this case we can say that the “intentional stance” is an efficient description. However, this doesn’t make sense with description complexity: the description “optimal policy for U and ζ” is of size K(U)+K(ζ)+O(1) (K(x) stands for “description complexity of x”).
To salvage this idea, we need to take not only description complexity but also computational complexity into account. [EDIT: I was wrong, and we can get a well-defined concept in the unbounded setting too, see child comment. The bounded concept is still interesting.] For the intentional stance to be non-vacuous we need to demand that the policy does some “hard work” in order to be optimal. Let’s make it formal. Consider any function of the type f:Σ∗→ΔΞ where Σ and Ξ are some finite alphabets. Then, we can try to represent it by a probabilistic automaton T:S×Σ→Δ(S×Ξ), where S is the finite set space, T is the transition kernel, and we’re feeding symbols into the automaton one by one. Moreover, T can be represented as a boolean circuit R and this circuit can be the output of some program P executed by some fixed universal Turing machine. We can associate with this object 5 complexity parameters:
The description complexity, which is the length of P.
The computation time complexity, which is the size of R.
The computation space complexity, which is the maximum between the depth of R and log|S|.
The precomputation time complexity, which is the time it takes P to run.
The precomputation space complexity, which is the space P needs to run.
It is then natural to form a single complexity measure by applying a logarithm to the times and taking a linear combination of all 5 (we apply a logarithm so that a brute force search over n bits is roughly equivalent to hard-coding n bits). The coefficients in this combination represent the “prices” of the various resources (but we should probably fix the price of description complexity to be 1). Of course not all coefficients must be non-vanishing, it’s just that I prefer to keep maximal generality for now. We will denote this complexity measure C.
We can use such automatons to represent policies, finite POMDP environments and reward functions (ofc not any policy or reward function, but any that can be computed on a machine with finite space). In the case of policies, the computation time/space complexity can be regarded as the time/space cost of applying the “trained” algorithm, whereas the precomputation time/space complexity can be regarded as the time/space cost of training. If we wish, we can also think of the boolean circuit as a recurrent neural network.
We can also use C to define a prior ζ0, by ranging over programs P that output a valid POMDP and assigning probability proportional to 2−C to each instance. (Assuming that the environment has a finite state space might seem restrictive, but becomes quite reasonable if we use a quasi-Bayesian setting with quasi-POMDPs that are not meant to be complete descriptions of the environment; for now we won’t go into details about this.)
Now, return to our policy π. Given g>0, we define that ”π has goal-directed intelligence (at least) g” when there is a suitable prior ζ and utility function U s.t. for any policy π′, if Eζπ′[U]≥Eζπ[U] then C(π′)≥DKL(ζ0||ζ)+C(U)+g. When g=+∞ (i.e. no finite automaton can match the expected utility of π; in particular, this implies π is optimal since any policy can be approximated by a finite automaton), we say that π is “perfectly goal-directed”. Here, DKL(ζ0||ζ) serves as a way to measure the complexity of ζ, which also ensures ζ is non-dogmatic in some rather strong sense.
[EDIT: if we fix U and ζ then g is essentially the same as Yudkowsky’s definition of optimization power if we regard the policy as the “outcome” and use 2−C as our measure on the space of outcomes.]
With this definition we cannot “cheat” by encoding the policy into the prior or into the utility function, since that would allow no complexity difference. Therefore this notion seems like a non-trivial requirement on the policy. On the other hand, this requirement does hold sometimes, because solving the optimization problem can be much more computationally costly than just evaluating the utility function or sampling the prior.
Actually, as opposed to what I claimed before, we don’t need computational complexity bounds for this definition to make sense. This is because the Solomonoff prior is made of computable hypotheses but is uncomputable itself.
Given g>0, we define that ”π has (unbounded) goal-directed intelligence (at least) g” when there is a prior ζ and utility function U s.t. for any policy π′, if Eζπ′[U]≥Eζπ[U] then K(π′)≥DKL(ζ0||ζ)+K(U)+g. Here, ζ0 is the Solomonoff prior and K is Kolmogorov complexity. When g=+∞ (i.e. no computable policy can match the expected utility of π; in particular, this implies π is optimal since any policy can be approximated by a computable policy), we say that π is “perfectly (unbounded) goal-directed”.
Compare this notion to the Legg-Hutter intelligence measure. The LH measure depends on the choice of UTM in radical ways. In fact, for some UTMs, AIXI (which is the maximum of the LH measure) becomes computable or even really stupid. For example, it can always keep taking the same action because of the fear that taking any other action leads to an inescapable “hell” state. On the other hand, goal-directed intelligence differs only by O(1) between UTMs, just like Kolmogorov complexity. A perfectly unbounded goal-directed policy has to be uncomputable, and the notion of which policies are such doesn’t depend on the UTM at all.
I think that it’s also possible to prove that intelligence is rare, in the sense that, for any computable stochastic policy, if we regard it as a probability measure over deterministic policies, then for any ϵ>0 there is g s.t. the probability to get intelligence at least g is smaller than ϵ.
Also interesting is that, for bounded goal-directed intelligence, increasing the prices can only decrease intelligence by O(1), and a policy that is perfectly goal-directed w.r.t. lower prices is also such w.r.t. higher prices (I think). In particular, a perfectly unbounded goal-directed policy is perfectly goal-directed for any price vector. Informally speaking, an agent that is very smart relatively to a context with cheap computational resources is still very smart relatively to a context where they are expensive, which makes intuitive sense.
If we choose just one computational resource, we can speak of the minimal price for which a given policy is perfectly goal-directed, which is another way to measure intelligence with a more restricted domain. Curiously, our bounded Solomonoff-like prior has the shape of a Maxwell-Boltzmann distribution in which the prices are thermodynamic parameters. Perhaps we can regard the minimal price as the point of a phase transition.
Some problems to work on regarding goal-directed intelligence. Conjecture 5 is especially important for deconfusing basic questions in alignment, as it stands in opposition to Stuart Armstrong’s thesis about the impossibility to deduce preferences from behavior alone.
Conjecture. Informally: It is unlikely to produce intelligence by chance. Formally: Denote Π the space of deterministic policies, and consider some μ∈ΔΠ. Suppose μ is equivalent to a stochastic policy π∗. Then, Eπ∼μ[g(π)]=O(C(π∗)).
Find an “intelligence hierarchy theorem”. That is, find an increasing sequence {gn} s.t. for every n, there is a policy with goal-directed intelligence in (gn,gn+1) (no more and no less).
What is the computational complexity of evaluating g given (i) oracle access to the policy or (ii) description of the policy as a program or automaton?
What is the computational complexity of producing a policy with given g?
Conjecture. Informally: Intelligent agents have well defined priors and utility functions. Formally: For every (U,ζ) with C(U)<∞ and DKL(ζ0||ζ)<∞, and every ϵ>0, there exists g∈(0,∞) s.t. for every policy π with intelligence at least g w.r.t. (U,ζ), and every (~U,~ζ) s.t.π has intelligence at least g w.r.t. them, any optimal policies π∗,~π∗ for (U,ζ) and (~U,~ζ) respectively satisfy Eζ~π∗[U]≥Eζπ∗[U]−ϵ.
re: #5, that doesn’t seem to claim that we can infer U given their actions, which is what the impossibility of deducing preferences is actually claiming. That is, assuming 5, we still cannot show that there isn’t some U1≠U2 such that π∗(U1,ζ)=π∗(U2,ζ).
(And as pointed out elsewhere, it isn’t Stuart’s thesis, it’s a well known and basic result in the decision theory / economics / philosophy literature.)
re: #5, that doesn’t seem to claim that we can infer U given their actions, which is what the impossibility of deducing preferences is actually claiming.
You misunderstand the intent. We’re talking about inverse reinforcement learning. The goal is not necessarily inferring the unknown U, but producing some behavior that optimizes the unknown U. Ofc if the policy you’re observing is optimal then it’s trivial to do so by following the same policy. But, using my approach we might be able to extend it into results like “the policy you’re observing is optimal w.r.t. certain computational complexity, and your goal is to produce an optimal policy w.r.t. higher computational complexity.”
(Btw I think the formal statement I gave for 5 is false, but there might be an alternative version that works.)
(And as pointed out elsewhere, it isn’t Stuart’s thesis, it’s a well known and basic result in the decision theory / economics / philosophy literature.)
I am referring to this and related work by Armstrong.
Epistemic status: Leaning heavily into inside view, throwing humility to the winds.
Imagine TAI is magically not coming (CDT-style counterfactual[1]). Then, the most notable-in-hindsight feature of modern times might be the budding of mathematical metaphysics (Solomonoff induction, AIXI, Yudkowsky’s “computationalist metaphilosophy”[2], UDT, infra-Bayesianism...) Perhaps, this will lead to an “epistemic revolution” comparable only with the scientific revolution in magnitude. It will revolutionize our understanding of the scientific method (probably solving the interpretation of quantum mechanics[3], maybe quantum gravity, maybe boosting the soft sciences). It will solve a whole range of philosophical questions, some of which humanity was struggling with for centuries (free will, metaethics, consciousness, anthropics...)
But, the philosophical implications of the previous epistemic revolution were not so comforting (atheism, materialism, the cosmic insignificance of human life)[4]. Similarly, the revelations of this revolution might be terrifying[5]. In this case, it remains to be seen which will seem justified in hindsight: the Litany of Gendlin, or the Lovecraftian notion that some knowledge is best left alone (and I say this as someone fully committed to keep digging into this mine of Khazad-dum).
The EDT-style counterfactual “TAI is not coming” would imply that a lot of my thinking on related topics is wrong which would yield different conclusions. The IB-style counterfactual (conjunction of infradistributions) would probably be some combination of the above with “Nirvana” (contradiction) and “what if I tried my hardest to prevent TAI from coming”, which is also not my intent here.
I mean the idea that philosophical questions can be attacked by reframing them as computer science questions (“how an algorithm feels from inside” et cetera). The name “computationalist metaphilosophy” is my own, not Yudkowsky’s.
I’m not sure what you mean by CDT- and EDT-style counterfactuals. I have some guesses but please clarify. I think EDT-style counterfactual means, assuming I am a bayesian reasoner, just conditioning on the event “TAI won’t come”, so it’s thinking about the distribution P(O | TAI won’t come).
One could think that the CDT-counterfactual you’re considering means thinking about the distribution P(O | do(TAI doesn’t come)) where do is the do operator from Judea Pearl’s do calculus for causality. In simple words, this means that we consider the world just like ours but whenever someone tries to launch a TAI, god’s intervention (that doesn’t make sense together with everything we know about physics) prevents it from working. But I think this is not what you mean.
My best guess of what counterfactual you mean is as follows. Among all possible sets laws of physics (or, alternatively, Turing machines running which leads to existence of physical realities), you guess that there exists a set of laws that produces a physical reality where there will appear a civilization approximately (but not exactly) like hours and they’ll have a 21-st century approximately like hours, but under their physical laws there won’t be TAI. And you want to analyze what’s going to happen with that civilization.
You seem to be implying that they will be terrifying for the exact opposite reasons why the previous epistemic revolution’s philosophical implications were.
What do you mean by “exact opposite reasons”? To me, it seems like continuation of the same trend of humiliating the human ego:
you are not going to live forever
yes, you are mere atoms
your planet is not the center of the universe
even your sun is not special
your species is related to the other species that you consider inferior
instead of being logical, your mind is a set of short-sighted agents fighting each other
Followed by:
even your reality is not special
your civilization is too stupid to stop doing the thing(s) that will predictably kill all of you
Probably not too original but I haven’t seen it clearly written anywhere.
There are several ways to amplify imitators with different safety-performance tradeoffs. This is something to consider when designing IDA-type solutions.
Amplifying by objective time: The AI is predicting what the user(s) will output after thinking about a problem for a long time. This method is the strongest, but also the least safe. It is the least safe because malign AI might exist in the future, which affects the prediction, which creates an attack vector for future malign AI to infiltrate the present world. We can try to defend by adding a button for “malign AI is attacking”, but that still leaves us open to surprise takeovers in which there is no chance to press the button.
Amplifying by subjective time: The AI is predicting what the user(s) will output after thinking about a problem for a short time, where in the beginning they are given the output of a similar process that ran for one iteration less. So, this simulates a “groundhog day” scenario where the humans wake up in the same objective time period over and over without memory of the previous iterations but with a written legacy. This is weaker than amplifying by objective time, because learning previous results is an overhead, and illegible intuitions might be hard to transmit. This is safer than amplifying by objective time, but if there is some probability of malign AI created in the short time period, there is still an attack vector. The malign AI leakage in this method is roughly proportional to subjective time of simulation times the present rate of malign AI takeover, as opposed to amplification by objective time where leakage is proportional to subjective time of simulation times some average future rate of malign AI takeover. However, by the time we are able to create this benign AI, the present rate of malign AI takeover might also be considerable.
Amplifying by probability: We allow the user(s) to choose “success” or “failure” (or some continuous metric) after completing their work, and make the AI skew the distribution of predictions toward success. This is similar to amplifying by subjective time without any transmission of information. It is weaker and about as safe. The potential advantage is, lower sample complexity: the AI only needs to have a reliable distribution of outcomes after the initial state instead of subsequent states.
Amplifying by parallelization: The AI is predicting the output of many copies of the user working together, by having strictly defined interfaces between the copies, over a time period similar to real time. For example, we can imagine a hierarchical organization where each person gives subtasks to their subordinates. We can then simulate such an organization with a copy of some subset of users in each role. To do this, the AI only needs to learn what a given subset of users would do given a particular task from their supervisors and particular results by their subordinates. This method is weaker than previous methods since it requires that the task at hand can be parallelized. But, it is also the safest since the rate of malign AI takeover is only amplified by O(1) compared to the background. [EDIT: Actually, it’s not safer than subjective time because the AI would sample the external world independently for each node in the organization. To avoid this, we would need to somehow define a correspondence between the outcome sets of worlds in which the user was queried at different nodes, and I don’t know how to do this.]
A complete solution can try to combine all of those methods, by simulating a virtual organization where the members can control which method is applied at every point. This way they can strive for the optimal risk-performance balance: parallelize everything that can be parallelized and amplify otherwise tasks that cannot be parallelized, change the subjective/objective time balance based on research into malign AI timelines etc.
More observations about this attack vector (“attack from counterfactuals”). I focus on “amplifying by subjective time”.
The harder the takeoff the more dangerous this attack vector: During every simulation cycle, ability to defend against simulated malign AI depends on the power of the defense system in the beginning of the cycle[1]. On the other hand, the capability of the attacker depends on its power in the end of the cycle. Therefore, if power grows very fast this is bad news for the defender. On the other hand, if power grows very slowly, the defender wins because defense is probably easier.
Inner control of anchor makes system safer: Given a fixed objective time anchor, the more times the simulation goes through a cycle, the higher its exposure to the threat, since improving the defense system takes time even if you have all the knowledge. Therefore, we want to give the simulated user the ability to move the objective time anchor forward in time, in order to benefit from improvements in the defense system.
Additional information about the external world makes system safer: Suppose our subjective probability of a malign AI successfully attacking during a single simulation cycle is 0.1%. Then performing 1000 cycles superficially yields a failure probability of about 37%. However, this is only the case if attacks on different cycles are independent random variables. If the AI knows as much about the possibility of such an attack as we do, this is a justified assumption. However, if the AI knows more, then, from our subjective perspective, these variables are positively correlated. In particular, if the AI knows with certainty whether a successful attack would occur or not the failure probability over 1000 cycles goes back to 0.1%. Therefore, it is beneficial to give the AI extra information about the world, just so it can estimate the probability of attack with more confidence.
More precisely, it is somewhat better than this since, if we are at some late cycle among a sequence of cycles anchored to same point in objective time, the simulated user can use knowledge generated in previous cycles to improve the defense system during the current cycle. So, early cycles contribute more to the risk.
I haverepeatedlyargued for a departure from pure Bayesianism that I call “quasi-Bayesianism”. But, coming from a LessWrong-ish background, it might be hard to wrap your head around the fact Bayesianism is somehow deficient. So, here’s another way to understand it, using Bayesianism’s own favorite trick: Dutch booking!
Consider a Bayesian agent Alice. Since Alice is Bayesian, ey never randomize: ey just follow a Bayes-optimal policy for eir prior, and such a policy can always be chosen to be deterministic. Moreover, Alice always accepts a bet if ey can choose which side of the bet to take: indeed, at least one side of any bet has non-negative expected utility. Now, Alice meets Omega. Omega is very smart so ey know more than Alice and moreover ey can predict Alice. Omega offers Alice a series of bets. The bets are specifically chosen by Omega s.t. Alice would pick the wrong side of each one. Alice takes the bets and loses, indefinitely. Alice cannot escape eir predicament: ey might know, in some sense, that Omega is cheating em, but there is no way within the Bayesian paradigm to justify turning down the bets.
A possible counterargument is, we don’t need to depart far from Bayesianism to win here. We only need to somehow justify randomization, perhaps by something like infinitesimal random perturbations of the belief state (like with reflective oracles). But, in a way, this is exactly what quasi-Bayesianism does: a quasi-Bayes-optimal policy is in particular Bayes-optimal when the prior is taken to be in Nash equilibrium of the associated zero-sum game. However, Bayes-optimality underspecifies the policy: not every optimal reply to a Nash equilibrium is a Nash equilibrium.
This argument is not entirely novel: it is just a special case of an environment that the agent cannot simulate, which is the original motivation for quasi-Bayesianism. In some sense, any Bayesian agent is dogmatic: it dogmatically beliefs that the environment is computationally simple, since it cannot consider a hypothesis which is not. Here, Omega exploits this false dogmatic belief.
Bayeseans are allowed to understand that there are agents with better estimates than they have. And that being offered a bet _IS_ evidence that the other agent THINKS they have an advantage.
Randomization (aka “mixed strategy”) is well-understood as the rational move in games where opponents are predicting your choices. I have read nothing that would even hint that it’s unavailable to Bayesean agents. The relevant probability (updated per Bayes’s Rule) would be “is my counterpart trying to minimize my payout based on my choices”.
edit: I realize you may be using a different definition of “bayeseanism” than I am. I’m thinking humans striving for rational choices, which perforce includes the knowledge of incomplete computation and imperfect knowledge. Naive agents can be imagined that don’t have this complexity. Those guys are stuck, and Omega’s gonna pwn them.
I’m thinking humans striving for rational choices,
It feels like there’s better words for this like rationality, whereas bayesianism is a more specific philosophy about how best to represent and update beliefs.
And here I thought the reason was going to be that Bayesianism doesn’t appear to include the cost of computation. (Thus, the usual dutch book arguments should be adjusted so that “optimal betting” does not leave one worse off for having payed, say, an oracle, too much for computation.)
Game theory is widely considered the correct description of rational behavior in multi-agent scenarios. However, real world agents have to learn, whereas game theory assumes perfect knowledge, which can be only achieved in the limit at best. Bridging this gap requires using multi-agent learning theory to justify game theory, a problem that is mostly open (but some results exist). In particular, we would like to prove that learning agents converge to game theoretic solutions such as Nash equilibria (putting superrationality aside: I think that superrationality should manifest via modifying the game rather than abandoning the notion of Nash equilibrium).
The simplest setup in (non-cooperative) game theory is normal form games. Learning happens by accumulating evidence over time, so a normal form game is not, in itself, a meaningful setting for learning. One way to solve this is replacing the normal form game by a repeated version. This, however, requires deciding on a time discount. For sufficiently steep time discounts, the repeated game is essentially equivalent to the normal form game (from the perspective of game theory). However, the full-fledged theory of intelligent agents requires considering shallow time discounts, otherwise there is no notion of long-term planning. For shallow time discounts, the game theory of a repeated game is very different from the game theory of the original normal form game. In fact, the folk theorem asserts that any payoff vector above the maximin of each player is a possible Nash payoff. So, proving convergence to a Nash equilibrium amounts (more or less) to proving converges to at least the maximin payoff. This is possible using incomplete models, but doesn’t seem very interesting: to receive the maximin payoff, the agents only have to learn the rules of the game, they need not learn the reward functions of the other players or anything else about them.
We arrive at the question, what setting is realistic (in the sense of involving learning with shallow time discount) and is expected to produce Nash equilibria for a normal form game? I suggest the following. Instead of a fixed set of agents repeatedly playing against each other, we consider a population of agents that are teamed-off randomly on each round of the game. The population is assumed to be large enough for agents not to encounter each other more than once. This can be formalized as follows. Let Ai be the pure strategy set of the i-th agent and O:=∏iAi the set of pure outcomes. The set of n-round outcome histories is On. The population of agents on the n-round can then be described as a probability measureμn∈ΔOn. Suppose the policy of the i-th player (that is, of all the agents that take the role of the i-th player) is πi:On→ΔAi. Then we can define a time evolution rule that produces μn+1 from μn. This rule works as follows: in order to sample μn+1 we sample μn once per player (this is the history the given player has seen), sample the policy of each player on its own history, and produce a new history by appending the resulting outcome to one of the old histories (it doesn’t matter which). A set of policies is considered to be in equilibrium, when for any i, and any alternative policy π′i, letting π′i play against the same population (i.e. all other copies of the i-th player still play πi) doesn’t improve expected utility. In other words, on each round the “mutant” agent retains its own history but the other player histories are still sampled from the same μn. It is easy to see that any equilibrium payoff in this setting is a Nash payoff in the original normal form game. We can then legitimately ask whether taking the πi to be learning algorithms would result in convergence to a Nash payoff in the γ→1 (shallow time discount) limit.
For example, consider the Prisoner’s dilemma. In the repeated Prisoner’s dilemma with shallow time discount, CC is an equilibrium because of the tit-for-tat policy. On the other hand, in the “population” (massively multi-player?) repeated Prisoner’s dilemma, DD is the only equilibrium. Tit-for-tat doesn’t work because a single “defect bot” can exploit a population of tit-for-tats: on each round it plays with a new opponent that doesn’t know the defect bot defected on the previous round.
Note that we get a very different setting if we allow the players to see each other’s histories, more similar (equivalent?) to the regular repeated game. For example, in the Prisoner’s Dilemma we have a version of tit-for-tat that responds to what its current opponent played in its previous round (against a different opponent). This may be regarded as a confirmation of the idea that agents that know each other’s source code are effectively playing a repeated game: in this setting, knowing the source code amounts to knowing the history.
We can modify the population game setting to study superrationality. In order to do this, we can allow the agents to see a fixed size finite portion of the their opponents’ histories. This should lead to superrationality for the same reasons I discussedbefore. More generally, we can probably allow each agent to submit a finite state automaton of limited size, s.t. the opponent history is processed by the automaton and the result becomes known to the agent.
What is unclear about this is how to define an analogous setting based on source code introspection. While arguably seeing the entire history is equivalent to seeing the entire source code, seeing part of the history, or processing the history through a finite state automaton, might be equivalent to some limited access to source code, but I don’t know to define this limitation.
EDIT: Actually, the obvious analogue is processing the source code through a finite state automaton.
Instead of postulating access to a portion of the history or some kind of limited access to the opponent’s source code, we can consider agents with full access to history / source code but finite memory. The problem is, an agent with fixed memory size usually cannot have regret going to zero, since it cannot store probabilities with arbitrary precision. However, it seems plausible that we can usually get learning with memory of size O(log11−γ). This is because something like “counting pieces of evidence” should be sufficient. For example, if consider finite MDPs, then it is enough to remember how many transitions of each type occurred to encode the belief state. There question is, does assuming O(log11−γ) memory (or whatever is needed for learning) is enough to reach superrationality.
What do you mean by equivalent? The entire history doesn’t say what the opponent will do later or would do against other agents, and the source code may not allow you to prove what the agent does if it involves statements that are true but not provable.
For a fixed policy, the history is the only thing you need to know in order to simulate the agent on a given round. In this sense, seeing the history is equivalent to seeing the source code.
The claim is: In settings where the agent has unlimited memory and sees the entire history or source code, you can’t get good guarantees (as in the folk theorem for repeated games). On the other hand, in settings where the agent sees part of the history, or is constrained to have finite memory (possibly of size O(log11−γ)?), you can (maybe?) prove convergence to Pareto efficient outcomes or some other strong desideratum that deserves to be called “superrationality”.
In the previous “population game” setting, we assumed all players are “born” at the same time and learn synchronously, so that they always play against players of the same “age” (history length). Instead, we can consider a “mortal population game” setting where each player has a probability 1−γ to die on every round, and new players are born to replenish the dead. So, if the size of the population is N (we always consider the “thermodynamic” N→∞ limit), N(1−γ) players die and the same number of players are born on every round. Each player’s utility function is a simple sum of rewards over time, so, taking mortality into account, effectively ey have geometric time discount. (We could use age-dependent mortality rates to get different discount shapes, or allow each type of player to have different mortality=discount rate.) Crucially, we group the players into games randomly, independent of age.
As before, each player type i chooses a policy . (We can also consider the case where players of the same type may have different policies, but let’s keep it simple for now.) In the thermodynamic limit, the population is described as a distribution over histories, which now are allowed to be of variable length: μn∈ΔO∗. For each assignment of policies to player types, we get dynamics μn+1=Tπ(μn) where Tπ:ΔO∗→ΔO∗. So, as opposed to immortal population games, mortal population games naturally give rise to dynamical systems.
If we consider only the age distribution, then its evolution doesn’t depend on π and it always converges to the unique fixed point distribution ζ(k)=(1−γ)γk. Therefore it is natural to restrict the dynamics to the subspace of ΔO∗ that corresponds to the age distribution ζ. We denote it P.
Does the dynamics have fixed points?O∗ can be regarded as a subspace of (O⊔{⊥})ω. The later is compact (in the product topology) by Tychonoff’s theorem and Polish, but O∗ is not closed. So, w.r.t. the weak topology on probability measure spaces, Δ(O⊔{⊥})ω is also compact but ΔO∗ isn’t. However, it is easy to see that Pis closed in Δ(O⊔{⊥})ω and therefore compact. It may also be regarded as a convex subset of an appropriate Banach space (the dual of the space of Lipschitz functions on some metrization of (O⊔{⊥})ω). Moreover, it is easy to see Tπ is continuous (for populations that are close in the Kantorovich-Rubinstein metric, only the old players may have very different distributions, but old players are a small fraction of the population so their effect on the next round is small). By the Schauder fixed-point theorem, it follows that Tπ has a fixed point.
What are the fixed points like? Of course it depends on π. In a fixed point, every player observes a sequence of IID plays in all of eir games. Therefore, if π satisfies the (very mild!) learning-theoretic desideratum that, upon observing an IID sequence, it converges to optimal response in the γ→1 limit, then, in the same limit, fixed points are Nash equilibria. This works even for extremely simple learning algorithms, such as “assume the plays in the next game will be sampled from a random past game”, and it works for any Bayesian or “quasi-Bayesian” (i.e. using incomplete/fuzzy models) agent that includes all IID processes in its prior.
This raises a range of interesting questions:
Are any/all of the fixed points attractors?
Does convergence to a fixed point occur for all or at least almost all initial conditions?
Do all Nash equilibria correspond to fixed points?
Do stronger game theoretic solution concepts (e.g. proper equilibria) have corresponding dynamical properties?
Mortal population games are obviously reminiscent of evolutionary game theory. However, there are substantial differences. In mortal population games, the game doesn’t have to be symmetric, we consider a single policy rather than many competing policies, the policies learn from experience instead of corresponding to fixed strategies, and mortality rate doesn’t depend on the reward. In evolutionary game theory, convergence usually cannot be guaranteed. For example, in the rock-scissors-paper game, the population may cycle among the different strategies. On the other hand, in mortal population games, if the game is two-player zero-sum (which includes rock-paper-scissors), and the policy is quasi-Bayesian with appropriate prior, convergence is guaranteed. This is because each player can easily learn to guarantee maximin payoff. Continuity arguments probably imply that at least for small perturbations of zero-sum, there will still be convergence. This leads to some hope that convergence can be guaranteed even in general games, or at least under some relatively mild conditions.
Here’s a question inspired by thinking about Turing RL, and trying to understand what kind of “beliefs about computations” should we expect the agent to acquire.
Does mathematics have finite information content?
First, let’s focus on computable mathematics. At first glance, the answer seems obviously “no”: because of the halting problem, there’s no algorithm (i.e. a Turing machine that always terminates) which can predict the result of every computation. Therefore, you can keep learning new facts about results of computations forever. BUT, maybe most of those new facts are essentially random noise, rather than “meaningful” information?
Is there a difference of principle between “noise” and “meaningful content”? It is not obvious, but the answer is “yes”: in algorithmic statistics there is the notion of “sophistication” which measures how much “non-random” information is contained in some data. In our setting, the question can be operationalized as follows: is it possible to have an algorithm A plus an infinite sequence of bits R, s.t.R is random in some formal sense (e.g. Martin-Lof) and A can decide the output of any finite computation if it’s also given access to R?
The answer to the question above is “yes”! Indeed, Chaitin’s constant is Martin-Lof random. Given access to Chaitin’s constant, it is possible to construct a halting oracle, therefore A can decide whether the computation halts, and if it does, run it (and if doesn’t, output N/A or whatever).
[EDIT: Actually, this is not quite right. The way you use Chaitin’s constant to emulate a halting oracle produces something that’s only guaranteed to halt if you give it the correct Chaitin’s constant.]
But, this is a boring solution. In practice we are interested at efficient methods of answering mathematical questions, and beliefs acquired by resource bounded agents. Hence, the question becomes: given a resource bound B (e.g. a bound on space or time complexity), is it possible to have A and R similar to above, s.t.A respects the bound B and R is pseudorandom in some formal sense w.r.t. the bound B?
[EDIT: I guess that the analogous thing to the unbounded setting would be, A only has to respect B when given the correct R. But the real conclusion is probably that we should look for something else instead, e.g. some kind of infradistribution.]
This is a fun question, because any answer would be fascinating in its own way: either computable mathematics has finite content in some strong formal sense (!) or mathematics is infinitely sophisticated in some formal sense (!)
We can also go in the other direction along the “hierarchy of feasibility”, although I’m not sure how useful is that. Instead of computable mathematics, let’s consider determining the truth (not provability, but actual truth) of sentences in e.g. Peano Arithmetic. Does A and R as above still exist? This would require e.g. a Martin-Lof random sequence which allows making any finite number of Turing jumps.
Wikipedia claims that every sequence is Turing reducible to a random one, giving a positive answer to the non-resource-bounded version of any question of this form. There might be a resource-bounded version of this result as well, but I’m not sure.
Epistemic status: no claims to novelty, just (possibly) useful terminology.
[EDIT: I increased all the class numbers by 1 in order to admit a new definition of “class I”, see child comment.]
I propose a classification on AI systems based on the size of the space of attack vectors. This classification can be applied in two ways: as referring to the attack vectors a priori relevant to the given architectural type, or as referring to the attack vectors that were not mitigated in the specific design. We can call the former the “potential” class and the latter the “effective” class of the given system. In this view, the problem of alignment is designing potential class V (or at least IV) systems are that effectively class 0 (or at least I-II).
Class II: Systems that only ever receive synthetic data that has nothing to do with the real world
Examples:
AI that is trained to learn Go by self-play
AI that is trained to prove random mathematical statements
AI that is trained to make rapid predictions of future cell states in the game of life for random initial conditions
AI that is trained to find regularities in sequences corresponding to random programs on some natural universal Turing machine with bounded runtime
Class II systems by and large don’t admit any attack vectors. [EDIT: Inaccurate, see child comment]
Such systems might have higher effective class if bugs in the implementation lead to real-world data leaking into the system, or if the ostensibly synthetic data reveals something important about the world via the choices made by its designers (for example, a video game with complex rules inspired by the real world).
Class III: Systems for which there is no distribution shift between training and deployment, and also the label space is small
Example: AI that is trained to distinguish between cat images and dog images, and the images selected for training are a perfectly representative sample of images used in deployment.
Class III systems admit attacks by non-Cartesian daemons.
If the label space is large, a Cartesian daemon can choose a low probability for randomly producing a malign label, such that there is a significant probability that this won’t happen in training but will happen in deployment. This moves the system to class IV. If there is distribution shift, a Cartesian daemon can distinguish between training and deployment and use it to perform a “treacherous turn” attack. This also moves the system to class IV.
Such systems have lower effective class if non-Cartesian daemons are mitigated, for example by well-designed applications of homomorphic cryptography. They have higher effective class if deployed in a setting which does involve distributional shift, perhaps unanticipated by the designers.
Class IV: Systems which are non-agentic but do involve distribution shift or large label space
Examples:
AI that learns to imitate humans
AI that learns to predict the stock market
Generative language models
Class IV systems admit attacks by Cartesian and non-Cartesian daemons. [EDIT: Also attack from counterfactuals. The latter requires a large label space and doesn’t require a distribution shift per se.]
Such systems have lower effective class if Cartesian daemons are mitigated, for example by carefully shaping the prior / inductive bias and applying some sort of confidence threshold / consensus algorithm. They can be effective class V if not designed to avoid self-fulfilling prophecies and/or incentives to mispredict at present to improve prediction in the future.
Class V: Agentic systems
Examples:
AI that trades in the stock market
AI that optimizes long-term ad revenue
AI that defends the world against unaligned AIs
Class V systems admit attacks by daemons but are also dangerous by default due to divergence of their utility function from the human utility function.
Such system can have lower effective class if the utility function is very cleverly designed, for example to reliably accomplish learning of human values.
Class II or higher systems might admit an attack vector by daemons that infer the universe from the agent’s source code. That is, we can imagine a malign hypothesis that makes a treacherous turn after observing enough past actions to infer information about the system’s own source code and infer the physical universe from that. (For example, in a TRL setting it can match the actions to the output of a particular program for envelope.) Such daemons are not as powerful as malign simulation hypotheses, since their prior probability is not especially large (compared to the true hypothesis), but might still be non-negligible. Moreover, it is not clear whether the source code can realistically have enough information to enable an attack, but the opposite is not entirely obvious.
To account for this I propose the designate class I systems which don’t admit this attack vector. For the potential sense, it means that either (i) the system’s design is too simple to enable inferring much about the physical universe, or (ii) there is no access to past actions (including opponent actions for self-play) or (iii) the label space is small, which means an attack requires making many distinct errors, and such errors are penalized quickly. And ofc it requires no direct access to the source code.
We can maybe imagine an attack vector even for class I systems, if most metacosmologically plausible universes are sufficiently similar, but this is not very likely. Nevertheless, we can reserve the label class 0 for systems that explicitly rule out even such attacks.
I find it interesting to build simple toy models of the human utility function. In particular, I was thinking about the aggregation of value associated with other people. In utilitarianism this question is known as “population ethics” and is infamously plagued with paradoxes. However, I believe that is the result of trying to be impartial. Humans are very partial and this allows coherent ways of aggregation. Here is my toy model:
Let Alice be our viewpoint human. Consider all social interactions Alice has, categorized by some types or properties, and assign a numerical weight to each type of interaction. Let it(A,B)>0 be the weight of the interaction person A had with person B at time t (if there was no interaction at this time then it(A,B)=0). Then, we can define Alice’s affinity to Bob as
afft(Alice,Bob):=t∑s=−∞αt−sis(Alice,Bob)
Here α∈(0,1) is some constant. Ofc αt−s can be replaced by many other functions.
Now, we can the define the social distance of Alice to Bob as
Here β>0 is some constant, and the power law was chosen rather arbitrarily, there are many functions of aff that can work. Dead people should probably count in the infimum, but their influence wanes over time since they don’t interact with anyone (unless we count consciously thinking about a person as an interaction, which we might).
This is a time-dependent metric (or quasimetric, if we allow for asymmetric interactions such as thinking about someone or admiring someone from afar) on the set of people. If i is bounded and there is a bounded number of people Alice can interact with at any given time, then there is some C>1 s.t. the number of people within distance r from Alice is O(Cr). We now define the reward as
rt(Alice):=∑pλdt(Alice,p)wt(p)
Here λ∈(0,1C) is some constant and wt(p) is the “welfare” of person p at time t, or whatever is the source of value of people for Alice. Finally, the utility function is a time discounted sum of rewards, probably not geometric (because hyperbolic discounting is a thing). It is also appealing to make the decision rule to be minimax-regret over all sufficiently long time discount parameters, but this is tangential.
Notice how the utility function is automatically finite and bounded, and none of the weird paradoxes of population ethics and infinitary ethics crop up, even if there is an infinite number of people in the universe. I like to visualize people space a tiling of hyperbolic space, with Alice standing in the center of a Poincare or Beltrami-Klein model of it. Alice’s “measure of caring” is then proportional to volume in the model (this probably doesn’t correspond to exactly the same formula but it’s qualitatively right, and the formula is only qualitative anyway).
From a learning-theoretic perspective, we can reformulate the problem of embedded agency as follows: What kind of agent, and in what conditions, can effectively plan for events after its own death? For example, Alice bequeaths eir fortune to eir children, since ey want them be happy even when Alice emself is no longer alive. Here, “death” can be understood to include modification, since modification is effectively destroying an agent and replacing it by different agent[1]. For example, Clippy 1.0 is an AI that values paperclips. Alice disabled Clippy 1.0 and reprogrammed it to value staples before running it again. Then, Clippy 2.0 can be considered to be a new, different agent.
First, in order to meaningfully plan for death, the agent’s reward function has to be defined in terms of something different than its direct perceptions. Indeed, by definition the agent no longer perceives anything after death. Instrumental reward functions are somewhat relevant but still don’t give the right object, since the reward is still tied to the agent’s actions and observations. Therefore, we will consider reward functions defined in terms of some fixed ontology of the external world. Formally, such an ontology can be an incomplete[2] Markov chain, the reward function being a function of the state. Examples:
The Markov chain is a representation of known physics (or some sector of known physics). The reward corresponds to the total mass of diamond in the world. To make this example work, we only need enough physics to be able to define diamonds. For example, we can make do with quantum electrodynamics + classical gravity and have the Knightian uncertainty account for all nuclear and high-energy phenomena.
The Markov chain is a representation of people and social interactions. The reward correspond to concepts like “happiness” or “friendship” et cetera. Everything that falls outside the domain of human interactions is accounted by Knightian uncertainty.
The Markov chain is Botworld with some of the rules left unspecified. The reward is the total number of a particular type of item.
Now we need to somehow connect the agent to the ontology. Essentially we need a way of drawing Cartesian boundaries inside the (a priori non-Cartesian) world. We can accomplish this by specifying a function that assigns an observation and projected action to every state out of some subset of states. Entering this subset corresponds to agent creation, and leaving it corresponds to agent destruction. For example, we can take the ontology to be Botworld + marked robot and the observations and actions be the observations and actions of that robot. If we don’t want marking a particular robot as part of the ontology, we can use a more complicated definition of Cartesian boundary that specifies a set of agents at each state plus the data needed to track these agents across time (in this case, the observation and action depend to some extent on the history and not only the current state). I will leave out the details for now.
Finally, we need to define the prior. To do this, we start by choosing some prior over refinements of the ontology. By “refinement”, I mean removing part of the Knightian uncertainty, i.e. considering incomplete hypotheses which are subsets of the “ontological belief”. For example, if the ontology is underspecified Botworld, the hypotheses will specify some of what was left underspecified. Given such a “objective” prior and a Cartesian boundary, we can construct a “subjective” prior for the corresponding agent. We transform each hypothesis via postulating that taking an action that differs from the projected action leads to “Nirvana” state. Alternatively, we can allow for stochastic action selection and use the gambler construction.
Does this framework guarantee effective planning for death? A positive answer would correspond to some kind of learnability result (regret bound). To get learnability, will first need that the reward is either directly on indirectly observable. By “indirectly observable” I mean something like with semi-instrumental reward functions, but accounting for agent mortality. I am not ready to formulate the precise condition atm. Second, we need to consider an asymptotic in which the agent is long lived (in addition to time discount being long-term), otherwise it won’t have enough time to learn. Third (this is the trickiest part), we need the Cartesian boundary to flow with the asymptotic as well, making the agent “unspecial”. For example, consider Botworld with some kind of simplicity prior. If I am a robot born at cell zero and time zero, then my death is an event of low description complexity. It is impossible to be confident about what happens after such a simple event, since there will always be competing hypotheses with different predictions and a probability that is only lower by a factor of Ω(1). On the other hand, if I am a robot born at cell 2439495 at time 9653302 then it would be surprising if the outcome of my death would be qualitatively different from the outcome of the death of any other robot I observed. Finding some natural, rigorous and general way to formalize this condition is a very interesting problem. Of course, even without learnability we can strive for Bayes-optimality or some approximation thereof. But, it is still important to prove learnability under certain conditions to test that this framework truly models rational reasoning about death.
Additionally, there is an intriguing connection between some of these ideas and UDT, if we consider TRL agents. Specifically, a TRL agent can have a reward function that is defined in terms of computations, exactly like UDT is often conceived. For example, we can consider an agent whose reward is defined in terms of a simulation of Botworld, or in terms of taking expected value over a simplicity prior over many versions of Botworld. Such an agent would be searching for copies of itself inside the computations it cares about, which may also be regarded as a form of “embeddedness”. It seems like this can be naturally considered a special case of the previous construction, if we allow the “ontological belief” to include beliefs pertaining to computations.
Learning theory distinguishes between two types of settings: realizable and agnostic (non-realizable). In a realizable setting, we assume that there is a hypothesis in our hypothesis class that describes the real environment perfectly. We are then concerned with the sample complexity and computational complexity of learning the correct hypothesis. In an agnostic setting, we make no such assumption. We therefore consider the complexity of learning the best approximation of the real environment. (Or, the best reward achievable by some space of policies.)
In offline learning and certain varieties of online learning, the agnostic setting is well-understood. However, in more general situations it is poorly understood. The only agnostic result for long-term forecasting that I know is Shalizi 2009, however it relies on ergodicity assumptions that might be too strong. I know of no agnostic result for reinforcement learning.
Quasi-Bayesianism was invented to circumvent the problem. Instead of considering the agnostic setting, we consider a “quasi-realizable” setting: there might be no perfect description of the environment in the hypothesis class, but there are some incomplete descriptions. But, so far I haven’t studied quasi-Bayesian learning algorithms much, so how do we know it is actually easier than the agnostic setting? Here is a simple example to demonstrate that it is.
Consider a multi-armed bandit, where the arm space is [0,1]. First, consider the follow realizable setting: the reward is a deterministic function r:[0,1]→[0,1] which is known to be a polynomial of degree d at most. In this setting, learning is fairly easy: it is enough to sample d+1 arms in order to recover the reward function and find the optimal arm. It is a special case of the general observation that learning is tractable when the hypothesis space is low-dimensional in the appropriate sense.
Now, consider a closely related agnostic setting. We can still assume the reward function is deterministic, but nothing is known about its shape and we are still expected to find the optimal arm. The arms form a low-dimensional space (one-dimensional actually) but this helps little. It is impossible to predict anything about any arm except those we already tested, and guaranteeing convergence to the optimal arm is therefore also impossible.
Finally, consider the following quasi-realizable setting: each incomplete hypothesis in our class states that the reward function is lower-bounded by a particular polynomial f:[0,1]→[0,1] of degree d at most. Our algorithm needs to converge to a reward which is at least the maximum of maxima of correct lower bounds. So, the desideratum is weaker than in the agnostic case, but we still impose no hard constraint on the reward function. In this setting, we can use the following algorithm. On each step, fit the most optimistic lower bound to those arms that were already sampled, find its maximum and sample this arm next. I haven’t derived the convergence rate, but it seems probable the algorithm will converge rapidly (for low d). This is likely to be a special case of some general result on quasi-Bayesian learning with low-dimensional priors.
Given a hypothesis about the universe, we can tell which programs are running. (This is just the bridge transform.)
Given a program, we can tell whether it is an agent, and if so, which utility function it has[1] (the “evaluating agent” section of the article).
I will now outline how we can use these building blocks to solve both the inner and outer alignment problem. The rough idea is:
For each hypothesis in the prior, check which agents are precursors of our agent according to this hypothesis.
Among the precursors, check whether some are definitely neither humans nor animals nor previously created AIs.
If there are precursors like that, discard the hypothesis (it is probably a malign simulation hypothesis).
If there are no precursors like that, decide which of them are humans.
Follow an aggregate of the utility functions of the human precursors (conditional on the given hypothesis).
Detection
How to identify agents which are our agent’s precursors? Let our agent be G and let H be another agents which exists in the universe according to hypothesis Θ[2]. Then, H is considered to be a precursor of G in universe Θ when there is some H-policy σ s.t. applying the counterfactual ”H follows σ” to Θ (in the usual infra-Bayesian sense) causes G not to exist (i.e. its source code doesn’t run).
A possible complication is, what if Θ implies that H creates G / doesn’t interfere with the creation of G? In this case H might conceptually be a precursor, but the definition would not detect it. It is possible that any such Θ would have a sufficiently large description complexity penalty that it doesn’t matter. On the second hand, if Θ is unconditionally Knightian uncertain about H creating G then the utility will be upper bounded by the scenario in which G doesn’t exist, which is liable to make Θ an effectively falsified hypothesis. On the third hand, it seems plausible that the creation of G by H would be contingent on G’s behavior (Newcomb-style, which we know how it works in infra-Bayesianism), in which case Θ is not falsified and the detection works. In any case, there is a possible variant of the definition to avoid the problem: instead of examining only Θ we also examine coarsenings of Θ which are not much more complex to describe (in the hope that some such coarsening would leave the creation of G uncertain).
Notice that any agent whose existence is contingent on G’s policy cannot be detected as a precursor: the corresponding program doesn’t even “run”, because we don’t apply a G-policy-counterfactual to the bridge transform.
Classification
How to decide which precursors are which? One tool we have is the g parameter and the computational resource parameters in the definition of intelligence. In addition we might be able to create a very rough neuroscience-based model of humans. Also, we will hopefully have a lot of information about other AIs that can be relevant. Using these, it might be possible to create a rough benign/malign/irrelevant classifier, s.t.
Humans are classified as “benign”.
Most (by probability mass) malign simulation hypotheses contain at least one precursor classified as “malign”.
Non-human agents that exist in the causal past of our AI in the null (non-simulation) hypothesis are classified as “irrelevant”.
Assistance
Once we detected and classified precursors in each hypothesis, we discard all hypotheses that contain malign precursors. In the remaining hypotheses, we perform some kind of aggregation on the utility functions of the benign precursors (for example, this). The utility functions from different hypotheses are somehow normalized to form the overall utility function. Alternatively, we do a maximal lottery vote for the policy, where each hypothesis is a voter with weight proportional to its prior probability mass.
Inner Alignment
Why can this solve inner alignment? In any model-based approach, the AI doesn’t train the policy directly. Instead, it trains models and uses them to compute the policy. I suspect that the second step cannot create mesa-optimizers, since it only involves control and not learning[3]. Hence, any mesa-optimizer has to originate from the first step, i.e. from the model/hypothesis. And, any plausible physicalist hypothesis which contains a mesa-optimizer has to look like a malign simulation hypothesis.
Outer Alignment
Why can this solve outer alignment? Presumably, we are aggregating human utility functions. This doesn’t assume humans are perfect agents: g can be less than infinity. I suspect that when g<∞ the utility function becomes somewhat ambiguous, but the ambiguity can probably be resolved arbitrarily or maybe via a risk-averse method. What if the AI modifies the humans? Then only pre-modification humans are detected as precursors, and there’s no problem.
Moreover, the entire method can be combined with the Hippocratic principle to avoid catastrophic mistakes out of ignorance (i.e. to go from intent alignment to impact alignment).
We do need a lot more research to fully specify this “utility reconstruction” and check that it satisfies reasonable desiderata. But, the existence of a natural utility-function-dependent measure of intelligence suggests it is possible.
In modern deep RL systems, there might not be a clear line between learning and control. For example, if we use model-free RL to produce the policy for a given hypothesis, then there is learning happening there as well. In such an architecture, the value function or Q-function should be regarded as part of the hypothesis for our purpose.
Then, H is considered to be a precursor of G in universe Θ when there is some H-policy σ s.t. applying the counterfactual ”H follows σ” to Θ (in the usual infra-Bayesian sense) causes G not to exist (i.e. its source code doesn’t run).
A possible complication is, what if Θ implies that H creates G / doesn’t interfere with the creation of G? In this case H might conceptually be a precursor, but the definition would not detect it.
Can you please explain how does this not match the definition? I don’t yet understand all the math, but intuitively, if H creates G / doesn’t interfere with the creation of G, then if H instead followed policy “do not create G/ do interfere with the creation of G”, then G’s code wouldn’t run?
Can you please give an example of a precursor that does match the definition?
The problem is that if Θ implies that H creates G but you consider a counterfactual in which H doesn’t create G then you get an inconsistent hypothesis i.e. a HUC which contains only 0. It is not clear what to do with that. In other words, the usual way of defining counterfactuals in IB (I tentatively named it “hard counterfactuals”) only makes sense when the condition you’re counterfactualizing on is something you have Knightian uncertainty about (which seems safe to assume if this condition is about your own future action but not safe to assume in general). In a child post I suggested solving this by defining “soft counterfactuals” where you consider coarsenings of Θ in addition to Θ itself.
These are notoriously difficult to deal with. The only methods I know are that applicable to other protocols are homomorphic cryptography and quantilization of envelope (external computer) actions. But, in this protocol, they are dealt with the same as Cartesian daemons! At least if we assume a non-Cartesian attack requires an envelope action, the malign hypotheses which are would-be sources of such actions are discarded without giving an opportunity for attack.
Weaknesses
My main concerns with this approach are:
The possibility of major conceptual holes in the definition of precursors. More informal analysis can help, but ultimately mathematical research in infra-Bayesian physicalism in general and infra-Bayesian cartesian/physicalist multi-agent interactions in particular is required to gain sufficient confidence.
The feasibility of a good enough classifier. At present, I don’t have a concrete plan for attacking this, as it requires inputs from outside of computer science.
Inherent “incorrigibility”: once the AI becomes sufficiently confident that it correctly detected and classified its precursors, its plans won’t defer to the users any more than the resulting utility function demands. On the second hand, I think the concept of corrigibility is underspecified so much that I’m not sure it is solved (rather than dissolved) even in the Book. Moreover, the concern can be ameliorated by sufficiently powerful interpretability tools. It is therefore desirable to think more of how to achieve interpretability in this context.
A question that often comes up in discussion of IRL: are agency and values purely behavioral concepts, or do they depend on how the system produces its behavior? The cartesian measure of agency I proposed seems purely behavioral, since it only depends on the policy. The physicalist version seems less so since it depends on the source code, but this difference might be minor: this role of the source is merely telling the agent “where” it is in the universe. However, on closer examination, the physicalist g is far from purely behaviorist, and this is true even for cartesian Turing RL. Indeed, the policy describes not only the agent’s interaction with the actual environment but also its interaction with the “envelope” computer. In a sense, the policy can be said to reflects the agent’s “conscious thoughts”.
This means that specifying an agent requires not only specifying its source code but also the “envelope semantics” C (possibly we also need to penalize for the complexity of C in the definition of g). Identifying that an agent exists requires not only that its source code is running, but also, at least that its history h is C-consistent with the α∈2Γ variable of the bridge transform. That is, for any y∈α we must have dCy for some destiny d⊐h. In other words, we want any computation the agents ostensibly runs on the envelope to be one that is physically manifest (it might be this condition isn’t sufficiently strong, since it doesn’t seem to establish a causal relation between the manifesting and the agent’s observations, but it’s at least necessary).
Notice also that the computational power of the envelope implied by C becomes another characteristic of the agent’s intelligence, together with g as a function of the cost of computational resources. It might be useful to come up with natural ways to quantify this power.
It can be useful to identify and assist specifically the user rather than e.g. any human that ever lived (and maybe some hominids). For this purpose I propose the following method. It also strengthens the protocol by relieving some pressure from other classification criteria.
Given two agents G and H, which can ask which points on G‘s timeline are in the causal past of which points of H‘s timeline. To answer this, consider the counterfactual in which G takes a random action (or sequence of actions) at some point (or interval) on G‘s timeline, and measure the mutual information between this action(s) and H‘s observations at some interval on H’s timeline.
Using this, we can effectively construct a future “causal cone” emanating from the AI’s origin, and also a past causal cone emanating from some time t on the AI’s timeline. Then, “nearby” agents will meet the intersection of these cones for low values of t whereas “faraway” agents will only meet it for high values of t or not at all. To first approximation, the user would be the “nearest” precursor[1] agent i.e. the one meeting the intersection for the minimal t.
More precisely, we expect the user’s observations to have nearly maximal mutual information with the AI’s actions: the user can e.g. see every symbol the AI outputs to the display. However, the other direction is less clear: can the AI’s sensors measure every nerve signal emanating from the user’s brain? To address this, we can fix t to a value s.t. we expect only the user the meet the intersection of cones, and have the AI select the agent which meets this intersection for the highest mutual information threshold.
This probably does not make the detection of malign agents redundant, since AFAICT a malign simulation hypothesis might be somehow cleverly arranged to make a malign agent the user.
More on Counterfactuals
In the parent post I suggested “instead of examining only Θ we also examine coarsenings of Θ which are not much more complex to describe”. A possible elegant way to implement this:
Consider the entire portion ¯Θ of our (simplicity) prior which consists of coarsenings of Θ.
There seems to be an even more elegant way to define causal relationships between agents, or more generally between programs. Starting from a hypothesis Θ∈□(Γ×Φ), for Γ=ΣR, we consider its bridge transform B∈□(Γ×2Γ×Φ). Given some subset of programs Q⊆R we can define Δ:=ΣQ then project B to BΔ∈□(Γ×2Δ)[1]. We can then take bridge transform again to get some C∈□(Γ×2Γ×2Δ). The 2Γ factor now tells us which programs causally affect the manifestation of programs in Q. Notice that by Proposition 2.8 in the IBP article, when Q=R we just get all programs that are running, which makes sense.
Agreement Rules Out Mesa-Optimization
The version of PreDCA without any explicit malign hypothesis filtering might be immune to malign hypotheses, and here is why. It seems plausible that IBP admits an agreement theorem (analogous to Aumann’s) which informally amounts to the following: Given two agents Alice and Bobcat that (i) share the same physical universe, (ii) have a sufficiently tight causal relationship (each can see what the other sees), (iii) have unprivileged locations inside the physical universe, (iv) start from similar/compatible priors and (v) [maybe needed?] similar utility functions, they converge to similar/compatible beliefs, regardless of the complexity of translation between their subjective viewpoints. This is plausible because (i) as opposed to the cartesian framework, different bridge rules don’t lead to different probabilities and (ii) if Bobcat considers a simulation hypothesis plausible, and the simulation is sufficiently detailed to fool it indefinitely, then the simulation contains a detailed simulation of Alice and hence Alice must also consider this to be plausible hypothesis.
If the agreement conjecture is true, then the AI will converge to hypotheses that all contain the user, in a causal relationship with the AI that affirms them as the user. Moreover, those hypotheses will be compatible with the user’s own posterior (i.e. the differences can be attributed the AIs superior reasoning). Therefore, the AI will act on the user’s behalf, leaving no room for mesa-optimizers. Any would-be mesa-optimizer has to take the shape of a hypothesis that the user should also believe, within which the pointer-to-values still points to the right place.
Two nuances:
Maybe in practice there’s still room for simulation hypotheses of the AI which contain coarse-grained simulations of the user. In this case, the user detection algorithm might need to allow for coarsely simulated agents.
If the agreement theorem needs condition v, we get a self-referential loop: if the AI and the user converge to the same utility function, the theorem guarantees them to converge to the same utility function, but otherwise it doesn’t. This might make the entire thing a useless tautology, or there might be a way to favorably resolve the self-reference, vaguely analogously to how Loeb’s theorem allows resolving the self-reference in prisoner dilemma games between FairBots.
There are actually two ways to do this, corresponding to the two natural mappings Γ×2Γ→Γ×2Δ. The first is just projecting the subset of Γ to a subset of Δ, the second is analogous to what’s used in Proposition 2.16 of the IBP article. I’m not entirely sure what’s correct here.
Hi Vanessa! Thanks again for your previous answers. I’ve got one further concern.
Are all mesa-optimizers really only acausal attackers?
I think mesa-optimizers don’t need to be purely contained in a hypothesis (rendering them acausal attackers), but can be made up of a part of the hypotheses-updating procedures (maybe this is obvious and you already considered it).
Of course, since the only way to change the AGI’s actions is by changing its hypotheses, even these mesa-optimizers will have to alter hypothesis selection. But their whole running program doesn’t need to be captured inside any hypothesis (which would be easier for classifying acausal attackers away).
That is, if we don’t think about how the AGI updates its hypotheses, and just consider them magically updating (without any intermediate computations), then of course, the only mesa-optimizers will be inside hypotheses. If we actually think about these computations and consider a brute-force search over all hypotheses, then again they will only be found inside hypotheses, since the search algorithm itself is too simple and provides no further room for storing a subagent (even if the mesa-optimizer somehow takes advantage of the details of the search). But if more realistically our AGI employs more complex heuristics to ever-better approximate optimal hypotheses update, mesa-optimizers can be partially or completely encoded in those (put another way, those non-optimal methods can fail / be exploited). This failure could be seen as a capabilities failure (in the trivial sense that it failed to correctly approximate perfect search), but I think it’s better understood as an alignment failure.
The way I see PreDCA (and this might be where I’m wrong) is as an “outer top-level protocol” which we can fit around any superintelligence of arbitrary architecture. That is, the superintelligence will only have to carry out the hypotheses update (plus some trivial calculations over hypotheses to find the best action), and given it does that correctly, since the outer objective we’ve provided is clearly aligned, we’re safe. That is, PreDCA is an outer objective that solves outer alignment. But we still need to ensure the hypotheses update is carried out correctly (and that’s everything our AGI is really doing).
I don’t think this realization rules out your Agreement solution, since if truly no hypothesis can steer the resulting actions in undesirable ways (maybe because every hypothesis with a user has the human as the user), then obviously not even optimizers in hypothesis update can find malign hypotheses (although they can still causally attack hacking the computer they’re running on etc.). But I think your Agreement solution doesn’t completely rule out any undesirable hypothesis, but only makes it harder for an acausal attacker to have the user not be the human. And in this situation, an optimizer in hypothesis update could still select for malign hypotheses in which the human is subtly incorrectly modelled in such a precise way that has relevant consequences for the actions chosen. This can again be seen as a capabilities failure (not modelling the human well enough), but it will always be present to some degree, and it could be exploited by mesa-optimizers.
There’s a class of AI risk mitigation strategies which relies on the users to perform the pivotal act using tools created by AI (e.g. nanosystems). These strategies are especially appealing if we want to avoid human models. Here is a concrete alignment protocol for these strategies, closely related to AQD, which we call autocalibrating quantilized RL (AQRL).
First, suppose that we are able formulate the task as episodic RL with a formally specified reward function. The reward function is necessarily only a proxy for our true goal, since it doesn’t contain terms such as “oh btw don’t kill people while you’re building the nanosystem”. However, suppose the task is s.t. accomplishing it in the intended way (without Goodharting or causing catastrophic side effects) is easier than performing any attack. We will call this the “relative difficulty assumption” (RDA). Then, there exists a value for the quantilization parameter s.t. quantilized RL performs the task in the intended way.
We might not know how to set the quantilization parameter on our own, but we can define a performance goal for the task (in terms of expected total reward) s.t. the RDA holds. This leads to algorithms which gradually tune the quantilization parameter until the performance goal is met, while maintaining a proper balance between safety and sample complexity. Here it is important to keep track of epistemic vs. aleatoric uncertainty: the performance goal is the expectation of total reward relatively to aleatoric uncertainty (i.e. the stochasticity of a given hypothesis), whereas the safety goal is a bound on the expected cost of overshooting the optimal quantilization parameter relatively to both aleatoric and epistemic uncertainty (i.e. uncertainty between different hypotheses). This secures the system against malign hypotheses that are trying to cause an overshoot.
Notice the hardening the system (i.e. making it more secure using conventional methods) increases the likelihood of the RDA. That is, although it is infeasible to manually harden the system against superintelligent attacks, it can very well be feasible to harden it against attacks available at the minimal level of optimization power (= quantilization parameter) required to meet the performance goal, at least to such an extent that the intended solution becomes more appealing for the AI.
AQRL involves the risk that the RDA is false, even though it seems true to the designers. To mitigate this risk, we can deploy honeypots. That is, we add auxiliary goals to our reward function s.t. (i) if the goal is reached, maximal total reward is awarded (ii) the goal is harmless in itself (iii) reaching the goal is still harder than the intended solution, but by a smaller margin than performing attacks (iv) we can detect the event of reaching the goal. For example, suppose that the AI is not supposed to have Internet access, so gaining Internet access is a path to a variety of possible attacks. Then, we can add an auxiliary goal of the form “send such-and-such innocuous packet to such-and-such IP address”. If the RDA is false and the AI succeeds gaining Internet access earlier (on the quantilization parameter scale) than reaching the performance goal, once the AI sent the packet it will randomize all its remaining actions (since it is operating at the earliest quantilization parameter which allows this). The users will then be alerted that security was breached and rethink their next steps.
Now, suppose that we can only specify the reward function via concepts that have to be learned from examples. This opens the risk of generalization failure. Such risk is not necessarily a problem since we are still protected by quantilization. However, it needs to be factored into our safety bounds. An especially concerning possibility is that the AI will interpret the concepts as “would be labeled by humans as such-and-such” instead of the intended interpretation, creating incentives to deceive. Moreover, this encourages learning human models which puts more pressure on the RDA by making it easier to attack.
In order to avoid learning human models, we can use methods along these lines. Specifically, suppose that, in addition to the dataset for learning out task-relevant concepts, we supply a dataset designed specifically for learning human models. We can then design an algorithm that produces models of the task-relevant concepts s.t. the human-centric dataset cannot be compressed w.r.t. them. That is, the algorithm makes sure that access to the models it produces doesn’t help with learning the human-centric dataset, implying that the concept models don’t contain human models. Moreover, we can extend this approach to the entire process, s.t. human models are not learned from any other source of information as well.
Notice that although this method excludes human models from the actual RL process, it encourages them in intermediate computations, since we are actively supplying a human-centric dataset to the system. But, this is not dangerous if the intermediate computation is hardened against non-Cartesian daemons (a big “if” but one we need to deal with anyway).
Much of the orthodox LessWrongian approach to rationality (as it is expounded in Yudkowsky’s Sequences and onwards) is grounded in Bayesian probability theory. However, I now realize that pure Bayesianism is wrong, instead the right thing is quasi-Bayesianism. This leads me to ask, what are the implications of quasi-Bayesianism on human rationality? What are the right replacements for (the Bayesian approach to) bets, calibration, proper scoring rules et cetera? Does quasi-Bayesianism clarify important confusing issues in regular Bayesianism such as the proper use of inside and outside view? Is there rigorous justification to the intuition that we should have more Knightian uncertainty about questions with less empirical evidence? Does any of it influence various effective altruism calculations in surprising ways? What common LessWrongian wisdom does it undermine, if any?
In the anthropic trilemma, Yudkowsky writes about the thorny problem of understanding subjective probability in a setting where copying and modifying minds is possible. Here, I will argue that infra-Bayesianism (IB) leads to the solution.
Consider a population of robots, each of which in a regular RL agent. The environment produces the observations of the robots, but can also make copies or delete portions of their memories. If we consider a random robot sampled from the population, the history they observed will be biased compared to the “physical” baseline. Indeed, suppose that a particular observation c has the property that every time a robot makes it, 10 copies of them are created in the next moment. Then, a random robot will have c much more often in their history than the physical frequency with which c is encountered, due to the resulting “selection bias”. We call this setting “anthropic RL” (ARL).
The original motivation for IB was non-realizability. But, in ARL, Bayesianism runs into issues even when the environment is realizable from the “physical” perspective. For example, we can consider an “anthropic MDP” (AMDP). An AMDP has finite sets of actions (A) and states (S), and a transition kernel T:A×S→Δ(S∗). The output is a string of states instead of a single state, because many copies of the agent might be instantiated on the next round, each with their own state. In general, there will be no single Bayesian hypothesis that captures the distribution over histories that the average robot sees at any given moment of time (at any given moment of time we sample a robot out of the population and look at their history). This is because the distributions at different moments of time are mutually inconsistent.
[EDIT: Actually, given that we don’t care about the order of robots, the signature of the transition kernel should be T:A×S→ΔNS]
The consistency that is violated is exactly the causality property of environments. Luckily, we know how to deal with acausality: using the IB causal-acausal correspondence! The result can be described as follows: Murphy chooses a time moment n∈N and guesses the robot policy π until time n. Then, a simulation of the dynamics of (π,T) is performed until time n, and a single history is sampled from the resulting population. Finally, the observations of the chosen history unfold in reality. If the agent chooses an action different from what is prescribed, Nirvana results. Nirvana also happens after time n (we assume Nirvana reward 1 rather than ∞).
This IB hypothesis is consistent with what the average robot sees at any given moment of time. Therefore, the average robot will learn this hypothesis (assuming learnability). This means that for n≫11−γ≫0, the population of robots at time n has expected average utility with a lower bound close to the optimum for this hypothesis. I think that for an AMDP this should equal the optimum expected average utility you can possibly get, but it would be interesting to verify.
Curiously, the same conclusions should hold if we do a weighted average over the population, with any fixed method of weighting. Therefore, the posterior of the average robot behaves adaptively depending on which sense of “average” you use. So, your epistemology doesn’t have to fix a particular method of counting minds. Instead different counting methods are just different “frames of reference” through which to look, and you can be simultaneously rational in all of them.
Could you expand a little on why you say that no Bayesian hypothesis captures the distribution over robot-histories at different times? It seems like you can unroll an AMDP into a “memory MDP” that puts memory information of the robot into the state, thus allowing Bayesian calculation of the distribution over states in the memory MDP to capture history information in the AMDP.
I’m not sure what do you mean by that “unrolling”. Can you write a mathematical definition?
Let’s consider a simple example. There are two states: s0 and s1. There is just one action so we can ignore it.s0 is the initial state. An s0 robot transition into an s1 robot. An s1 robot transitions into an s0 robot and an s1 robot. How will our population look like?
0th step: all robots remember s0
1st step: all robots remember s0s1
2nd step: 1⁄2 of robots remember s0s1s0 and 1⁄2 of robots remember s0s1s1
3rd step: 1⁄3 of robots remembers s0s1s0s1, 1⁄3 of robots remember s0s1s1s0 and 1⁄3 of robots remember s0s1s1s1
There is no Bayesian hypothesis a robot can have that gives correct predictions both for step 2 and step 3. Indeed, to be consistent with step 2 we must have Pr[s0s1s0]=12 and Pr[s0s1s1]=12. But, to be consistent with step 3 we must have Pr[s0s1s0]=13, Pr[s0s1s1]=23.
In other words, there is no Bayesian hypothesis s.t. we can guarantee that a randomly sampled robot on a sufficiently late time step will have learned this hypothesis with high probability. The apparent transition probabilities keep shifting s.t. it might always continue to seem that the world is complicated enough to prevent our robot from having learned it already.
Or, at least it’s not obvious there is such a hypothesis. In this example, Pr[s0s1s1]Pr[s0s1s0] will converge to the golden ratio at late steps. But, do all probabilities converge fast enough for learning to happen, in general? I don’t know, maybe for finite state spaces it can work. Would definitely be interesting to check.
[EDIT: actually, in this example there is such a hypothesis but in general there isn’t, see below]
Text whose primary goal is conveying information (as opposed to emotion, experience or aesthetics) should be skimming friendly. Time is expensive, words are cheap. Skimming is a vital mode of engaging with text, either to evaluate whether it deserves a deeper read or to extract just the information you need. As a reader, you should nurture your skimming skills. As a writer, you should treat skimmers as a legitimate and important part of your target audience. Among other things it means:
Good title and TLDR/abstract
Clear and useful division into sections
Putting the high-level picture and conclusions first, the technicalities and detailed arguments later. Never leave the reader clueless about where you’re going with something for a long time.
Visually emphasize the central points and make them as self-contained as possible. For example, in the statement of mathematical theorems avoid terminology whose definition is hidden somewhere in the bulk of the text.
Stronger: as a writer you should assume your modal reader is a skimmer, both because they are, and because even non skimmers are only going to remember about the same number of things that the good skimmer does.
I propose to call metacosmology the hypothetical field of study which would be concerned with the following questions:
Studying the space of simple mathematical laws which produce counterfactual universes with intelligent life.
Studying the distribution over utility-function-space (and, more generally, mindspace) of those counterfactual minds.
Studying the distribution of the amount of resources available to the counterfactual civilizations, and broad features of their development trajectories.
Using all of the above to produce a distribution over concretized simulation hypotheses.
This concept is of potential interest for several reasons:
It can be beneficial to actually research metacosmology, in order to draw practical conclusions. However, knowledge of metacosmology can pose an infohazard, and we would need to precommit not to accept blackmail from potential simulators.
The metacosmology knowledge of a superintelligent AI determines the extent to which it poses risk via the influence of potential simulators.
In principle, we might be able to use knowledge of metacosmology in order to engineer an “atheist prior” for the AI that would exclude simulation hypotheses. However, this might be very difficult in practice.
Why do bad things happen to good people?
An AI progress scenario which seems possible and which I haven’t seen discussed: an imitation plateau.
The key observation is, imitation learning algorithms[1] might produce close-to-human-level intelligence even if they are missing important ingredients of general intelligence that humans have. That’s because imitation might be a qualitatively easier task than general RL. For example, given enough computing power, a human mind becomes realizable from the perspective of the learning algorithm, while the world-at-large is still far from realizable. So, an algorithm that only performs well in the realizable setting can learn to imitate a human mind, and thereby indirectly produce reasoning that works in non-realizable settings as well. Of course, literally emulating a human brain is still computationally formidable, but there might be middle scenarios where the learning algorithm is able to produce a good-enough-in-practice imitation of systems that are not too complex.
This opens the possibility that close-to-human-level AI will arrive while we’re still missing key algorithmic insights to produce general intelligence directly. Such AI would not be easily scalable to superhuman. Nevertheless, some superhuman performance might be produced by sped-up simulation, reducing noise in human behavior and controlling the initial conditions (e.g. simulating a human on a good day). As a result, we will have some period of time during which AGI is already here, automation is in full swing, but there’s little or no further escalation. At the end of this period, the missing ingredients will be assembled (maybe with the help of AI researchers) and superhuman AI (possibly a fast takeoff) begins.
It’s interesting to try and work out the consequences of such a scenario, and the implications on AI strategy.
Such as GPT-n
This seems similar to gaining uploads prior to AGI, and opens up all those superorg upload-city amplification/distillation constructions which should get past human level shortly after. In other words, the limitations of the dataset can be solved by amplification as soon as the AIs are good enough to be used as building blocks for meaningful amplification, and something human-level-ish seems good enough for that. Maybe even GPT-n is good enough for that.
That is similar to gaining uploads (borrowing terminology from Egan, we can call them “sideloads”), but it’s not obvious amplification/distillation will work. In the model based on realizability, the distillation step can fail because the system you’re distilling is too computationally complex (hence, too unrealizable). You can deal with it by upscaling the compute of the learning algorithm, but that’s not better than plain speedup.
To me this seems to be essentially another limitation of the human Internet archive dataset: reasoning is presented in an opaque way (most slow/deliberative thoughts are not in the dataset), so it’s necessary to do a lot of guesswork to figure out how it works. A better dataset both explains and summarizes the reasoning (not to mention gets rid of the incoherent nonsense, but even GPT-3 can do that to an extent by roleplaying Feynman).
Any algorithm can be represented by a habit of thought (Turing machine style if you must), and if those are in the dataset, they can be learned. The habits of thought that are simple enough to summarize get summarized and end up requiring fewer steps. My guess is that the human faculties needed for AGI can be both represented by sequences of thoughts (probably just text, stream of consciousness style) and easily learned with current ML. So right now the main obstruction is that it’s not feasible to build a dataset with those faculties represented explicitly that’s good enough and large enough for current sample-inefficient ML to grok. More compute in the learning algorithm is only relevant for this to the extent that we get a better dataset generator that can work on the tasks before it more reliably.
I don’t see any strong argument why this path will produce superintelligence. You can have a stream of thought that cannot be accelerated without investing a proportional amount of compute, while a completely different algorithm would produce a far superior “stream of thought”. In particular, such an approach cannot differentiate between features of the stream of thought that are important (meaning that they advance towards the goal) and features of the stream of though that are unimportant (e.g. different ways to phrase the same idea). This forces you to solve a task that is potentially much more difficult than just achieving the goal.
I was arguing that near human level babblers (including the imitation plateau you were talking about) should quickly lead to human level AGIs by amplification via stream of consciousness datasets, which doesn’t pose new ML difficulties other than design of the dataset. Superintelligence follows from that by any of the same arguments as for uploads leading to AGI (much faster technological progress; if amplification/distillation of uploads is useful straight away, we get there faster, but it’s not necessary). And amplified babblers should be stronger than vanilla uploads (at least implausibly well-educated, well-coordinated, high IQ humans).
For your scenario to be stable, it needs to be impossible (in the near term) to run the AGIs (amplified babblers) faster than humans, and for the AGIs to remain less effective than very high IQ humans. Otherwise you get acceleration of technological progress, including ML. So my point is that feasibility of imitation plateau depends on absence of compute overhang, not on ML failing to capture some of the ingredients of human general intelligence.
The imitation plateau can definitely be rather short. I also agree that computational overhang is the major factor here. However, a failure to capture some of the ingredients can be a cause of low computational overhead, whereas a success to capture all of the ingredients is a cause of high computational overhang, because the compute necessary to reach superintelligence might be very different in those two cases. Using sideloads to accelerate progress might still require years, whereas an “intrinsic” AGI might lead to the classical “foom” scenario.
EDIT: Although, since training is typically much more computationally expensive than deployment, it is likely that the first human-level imitators will already be significantly sped-up compared to humans, implying that accelerating progress will be relatively easy. It might still take some time from the first prototype until such an accelerate-the-progress project, but probably not much longer than deploying lots of automation.
I agree. But GPT-3 seems to me like a good estimate for how much compute it takes to run stream of consciousness imitation learning sideloads (assuming that learning is done in batches on datasets carefully prepared by non-learning sideloads, so the cost of learning is less important). And with that estimate we already have enough compute overhang to accelerate technological progress as soon as the first amplified babbler AGIs are developed, which, as I argued above, should happen shortly after babblers actually useful for automation of human jobs are developed (because generation of stream of consciousness datasets is a special case of such a job).
So the key things to make imitation plateau last for years are either sideloads requiring more compute than it looks like (to me) they require, or amplification of competent babblers into similarly competent AGIs being a hard problem that takes a long time to solve.
Another thing that might happen is a data bottleneck.
Maybe there will be a good enough dataset to produce a sideload that simulates an “average” person, and that will be enough to automate many jobs, but for a simulation of a competent AI researcher you would need a more specialized dataset that will take more time to produce (since there are a lot less competent AI researchers than people in general).
Moreover, it might be that the sample complexity grows with the duration of coherent thought that you require. That’s because, unless you’re training directly on brain inputs/outputs, non-realizable (computationally complex) environment influences contaminate the data, and in order to converge you need to have enough data to average them out, which scales with the length of your “episodes”. Indeed, all convergence results for Bayesian algorithms we have in the non-realizable setting require ergodicity, and therefore the time of convergence (= sample complexity) scales with mixing time, which in our case is determined by episode length.
In such a case, we might discover that many tasks can be automated by sideloads with short coherence time, but AI research might require substantially longer coherence times. And, simulating progress requires by design going off-distribution along certain dimensions which might make things worse.
Another way to describe the same (or similar) plateau: we could think about GPT-n as GLUT with approximation between prerecorded answers: it can produce intelligent products similar to the ones which were created by humans in the past and are presented in its training dataset – but not above the human intelligence level, as there is no superintelligent examples in the dataset.
I propose a new formal desideratum for alignment: the Hippocratic principle. Informally the principle says: an AI shouldn’t make things worse compared to letting the user handle them on their own, in expectation w.r.t. the user’s beliefs. This is similar to the dangerousness bound I talked about before, and is also related to corrigibility. This principle can be motivated as follows. Suppose your options are (i) run a Hippocratic AI you already have and (ii) continue thinking about other AI designs. Then, by the principle itself, (i) is at least as good as (ii) (from your subjective perspective).
More formally, we consider a (some extension of) delegative IRL setting (i.e. there is a single set of input/output channels the control of which can be toggled between the user and the AI by the AI). Let πυu be the the user’s policy in universe υ and πa the AI policy. Let T be some event that designates when we measure the outcome / terminate the experiment, which is supposed to happen with probability 1 for any policy. Let Vυ be the value of a state from the user’s subjective POV, in universe υ. Let μυ be the environment in universe υ. Finally, let ζ be the AI’s prior over universes and ϵ some sufficiently small bound. We require
∀T:Eυ∼ζ[(Eπυuμυ[Vυ(T)]−Eπaμυ[Vυ(T)])+]≤ϵ
Here, V(T) designates the value after event T happens, and (x)+ is defined to be 0 for x<0 and x otherwise.
I now outline an example of a Hippocratic protocol, which I call Hippocratic timeline-driven learning (HTDL).
Suppose, for simplicity, that the goal is accomplishing some binary task (i.e. we can either succeed or fail). We can imagine the task to be something like “construct a defense system against malign AI”. At any given moment, the user reports their subjective timeline: i.e. for, any t, the probabilities of success and failure after time t, assuming the user will continue working on the task by themselves. We assume the AI can learn a good model of the user’s policy and the user’s nominal report, but this nominal report might deviate from the true timeline because the system can be corrupted (i.e. the user manipulated to enter false information or the input channel hacked). Moreover, we assume that such corruption counts as failing the task.
The AI operates by maximizing the AI-expectation of the user’s (unknown) utility function Eυ∼ζ[Eπμυ[Uυ]], subject to the Hippocratic principle as a constraint. Since we’re working in a delegative setting, the AI can trivially achieve the constraint by always delegating. However, it can do much better than that, by short-term quantilizing w.r.t. the user’s reported success probability (with the user’s policy serving as baseline). When quantilizing the short-term policy, we can upper bound the probability of corruption via the user’s reported probability of short-term failure (which we assume to be low, i.e. we assume the malign AI is not imminent). This allows the AI to find parameters under which quantilization is guaranteed to improve things in expectation. Then, after completing this short-term maneuver, the AI can do another short-term quantilization, and continue in this vein. Essentially, the AI is gradually bending the user’s trajectory towards success.
Of course, we can improve the guarantees of such a protocol either by adding more information or by using stronger assumptions (e.g. assume the user is quasi-rational). But timeline-driven short-term quantilization serves as a useful feasibility proof.
(Update: I don’t think this was 100% right, see here for a better version.)
Attempted summary for morons like me: AI is trying to help the human H. They share access to a single output channel, e.g. a computer keyboard, so that the actions that H can take are exactly the same as the actions AI can take. Every step, AI can either take an action, or delegate to H to take an action. Also, every step, H reports her current assessment of the timeline / probability distribution for whether she’ll succeed at the task, and if so, how soon.
At first, AI will probably delegate to H a lot, and by watching H work, AI will gradually learn both the human policy (i.e. what H tends to do in different situations), and how different actions tend to turn out in hindsight from H’s own perspective (e.g., maybe whenever H takes action 17, she tends to declare shortly afterwards that probability of success now seems much higher than before—so really H should probably be taking action 17 more often!).
Presumably the AI, being a super duper fancy AI algorithm, learns to anticipate how different actions will turn out from H’s perspective much better than H herself. In other words, maybe it delegates to H, and H takes action 41, and the AI is watching this and shaking its head and thinking to itself “gee you dunce you’re gonna regret that”, and shortly thereafter the AI is proven correct.
OK, so now what? The naive answer would be: the AI should gradually stop delegating and start just doing the thing that leads to H feeling maximally optimistic later on.
But we don’t want to do that naive thing. There are two problems:
The first problem is “traps” (a.k.a. catastrophes). Let’s say action 0 is Press The History Eraser Button. H never takes that action. The AI shouldn’t either. What happens is: AI has no idea (wide confidence interval) about what the consequence of action 0 would be, so it doesn’t take it. This is the delegative RL thing—in the explore/exploit dilemma, the AI kinda sits back and lets H do the exploring, at least when a trap might be involved.
But here we want to go further. Let’s say that there’s some action which H might do, where the AI can predict the consequence pretty well, and the consequence is “H will eventually feel more optimistic about success in the task”. Then the AI can go do that action, right? Not necessarily! The action might still be bad, because it involved corrupting the feedback channel.
So let’s say “action 789 = H will eat a pot brownie”. Taking that action makes H both much more optimistic about success and much less likely to actually succeed in the task. (It makes her assessments inaccurate.)
If the AI has no idea what pot brownies do, it won’t take action 789 because of the previous thing—maybe that action is a trap. But what if the AI has (indirectly) figured out, with 100% confidence, exactly what the consequence of action 789 would be?
In that case, since the AI’s decision criterion is all about H’s future reported odds of success, a naive algorithm would have the AI take action 789. To avoid that, we have this quantilization thing. How does that work?
Well, let’s say that H right now assigns 99% chance of eventual success. Therefore—assuming that H is well-calibrated and knows the consequences of action 789—it follows that H’s policy is to take that action with ≤1% probability. Basically, H is optimistic about success in the task, partly because H knows that she’s almost definitely not going to eat that pot brownie. I mean, she might eat it, if she gets really bored and frustrated and hungry. But probably not! So she’s feeling very optimistic.
OK, now the AI quantilizes by drawing 5 random samples from (its understanding of) H’s policy, and taking the action that (it believes) will lead to the highest future-optimism-about-success. Assume its beliefs are accurate. Compared to just doing what H would have done, this has downsides and upsides. The downside is, since action 789 scores really well by the AI’s decision criterion, the probability of action 789 is no longer ≤1%, but rather ≤5%. The upside is, if we’re not taking action 789, i.e. the other ≥95% of the probability mass, we’re probably doing something quite a bit better than what H would have done.
These could plausibly balance out such that the expected probability of success strictly increases, compared to what H would have done.
Please correct me if I’m misunderstanding anything.
This is about right.
Notice that typically we use the AI for tasks which are hard for H. This means that without the AI’s help, H’s probability of success will usually be low. Quantilization-wise, this is a problem: the AI will be able to eliminate those paths for which H will report failure, but maybe most of the probability mass among apparent-success paths is still on failure (i.e. the success report is corrupt). This is why the timeline part is important.
On a typical task, H expects to fail eventually but they don’t expect to fail soon. Therefore, the AI can safely consider a policies of the form “in the short-term, do something H would do with marginal probability, in the long-term go back to H’s policy”. If by the end of the short-term maneuver H reports an improved prognosis, this can imply that the improvement is genuine (since the AI knows H is probably uncorrupted at this point). Moreover, it’s possible that in the new prognosis H still doesn’t expect to fail soon. This allows performing another maneuver of the same type. This way, the AI can iteratively steer the trajectory towards true success.
The Hippocratic principle seems similar to my concept of non-obstruction (https://www.lesswrong.com/posts/Xts5wm3akbemk4pDa/non-obstruction-a-simple-concept-motivating-corrigibility), but subjective from the human’s beliefs instead of the AI’s.
Yes, there is some similarity! You could say that a Hippocratic AI needs to be continuously non-obstructive w.r.t. the set of utility functions and priors the user could plausibly have, given what the AI knows. Where, by “continuously” I mean that we are allowed to compare keeping the AI on or turning off at any given moment.
“Corrigibility” is usually defined as the property of AIs who don’t resist modifications by their designers. Why would we want to perform such modifications? Mainly it’s because we made errors in the initial implementation, and in particular the initial implementation is not aligned. But, this leads to a paradox: if we assume our initial implementation to be flawed in a way that destroys alignment, why wouldn’t it also be flawed in a way that destroys corrigibility?
In order to stop passing the recursive buck, we must assume some dimensions along which our initial implementation is not allowed to be flawed. Therefore, corrigibility is only a well-posed notion in the context of a particular such assumption. Seen through this lens, the Hippocratic principle becomes a particular crystallization of corrigibility. Specifically, the Hippocratic principle assumes the agent has access to some reliable information about the user’s policy and preferences (be it through timelines, revealed preferences or anything else).
Importantly, this information can be incomplete, which can motivate altering the agent along the way. And, the agent will not resist this alteration! Indeed, resisting the alteration is ruled out unless the AI can conclude with high confidence (and not just in expectation) that such resistance is harmless. Since we assumed the information is reliable, and the alteration is beneficial, the AI cannot reach such a conclusion.
For example, consider an HDTL agent getting upgraded to “Hippocratic CIRL” (assuming some sophisticated model of relationship between human behavior and human preferences). In order to resist the modification, the agent would need a resistance strategy that (i) doesn’t deviate too much from the human baseline and (ii) ends with the user submitting a favorable report. Such a strategy is quite unlikely to exist.
I think the people most interested in corrigibility are imagining a situation where we know what we’re doing with corrigibility (e.g. we have some grab-bag of simple properties we want satisfied), but don’t even know what we want from alignment, and then they imagine building an unaligned slightly-sub-human AGI and poking at it while we “figure out alignment.”
Maybe this is a strawman, because the thing I’m describing doesn’t make strategic sense, but I think it does have some model of why we might end up with something unaligned but corrigible (for at least a short period).
The concept of corrigibility was introduced by MIRI, and I don’t think that’s their motivation? On my model of MIRI’s model, we won’t have time to poke at a slightly subhuman AI, we need to have at least a fairly good notion of what to do with a superhuman AI upfront. Maybe what you meant is “we won’t know how to construct perfect-utopia-AI, so we will just construct a prevent-unaligned-AIs-AI and run it so that we can figure out perfect-utopia-AI in our leisure”. Which, sure, but I don’t see what it has to do with corrigibility.
Corrigibility is neither necessary nor sufficient for safety. It’s not strictly necessary because in theory an AI can resist modifications in some scenarios while always doing the right thing (although in practice resisting modifications is an enormous red flag), and it’s not sufficient since an AI can be “corrigible” but cause catastrophic harm before someone notices and fixes it.
What we’re supposed to gain from corrigibility is having some margin of error around alignment, in which case we can decompose alignment as corrigibility + approximate alignment. But it is underspecified if we don’t say along which dimensions or how big the margin is. If it’s infinite margin along all dimensions then corrigibility and alignment are just isomorphic and there’s no reason to talk about the former.
Very interesting—I’m sad I saw this 6 months late.
After thinking a bit, I’m still not sure if I want this desideratum. It seems to require a sort of monotonicity, where we can get superhuman performance just by going through states that humans recognize as good, and not by going through states that humans would think are weird or scary or unevaluable.
One case where this might come up is in competitive games. Chess AI beats humans in part because it makes moves that many humans evaluate as bad, but are actually good. But maybe this example actually supports your proposal—it seems entirely plausible to make a chess engine that only makes moves that some given population of humans recognize as good, but is better than any human from that population.
On the other hand, the humans might be wrong about the reason the move is good, so that the game is made of a bunch of moves that seem good to humans, but where the humans are actually wrong about why they’re good (from the human perspective, this looks like regularly having “happy surprises”). We might hope that such human misevaluations are rare enough that quantilization would lead to moves on average being well-evaluated by humans, but for chess I think that might be false! Computers are so much better than humans at chess that a very large chunk of the best moves according to both humans and the computer will be ones that humans misevaluate.
Maybe that’s more a criticism of quantilizers, not a criticism of this desideratum. So maybe the chess example supports this being a good thing to want? But let me keep critiquing quantilizers then :P
If what a powerful AI thinks is best (by an exponential amount) is to turn off the stars until the universe is colder, but humans think it’s scary and ban the AI from doing scary things, the AI will still try to turn off the stars in one of the edge-case ways that humans wouldn’t find scary. And if we think being manipulated like that is bad and quantilize over actions to make the optimization milder, turning off the stars is still so important that a big chunk of the best moves according to both humans and the computer are going to be ones that humans misevaluate, and the computer knows will lead to a “happy surprise” of turning off the stars not being scary. Quantilization avoids policies that precisely exploit tiny features of the world, and it avoids off-distribution behavior, but it still lets the AI get what it wants if it totally outsmarts the humans.
The other thing this makes me think of is Lagrange multipliers. I bet there’s a duality between applying this constraint to the optimization process, and adding a bias (I mean, a useful prior) to the AI’s process for modeling U.
When I’m deciding whether to run an AI, I should be maximizing the expectation of my utility function w.r.t. my belief state. This is just what it means to act rationally. You can then ask, how is this compatible with trusting another agent smarter than myself?
One potentially useful model is: I’m good at evaluating and bad at searching (after all, P≠NP). I can therefore delegate searching to another agent. But, as you point out, this doesn’t account for situations in which I seem to be bad at evaluating. Moreover, if the AI prior takes an intentional stance towards the user (in order to help learning their preferences), then the user must be regarded as good at searching.
A better model is: I’m good at both evaluating and searching, but the AI can access actions and observations that I cannot. For example, having additional information can allow it to evaluate better. An important special case is: the AI is connected to an external computer (Turing RL) which we can think of as an “oracle”. This allows the AI to have additional information which is purely “logical”. We need infra-Bayesianism to formalize this: the user has Knightian uncertainty over the oracle’s outputs entangled with other beliefs about the universe.
For instance, in the chess example, if I know that a move was produced by exhaustive game-tree search then I know it’s a good move, even without having the skill to understand why the move is good in any more detail.
Now let’s examine short-term quantilization for chess. On each cycle, the AI finds a short-term strategy leading to a position that the user evaluates as good, but that the user would require luck to manage on their own. This is repeated again and again throughout the game, leading to overall play substantially superior to the user’s. On the other hand, this play is not as good as the AI would achieve if it just optimized for winning at chess without any constrains. So, our AI might not be competitive with an unconstrained unaligned AI. But, this might be good enough.
I’m not sure what you’re saying in the “turning off the stars example”. If the probability for the user to autonomously decide to turn off the stars is much lower than the quantilization fraction, then the probability that quantilization will decide to turn off the stars is low. And, the quantilization fraction is automatically selected like this.
Agree with the first section, though I would like to register my sentiment that although “good at selecting but missing logical facts” is a better model, it’s still not one I’d want an AI to use when inferring my values.
I think my point is if “turn off the stars” is not a primitive action, but is a set of states of the world that the AI would overwhelming like to go to, then the actual primitive actions will get evaluated based on how well they end up going to that goal state. And since the AI is better at evaluating than us, we’re probably going there.
Another way of looking at this claim is that I’m telling a story about why the safety bound on quantilizers gets worse when quantilization is iterated. Iterated quantilization has much worse bounds than quantilizing over the iterated game, which makes sense if we think of games where the AI evaluates many actions better than the human.
I think you misunderstood how the iterated quantilization works. It does not work by the AI setting a long-term goal and then charting a path towards that goal s.t. it doesn’t deviate too much from the baseline over every short interval. Instead, every short-term quantilization is optimizing for the user’s evaluation in the end of this short-term interval.
Ah. I indeed misunderstood, thanks :) I’d read “short-term quantilization” as quantilizing over short-term policies evaluated according to their expected utility. My story doesn’t make sense if the AI is only trying to push up the reported value estimates (though that puts a lot of weight on these estimates).
I don’t understand what you mean here by quantilizing. The meaning I know is to take a random action over the top \alpha actions, on a given base distribution. But I don’t see a distribution here, or even a clear ordering over actions (given that we don’t have access to the utility function).
I’m probably missing something obvious, but more details would really help.
The distribution is the user’s policy, and the utility function for this purpose is the eventual success probability estimated by the user (as part of the timeline report), in the end of the “maneuver”. More precisely, the original quantilization formalism was for the one-shot setting, but you can easily generalize it, for example I did it for MDPs.
Oh, right, that makes a lot of sense.
So is the general idea that we quantilize such that we’re choosing in expectation an action that doesn’t have corrupted utility (by intuitively having something like more than twice as many actions in the quantilization than we expect to be corrupted), so that we guarantee the probability of following the manipulation of the learned user report is small?
I also wonder if using the user policy to sample actions isn’t limiting, because then we can only take actions that the user would take. Or do you assume by default that the support of the user policy is the full action space, so every action is possible for the AI?
Yes, although you probably want much more than twice. Basically, if the probability of corruption following the user policy is ϵ and your quantilization fraction is ϕ then the AI’s probability of corruption is bounded by ϵϕ.
Obviously it is limiting, but this is the price of safety. Notice, however, that the quantilization strategy is only an existence proof. In principle, there might be better strategies, depending on the prior (for example, the AI might be able to exploit an assumption that the user is quasi-rational). I didn’t specify the AI by quantilization, I specified it by maximizing EU subject to the Hippocratic constraint. Also, the support is not really the important part: even if the support is the full action space, some sequences of actions are possible but so unlikely that the quantilization will never follow them.
I like this because it’s simple and obviously correct. Also I can see at least one way you could implement it:
a. Suppose the AI is ‘shadowing’ a human worker doing a critical task. Say it is ‘shadowing’ a human physician.
b. Each time the AI observes the same patient, it regresses between [data from the patient] and [predicted decision a ‘good’ physician would make, predicted outcome for the ‘good’ decision]. Once the physician makes a decision and communicates it, the AI regresses between [decision the physician made] and [predicted outcome for that decision].
c. The machine also must have a confidence or this won’t work.
With large numbers and outright errors made by the physician, it’s then possible to detect all the cases where the [decision the physician made] has a substantially worse outcome than the [predicted decision a ‘good’ physician would make], and when the AI has a high confidence of this [requiring many observations of similar situations] and it’s time to call for a second opinion.
In the long run, of course, there will be a point where the [predicted decision a ‘good’ physician would make] is better than the [information gain from a second human opinion] and you really would do best by firing the physician and having the AI make the decisions from then on, trusting for it to call for a second opinion when it is not confident.
(as an example, alpha go zero likely doesn’t benefit from asking another master go player for a ‘second opinion’ when it sees the player it is advising make a bad call)
This idea was inspired by a correspondence with Adam Shimi.
It seem very interesting and important to understand to what extent a purely “behaviorist” view on goal-directed intelligence is viable. That is, given a certain behavior (policy), is it possible to tell whether the behavior is goal-directed and what are its goals, without any additional information?
Consider a general reinforcement learning settings: we have a set of actions A, a set of observations O, a policy is a mapping π:(A×O)∗→ΔA, a reward function is a mapping r:(A×O)∗→[0,1], the utility function is a time discounted sum of rewards. (Alternatively, we could use instrumental reward functions.)
The simplest attempt at defining “goal-directed intelligence” is requiring that the policy π in question is optimal for some prior and utility function. However, this condition is vacuous: the reward function can artificially reward only behavior that follows π, or the prior can believe that behavior not according to π leads to some terrible outcome.
The next natural attempt is bounding the description complexity of the prior and reward function, in order to avoid priors and reward functions that are “contrived”. However, description complexity is only naturally well-defined up to an additive constant. So, if we want to have a crisp concept, we need to consider an asymptotic in which the complexity of something goes to infinity. Indeed, it seems natural to ask that the complexity of the policy should be much higher than the complexity of the prior and the reward function: in this case we can say that the “intentional stance” is an efficient description. However, this doesn’t make sense with description complexity: the description “optimal policy for U and ζ” is of size K(U)+K(ζ)+O(1) (K(x) stands for “description complexity of x”).
To salvage this idea, we need to take not only description complexity but also computational complexity into account. [EDIT: I was wrong, and we can get a well-defined concept in the unbounded setting too, see child comment. The bounded concept is still interesting.] For the intentional stance to be non-vacuous we need to demand that the policy does some “hard work” in order to be optimal. Let’s make it formal. Consider any function of the type f:Σ∗→ΔΞ where Σ and Ξ are some finite alphabets. Then, we can try to represent it by a probabilistic automaton T:S×Σ→Δ(S×Ξ), where S is the finite set space, T is the transition kernel, and we’re feeding symbols into the automaton one by one. Moreover, T can be represented as a boolean circuit R and this circuit can be the output of some program P executed by some fixed universal Turing machine. We can associate with this object 5 complexity parameters:
The description complexity, which is the length of P.
The computation time complexity, which is the size of R.
The computation space complexity, which is the maximum between the depth of R and log|S|.
The precomputation time complexity, which is the time it takes P to run.
The precomputation space complexity, which is the space P needs to run.
It is then natural to form a single complexity measure by applying a logarithm to the times and taking a linear combination of all 5 (we apply a logarithm so that a brute force search over n bits is roughly equivalent to hard-coding n bits). The coefficients in this combination represent the “prices” of the various resources (but we should probably fix the price of description complexity to be 1). Of course not all coefficients must be non-vanishing, it’s just that I prefer to keep maximal generality for now. We will denote this complexity measure C.
We can use such automatons to represent policies, finite POMDP environments and reward functions (ofc not any policy or reward function, but any that can be computed on a machine with finite space). In the case of policies, the computation time/space complexity can be regarded as the time/space cost of applying the “trained” algorithm, whereas the precomputation time/space complexity can be regarded as the time/space cost of training. If we wish, we can also think of the boolean circuit as a recurrent neural network.
We can also use C to define a prior ζ0, by ranging over programs P that output a valid POMDP and assigning probability proportional to 2−C to each instance. (Assuming that the environment has a finite state space might seem restrictive, but becomes quite reasonable if we use a quasi-Bayesian setting with quasi-POMDPs that are not meant to be complete descriptions of the environment; for now we won’t go into details about this.)
Now, return to our policy π. Given g>0, we define that ”π has goal-directed intelligence (at least) g” when there is a suitable prior ζ and utility function U s.t. for any policy π′, if Eζπ′[U]≥Eζπ[U] then C(π′)≥DKL(ζ0||ζ)+C(U)+g. When g=+∞ (i.e. no finite automaton can match the expected utility of π; in particular, this implies π is optimal since any policy can be approximated by a finite automaton), we say that π is “perfectly goal-directed”. Here, DKL(ζ0||ζ) serves as a way to measure the complexity of ζ, which also ensures ζ is non-dogmatic in some rather strong sense.
[EDIT: if we fix U and ζ then g is essentially the same as Yudkowsky’s definition of optimization power if we regard the policy as the “outcome” and use 2−C as our measure on the space of outcomes.]
With this definition we cannot “cheat” by encoding the policy into the prior or into the utility function, since that would allow no complexity difference. Therefore this notion seems like a non-trivial requirement on the policy. On the other hand, this requirement does hold sometimes, because solving the optimization problem can be much more computationally costly than just evaluating the utility function or sampling the prior.
Actually, as opposed to what I claimed before, we don’t need computational complexity bounds for this definition to make sense. This is because the Solomonoff prior is made of computable hypotheses but is uncomputable itself.
Given g>0, we define that ”π has (unbounded) goal-directed intelligence (at least) g” when there is a prior ζ and utility function U s.t. for any policy π′, if Eζπ′[U]≥Eζπ[U] then K(π′)≥DKL(ζ0||ζ)+K(U)+g. Here, ζ0 is the Solomonoff prior and K is Kolmogorov complexity. When g=+∞ (i.e. no computable policy can match the expected utility of π; in particular, this implies π is optimal since any policy can be approximated by a computable policy), we say that π is “perfectly (unbounded) goal-directed”.
Compare this notion to the Legg-Hutter intelligence measure. The LH measure depends on the choice of UTM in radical ways. In fact, for some UTMs, AIXI (which is the maximum of the LH measure) becomes computable or even really stupid. For example, it can always keep taking the same action because of the fear that taking any other action leads to an inescapable “hell” state. On the other hand, goal-directed intelligence differs only by O(1) between UTMs, just like Kolmogorov complexity. A perfectly unbounded goal-directed policy has to be uncomputable, and the notion of which policies are such doesn’t depend on the UTM at all.
I think that it’s also possible to prove that intelligence is rare, in the sense that, for any computable stochastic policy, if we regard it as a probability measure over deterministic policies, then for any ϵ>0 there is g s.t. the probability to get intelligence at least g is smaller than ϵ.
Also interesting is that, for bounded goal-directed intelligence, increasing the prices can only decrease intelligence by O(1), and a policy that is perfectly goal-directed w.r.t. lower prices is also such w.r.t. higher prices (I think). In particular, a perfectly unbounded goal-directed policy is perfectly goal-directed for any price vector. Informally speaking, an agent that is very smart relatively to a context with cheap computational resources is still very smart relatively to a context where they are expensive, which makes intuitive sense.
If we choose just one computational resource, we can speak of the minimal price for which a given policy is perfectly goal-directed, which is another way to measure intelligence with a more restricted domain. Curiously, our bounded Solomonoff-like prior has the shape of a Maxwell-Boltzmann distribution in which the prices are thermodynamic parameters. Perhaps we can regard the minimal price as the point of a phase transition.
Some problems to work on regarding goal-directed intelligence. Conjecture 5 is especially important for deconfusing basic questions in alignment, as it stands in opposition to Stuart Armstrong’s thesis about the impossibility to deduce preferences from behavior alone.
Conjecture. Informally: It is unlikely to produce intelligence by chance. Formally: Denote Π the space of deterministic policies, and consider some μ∈ΔΠ. Suppose μ is equivalent to a stochastic policy π∗. Then, Eπ∼μ[g(π)]=O(C(π∗)).
Find an “intelligence hierarchy theorem”. That is, find an increasing sequence {gn} s.t. for every n, there is a policy with goal-directed intelligence in (gn,gn+1) (no more and no less).
What is the computational complexity of evaluating g given (i) oracle access to the policy or (ii) description of the policy as a program or automaton?
What is the computational complexity of producing a policy with given g?
Conjecture. Informally: Intelligent agents have well defined priors and utility functions. Formally: For every (U,ζ) with C(U)<∞ and DKL(ζ0||ζ)<∞, and every ϵ>0, there exists g∈(0,∞) s.t. for every policy π with intelligence at least g w.r.t. (U,ζ), and every (~U,~ζ) s.t.π has intelligence at least g w.r.t. them, any optimal policies π∗,~π∗ for (U,ζ) and (~U,~ζ) respectively satisfy Eζ~π∗[U]≥Eζπ∗[U]−ϵ.
re: #5, that doesn’t seem to claim that we can infer U given their actions, which is what the impossibility of deducing preferences is actually claiming. That is, assuming 5, we still cannot show that there isn’t some U1≠U2 such that π∗(U1,ζ)=π∗(U2,ζ).
(And as pointed out elsewhere, it isn’t Stuart’s thesis, it’s a well known and basic result in the decision theory / economics / philosophy literature.)
You misunderstand the intent. We’re talking about inverse reinforcement learning. The goal is not necessarily inferring the unknown U, but producing some behavior that optimizes the unknown U. Ofc if the policy you’re observing is optimal then it’s trivial to do so by following the same policy. But, using my approach we might be able to extend it into results like “the policy you’re observing is optimal w.r.t. certain computational complexity, and your goal is to produce an optimal policy w.r.t. higher computational complexity.”
(Btw I think the formal statement I gave for 5 is false, but there might be an alternative version that works.)
I am referring to this and related work by Armstrong.
Epistemic status: Leaning heavily into inside view, throwing humility to the winds.
Imagine TAI is magically not coming (CDT-style counterfactual[1]). Then, the most notable-in-hindsight feature of modern times might be the budding of mathematical metaphysics (Solomonoff induction, AIXI, Yudkowsky’s “computationalist metaphilosophy”[2], UDT, infra-Bayesianism...) Perhaps, this will lead to an “epistemic revolution” comparable only with the scientific revolution in magnitude. It will revolutionize our understanding of the scientific method (probably solving the interpretation of quantum mechanics[3], maybe quantum gravity, maybe boosting the soft sciences). It will solve a whole range of philosophical questions, some of which humanity was struggling with for centuries (free will, metaethics, consciousness, anthropics...)
But, the philosophical implications of the previous epistemic revolution were not so comforting (atheism, materialism, the cosmic insignificance of human life)[4]. Similarly, the revelations of this revolution might be terrifying[5]. In this case, it remains to be seen which will seem justified in hindsight: the Litany of Gendlin, or the Lovecraftian notion that some knowledge is best left alone (and I say this as someone fully committed to keep digging into this mine of Khazad-dum).
Of course, in the real world, TAI is coming.
The EDT-style counterfactual “TAI is not coming” would imply that a lot of my thinking on related topics is wrong which would yield different conclusions. The IB-style counterfactual (conjunction of infradistributions) would probably be some combination of the above with “Nirvana” (contradiction) and “what if I tried my hardest to prevent TAI from coming”, which is also not my intent here.
I mean the idea that philosophical questions can be attacked by reframing them as computer science questions (“how an algorithm feels from inside” et cetera). The name “computationalist metaphilosophy” is my own, not Yudkowsky’s.
No, I don’t think MWI is the right answer.
I’m not implying that learning these implications was harmful. Religion is comforting for some but terrifying and/or oppressive for others.
I have concrete reasons to suspect this, that I will not go into (suspect = assign low but non-negligible probability).
I’m not sure what you mean by CDT- and EDT-style counterfactuals. I have some guesses but please clarify. I think EDT-style counterfactual means, assuming I am a bayesian reasoner, just conditioning on the event “TAI won’t come”, so it’s thinking about the distribution P(O | TAI won’t come).
One could think that the CDT-counterfactual you’re considering means thinking about the distribution P(O | do(TAI doesn’t come)) where do is the do operator from Judea Pearl’s do calculus for causality. In simple words, this means that we consider the world just like ours but whenever someone tries to launch a TAI, god’s intervention (that doesn’t make sense together with everything we know about physics) prevents it from working. But I think this is not what you mean.
My best guess of what counterfactual you mean is as follows. Among all possible sets laws of physics (or, alternatively, Turing machines running which leads to existence of physical realities), you guess that there exists a set of laws that produces a physical reality where there will appear a civilization approximately (but not exactly) like hours and they’ll have a 21-st century approximately like hours, but under their physical laws there won’t be TAI. And you want to analyze what’s going to happen with that civilization.
[edited]
What do you mean by “exact opposite reasons”? To me, it seems like continuation of the same trend of humiliating the human ego:
you are not going to live forever
yes, you are mere atoms
your planet is not the center of the universe
even your sun is not special
your species is related to the other species that you consider inferior
instead of being logical, your mind is a set of short-sighted agents fighting each other
Followed by:
even your reality is not special
your civilization is too stupid to stop doing the thing(s) that will predictably kill all of you
Probably not too original but I haven’t seen it clearly written anywhere.
There are several ways to amplify imitators with different safety-performance tradeoffs. This is something to consider when designing IDA-type solutions.
Amplifying by objective time: The AI is predicting what the user(s) will output after thinking about a problem for a long time. This method is the strongest, but also the least safe. It is the least safe because malign AI might exist in the future, which affects the prediction, which creates an attack vector for future malign AI to infiltrate the present world. We can try to defend by adding a button for “malign AI is attacking”, but that still leaves us open to surprise takeovers in which there is no chance to press the button.
Amplifying by subjective time: The AI is predicting what the user(s) will output after thinking about a problem for a short time, where in the beginning they are given the output of a similar process that ran for one iteration less. So, this simulates a “groundhog day” scenario where the humans wake up in the same objective time period over and over without memory of the previous iterations but with a written legacy. This is weaker than amplifying by objective time, because learning previous results is an overhead, and illegible intuitions might be hard to transmit. This is safer than amplifying by objective time, but if there is some probability of malign AI created in the short time period, there is still an attack vector. The malign AI leakage in this method is roughly proportional to subjective time of simulation times the present rate of malign AI takeover, as opposed to amplification by objective time where leakage is proportional to subjective time of simulation times some average future rate of malign AI takeover. However, by the time we are able to create this benign AI, the present rate of malign AI takeover might also be considerable.
Amplifying by probability: We allow the user(s) to choose “success” or “failure” (or some continuous metric) after completing their work, and make the AI skew the distribution of predictions toward success. This is similar to amplifying by subjective time without any transmission of information. It is weaker and about as safe. The potential advantage is, lower sample complexity: the AI only needs to have a reliable distribution of outcomes after the initial state instead of subsequent states.
Amplifying by parallelization: The AI is predicting the output of many copies of the user working together, by having strictly defined interfaces between the copies, over a time period similar to real time. For example, we can imagine a hierarchical organization where each person gives subtasks to their subordinates. We can then simulate such an organization with a copy of some subset of users in each role. To do this, the AI only needs to learn what a given subset of users would do given a particular task from their supervisors and particular results by their subordinates. This method is weaker than previous methods since it requires that the task at hand can be parallelized.
But, it is also the safest since the rate of malign AI takeover is only amplified by O(1) compared to the background.[EDIT: Actually, it’s not safer than subjective time because the AI would sample the external world independently for each node in the organization. To avoid this, we would need to somehow define a correspondence between the outcome sets of worlds in which the user was queried at different nodes, and I don’t know how to do this.]A complete solution can try to combine all of those methods, by simulating a virtual organization where the members can control which method is applied at every point. This way they can strive for the optimal risk-performance balance: parallelize everything that can be parallelized and amplify otherwise tasks that cannot be parallelized, change the subjective/objective time balance based on research into malign AI timelines etc.
More observations about this attack vector (“attack from counterfactuals”). I focus on “amplifying by subjective time”.
The harder the takeoff the more dangerous this attack vector: During every simulation cycle, ability to defend against simulated malign AI depends on the power of the defense system in the beginning of the cycle[1]. On the other hand, the capability of the attacker depends on its power in the end of the cycle. Therefore, if power grows very fast this is bad news for the defender. On the other hand, if power grows very slowly, the defender wins because defense is probably easier.
Inner control of anchor makes system safer: Given a fixed objective time anchor, the more times the simulation goes through a cycle, the higher its exposure to the threat, since improving the defense system takes time even if you have all the knowledge. Therefore, we want to give the simulated user the ability to move the objective time anchor forward in time, in order to benefit from improvements in the defense system.
Additional information about the external world makes system safer: Suppose our subjective probability of a malign AI successfully attacking during a single simulation cycle is 0.1%. Then performing 1000 cycles superficially yields a failure probability of about 37%. However, this is only the case if attacks on different cycles are independent random variables. If the AI knows as much about the possibility of such an attack as we do, this is a justified assumption. However, if the AI knows more, then, from our subjective perspective, these variables are positively correlated. In particular, if the AI knows with certainty whether a successful attack would occur or not the failure probability over 1000 cycles goes back to 0.1%. Therefore, it is beneficial to give the AI extra information about the world, just so it can estimate the probability of attack with more confidence.
More precisely, it is somewhat better than this since, if we are at some late cycle among a sequence of cycles anchored to same point in objective time, the simulated user can use knowledge generated in previous cycles to improve the defense system during the current cycle. So, early cycles contribute more to the risk.
I think this would make a good top-level post. I have the feeling I’ll want to link to it later.
I retracted part of that, see the edit.
I have repeatedly argued for a departure from pure Bayesianism that I call “quasi-Bayesianism”. But, coming from a LessWrong-ish background, it might be hard to wrap your head around the fact Bayesianism is somehow deficient. So, here’s another way to understand it, using Bayesianism’s own favorite trick: Dutch booking!
Consider a Bayesian agent Alice. Since Alice is Bayesian, ey never randomize: ey just follow a Bayes-optimal policy for eir prior, and such a policy can always be chosen to be deterministic. Moreover, Alice always accepts a bet if ey can choose which side of the bet to take: indeed, at least one side of any bet has non-negative expected utility. Now, Alice meets Omega. Omega is very smart so ey know more than Alice and moreover ey can predict Alice. Omega offers Alice a series of bets. The bets are specifically chosen by Omega s.t. Alice would pick the wrong side of each one. Alice takes the bets and loses, indefinitely. Alice cannot escape eir predicament: ey might know, in some sense, that Omega is cheating em, but there is no way within the Bayesian paradigm to justify turning down the bets.
A possible counterargument is, we don’t need to depart far from Bayesianism to win here. We only need to somehow justify randomization, perhaps by something like infinitesimal random perturbations of the belief state (like with reflective oracles). But, in a way, this is exactly what quasi-Bayesianism does: a quasi-Bayes-optimal policy is in particular Bayes-optimal when the prior is taken to be in Nash equilibrium of the associated zero-sum game. However, Bayes-optimality underspecifies the policy: not every optimal reply to a Nash equilibrium is a Nash equilibrium.
This argument is not entirely novel: it is just a special case of an environment that the agent cannot simulate, which is the original motivation for quasi-Bayesianism. In some sense, any Bayesian agent is dogmatic: it dogmatically beliefs that the environment is computationally simple, since it cannot consider a hypothesis which is not. Here, Omega exploits this false dogmatic belief.
Bayeseans are allowed to understand that there are agents with better estimates than they have. And that being offered a bet _IS_ evidence that the other agent THINKS they have an advantage.
Randomization (aka “mixed strategy”) is well-understood as the rational move in games where opponents are predicting your choices. I have read nothing that would even hint that it’s unavailable to Bayesean agents. The relevant probability (updated per Bayes’s Rule) would be “is my counterpart trying to minimize my payout based on my choices”.
edit: I realize you may be using a different definition of “bayeseanism” than I am. I’m thinking humans striving for rational choices, which perforce includes the knowledge of incomplete computation and imperfect knowledge. Naive agents can be imagined that don’t have this complexity. Those guys are stuck, and Omega’s gonna pwn them.
It feels like there’s better words for this like rationality, whereas bayesianism is a more specific philosophy about how best to represent and update beliefs.
And here I thought the reason was going to be that Bayesianism doesn’t appear to include the cost of computation. (Thus, the usual dutch book arguments should be adjusted so that “optimal betting” does not leave one worse off for having payed, say, an oracle, too much for computation.)
Game theory is widely considered the correct description of rational behavior in multi-agent scenarios. However, real world agents have to learn, whereas game theory assumes perfect knowledge, which can be only achieved in the limit at best. Bridging this gap requires using multi-agent learning theory to justify game theory, a problem that is mostly open (but some results exist). In particular, we would like to prove that learning agents converge to game theoretic solutions such as Nash equilibria (putting superrationality aside: I think that superrationality should manifest via modifying the game rather than abandoning the notion of Nash equilibrium).
The simplest setup in (non-cooperative) game theory is normal form games. Learning happens by accumulating evidence over time, so a normal form game is not, in itself, a meaningful setting for learning. One way to solve this is replacing the normal form game by a repeated version. This, however, requires deciding on a time discount. For sufficiently steep time discounts, the repeated game is essentially equivalent to the normal form game (from the perspective of game theory). However, the full-fledged theory of intelligent agents requires considering shallow time discounts, otherwise there is no notion of long-term planning. For shallow time discounts, the game theory of a repeated game is very different from the game theory of the original normal form game. In fact, the folk theorem asserts that any payoff vector above the maximin of each player is a possible Nash payoff. So, proving convergence to a Nash equilibrium amounts (more or less) to proving converges to at least the maximin payoff. This is possible using incomplete models, but doesn’t seem very interesting: to receive the maximin payoff, the agents only have to learn the rules of the game, they need not learn the reward functions of the other players or anything else about them.
We arrive at the question, what setting is realistic (in the sense of involving learning with shallow time discount) and is expected to produce Nash equilibria for a normal form game? I suggest the following. Instead of a fixed set of agents repeatedly playing against each other, we consider a population of agents that are teamed-off randomly on each round of the game. The population is assumed to be large enough for agents not to encounter each other more than once. This can be formalized as follows. Let Ai be the pure strategy set of the i-th agent and O:=∏iAi the set of pure outcomes. The set of n-round outcome histories is On. The population of agents on the n-round can then be described as a probability measure μn∈ΔOn. Suppose the policy of the i-th player (that is, of all the agents that take the role of the i-th player) is πi:On→ΔAi. Then we can define a time evolution rule that produces μn+1 from μn. This rule works as follows: in order to sample μn+1 we sample μn once per player (this is the history the given player has seen), sample the policy of each player on its own history, and produce a new history by appending the resulting outcome to one of the old histories (it doesn’t matter which). A set of policies is considered to be in equilibrium, when for any i, and any alternative policy π′i, letting π′i play against the same population (i.e. all other copies of the i-th player still play πi) doesn’t improve expected utility. In other words, on each round the “mutant” agent retains its own history but the other player histories are still sampled from the same μn. It is easy to see that any equilibrium payoff in this setting is a Nash payoff in the original normal form game. We can then legitimately ask whether taking the πi to be learning algorithms would result in convergence to a Nash payoff in the γ→1 (shallow time discount) limit.
For example, consider the Prisoner’s dilemma. In the repeated Prisoner’s dilemma with shallow time discount, CC is an equilibrium because of the tit-for-tat policy. On the other hand, in the “population” (massively multi-player?) repeated Prisoner’s dilemma, DD is the only equilibrium. Tit-for-tat doesn’t work because a single “defect bot” can exploit a population of tit-for-tats: on each round it plays with a new opponent that doesn’t know the defect bot defected on the previous round.
Note that we get a very different setting if we allow the players to see each other’s histories, more similar (equivalent?) to the regular repeated game. For example, in the Prisoner’s Dilemma we have a version of tit-for-tat that responds to what its current opponent played in its previous round (against a different opponent). This may be regarded as a confirmation of the idea that agents that know each other’s source code are effectively playing a repeated game: in this setting, knowing the source code amounts to knowing the history.
We can modify the population game setting to study superrationality. In order to do this, we can allow the agents to see a fixed size finite portion of the their opponents’ histories. This should lead to superrationality for the same reasons I discussed before. More generally, we can probably allow each agent to submit a finite state automaton of limited size, s.t. the opponent history is processed by the automaton and the result becomes known to the agent.
What is unclear about this is how to define an analogous setting based on source code introspection. While arguably seeing the entire history is equivalent to seeing the entire source code, seeing part of the history, or processing the history through a finite state automaton, might be equivalent to some limited access to source code, but I don’t know to define this limitation.
EDIT: Actually, the obvious analogue is processing the source code through a finite state automaton.
Instead of postulating access to a portion of the history or some kind of limited access to the opponent’s source code, we can consider agents with full access to history / source code but finite memory. The problem is, an agent with fixed memory size usually cannot have regret going to zero, since it cannot store probabilities with arbitrary precision. However, it seems plausible that we can usually get learning with memory of size O(log11−γ). This is because something like “counting pieces of evidence” should be sufficient. For example, if consider finite MDPs, then it is enough to remember how many transitions of each type occurred to encode the belief state. There question is, does assuming O(log11−γ) memory (or whatever is needed for learning) is enough to reach superrationality.
What do you mean by equivalent? The entire history doesn’t say what the opponent will do later or would do against other agents, and the source code may not allow you to prove what the agent does if it involves statements that are true but not provable.
For a fixed policy, the history is the only thing you need to know in order to simulate the agent on a given round. In this sense, seeing the history is equivalent to seeing the source code.
The claim is: In settings where the agent has unlimited memory and sees the entire history or source code, you can’t get good guarantees (as in the folk theorem for repeated games). On the other hand, in settings where the agent sees part of the history, or is constrained to have finite memory (possibly of size O(log11−γ)?), you can (maybe?) prove convergence to Pareto efficient outcomes or some other strong desideratum that deserves to be called “superrationality”.
In the previous “population game” setting, we assumed all players are “born” at the same time and learn synchronously, so that they always play against players of the same “age” (history length). Instead, we can consider a “mortal population game” setting where each player has a probability 1−γ to die on every round, and new players are born to replenish the dead. So, if the size of the population is N (we always consider the “thermodynamic” N→∞ limit), N(1−γ) players die and the same number of players are born on every round. Each player’s utility function is a simple sum of rewards over time, so, taking mortality into account, effectively ey have geometric time discount. (We could use age-dependent mortality rates to get different discount shapes, or allow each type of player to have different mortality=discount rate.) Crucially, we group the players into games randomly, independent of age.
As before, each player type i chooses a policy . (We can also consider the case where players of the same type may have different policies, but let’s keep it simple for now.) In the thermodynamic limit, the population is described as a distribution over histories, which now are allowed to be of variable length: μn∈ΔO∗. For each assignment of policies to player types, we get dynamics μn+1=Tπ(μn) where Tπ:ΔO∗→ΔO∗. So, as opposed to immortal population games, mortal population games naturally give rise to dynamical systems.
If we consider only the age distribution, then its evolution doesn’t depend on π and it always converges to the unique fixed point distribution ζ(k)=(1−γ)γk. Therefore it is natural to restrict the dynamics to the subspace of ΔO∗ that corresponds to the age distribution ζ. We denote it P.
Does the dynamics have fixed points?O∗ can be regarded as a subspace of (O⊔{⊥})ω. The later is compact (in the product topology) by Tychonoff’s theorem and Polish, but O∗ is not closed. So, w.r.t. the weak topology on probability measure spaces, Δ(O⊔{⊥})ω is also compact but ΔO∗ isn’t. However, it is easy to see that P is closed in Δ(O⊔{⊥})ω and therefore compact. It may also be regarded as a convex subset of an appropriate Banach space (the dual of the space of Lipschitz functions on some metrization of (O⊔{⊥})ω). Moreover, it is easy to see Tπ is continuous (for populations that are close in the Kantorovich-Rubinstein metric, only the old players may have very different distributions, but old players are a small fraction of the population so their effect on the next round is small). By the Schauder fixed-point theorem, it follows that Tπ has a fixed point.
What are the fixed points like? Of course it depends on π. In a fixed point, every player observes a sequence of IID plays in all of eir games. Therefore, if π satisfies the (very mild!) learning-theoretic desideratum that, upon observing an IID sequence, it converges to optimal response in the γ→1 limit, then, in the same limit, fixed points are Nash equilibria. This works even for extremely simple learning algorithms, such as “assume the plays in the next game will be sampled from a random past game”, and it works for any Bayesian or “quasi-Bayesian” (i.e. using incomplete/fuzzy models) agent that includes all IID processes in its prior.
This raises a range of interesting questions:
Are any/all of the fixed points attractors?
Does convergence to a fixed point occur for all or at least almost all initial conditions?
Do all Nash equilibria correspond to fixed points?
Do stronger game theoretic solution concepts (e.g. proper equilibria) have corresponding dynamical properties?
Mortal population games are obviously reminiscent of evolutionary game theory. However, there are substantial differences. In mortal population games, the game doesn’t have to be symmetric, we consider a single policy rather than many competing policies, the policies learn from experience instead of corresponding to fixed strategies, and mortality rate doesn’t depend on the reward. In evolutionary game theory, convergence usually cannot be guaranteed. For example, in the rock-scissors-paper game, the population may cycle among the different strategies. On the other hand, in mortal population games, if the game is two-player zero-sum (which includes rock-paper-scissors), and the policy is quasi-Bayesian with appropriate prior, convergence is guaranteed. This is because each player can easily learn to guarantee maximin payoff. Continuity arguments probably imply that at least for small perturbations of zero-sum, there will still be convergence. This leads to some hope that convergence can be guaranteed even in general games, or at least under some relatively mild conditions.
Here’s a question inspired by thinking about Turing RL, and trying to understand what kind of “beliefs about computations” should we expect the agent to acquire.
Does mathematics have finite information content?
First, let’s focus on computable mathematics. At first glance, the answer seems obviously “no”: because of the halting problem, there’s no algorithm (i.e. a Turing machine that always terminates) which can predict the result of every computation. Therefore, you can keep learning new facts about results of computations forever. BUT, maybe most of those new facts are essentially random noise, rather than “meaningful” information?
Is there a difference of principle between “noise” and “meaningful content”? It is not obvious, but the answer is “yes”: in algorithmic statistics there is the notion of “sophistication” which measures how much “non-random” information is contained in some data. In our setting, the question can be operationalized as follows: is it possible to have an algorithm A plus an infinite sequence of bits R, s.t.R is random in some formal sense (e.g. Martin-Lof) and A can decide the output of any finite computation if it’s also given access to R?
The answer to the question above is “yes”! Indeed, Chaitin’s constant is Martin-Lof random. Given access to Chaitin’s constant, it is possible to construct a halting oracle, therefore A can decide whether the computation halts, and if it does, run it (and if doesn’t, output N/A or whatever).
[EDIT: Actually, this is not quite right. The way you use Chaitin’s constant to emulate a halting oracle produces something that’s only guaranteed to halt if you give it the correct Chaitin’s constant.]
But, this is a boring solution. In practice we are interested at efficient methods of answering mathematical questions, and beliefs acquired by resource bounded agents. Hence, the question becomes: given a resource bound B (e.g. a bound on space or time complexity), is it possible to have A and R similar to above, s.t.A respects the bound B and R is pseudorandom in some formal sense w.r.t. the bound B?
[EDIT: I guess that the analogous thing to the unbounded setting would be, A only has to respect B when given the correct R. But the real conclusion is probably that we should look for something else instead, e.g. some kind of infradistribution.]
This is a fun question, because any answer would be fascinating in its own way: either computable mathematics has finite content in some strong formal sense (!) or mathematics is infinitely sophisticated in some formal sense (!)
We can also go in the other direction along the “hierarchy of feasibility”, although I’m not sure how useful is that. Instead of computable mathematics, let’s consider determining the truth (not provability, but actual truth) of sentences in e.g. Peano Arithmetic. Does A and R as above still exist? This would require e.g. a Martin-Lof random sequence which allows making any finite number of Turing jumps.
Wikipedia claims that every sequence is Turing reducible to a random one, giving a positive answer to the non-resource-bounded version of any question of this form. There might be a resource-bounded version of this result as well, but I’m not sure.
Epistemic status: no claims to novelty, just (possibly) useful terminology.
[EDIT: I increased all the class numbers by 1 in order to admit a new definition of “class I”, see child comment.]
I propose a classification on AI systems based on the size of the space of attack vectors. This classification can be applied in two ways: as referring to the attack vectors a priori relevant to the given architectural type, or as referring to the attack vectors that were not mitigated in the specific design. We can call the former the “potential” class and the latter the “effective” class of the given system. In this view, the problem of alignment is designing potential class V (or at least IV) systems are that effectively class 0 (or at least I-II).
Class II: Systems that only ever receive synthetic data that has nothing to do with the real world
Examples:
AI that is trained to learn Go by self-play
AI that is trained to prove random mathematical statements
AI that is trained to make rapid predictions of future cell states in the game of life for random initial conditions
AI that is trained to find regularities in sequences corresponding to random programs on some natural universal Turing machine with bounded runtime
Class II systems by and large don’t admit any attack vectors. [EDIT: Inaccurate, see child comment]
Such systems might have higher effective class if bugs in the implementation lead to real-world data leaking into the system, or if the ostensibly synthetic data reveals something important about the world via the choices made by its designers (for example, a video game with complex rules inspired by the real world).
Class III: Systems for which there is no distribution shift between training and deployment, and also the label space is small
Example: AI that is trained to distinguish between cat images and dog images, and the images selected for training are a perfectly representative sample of images used in deployment.
Class III systems admit attacks by non-Cartesian daemons.
If the label space is large, a Cartesian daemon can choose a low probability for randomly producing a malign label, such that there is a significant probability that this won’t happen in training but will happen in deployment. This moves the system to class IV. If there is distribution shift, a Cartesian daemon can distinguish between training and deployment and use it to perform a “treacherous turn” attack. This also moves the system to class IV.
Such systems have lower effective class if non-Cartesian daemons are mitigated, for example by well-designed applications of homomorphic cryptography. They have higher effective class if deployed in a setting which does involve distributional shift, perhaps unanticipated by the designers.
Class IV: Systems which are non-agentic but do involve distribution shift or large label space
Examples:
AI that learns to imitate humans
AI that learns to predict the stock market
Generative language models
Class IV systems admit attacks by Cartesian and non-Cartesian daemons. [EDIT: Also attack from counterfactuals. The latter requires a large label space and doesn’t require a distribution shift per se.]
Such systems have lower effective class if Cartesian daemons are mitigated, for example by carefully shaping the prior / inductive bias and applying some sort of confidence threshold / consensus algorithm. They can be effective class V if not designed to avoid self-fulfilling prophecies and/or incentives to mispredict at present to improve prediction in the future.
Class V: Agentic systems
Examples:
AI that trades in the stock market
AI that optimizes long-term ad revenue
AI that defends the world against unaligned AIs
Class V systems admit attacks by daemons but are also dangerous by default due to divergence of their utility function from the human utility function.
Such system can have lower effective class if the utility function is very cleverly designed, for example to reliably accomplish learning of human values.
The idea comes from this comment of Eliezer.
Class II or higher systems might admit an attack vector by daemons that infer the universe from the agent’s source code. That is, we can imagine a malign hypothesis that makes a treacherous turn after observing enough past actions to infer information about the system’s own source code and infer the physical universe from that. (For example, in a TRL setting it can match the actions to the output of a particular program for envelope.) Such daemons are not as powerful as malign simulation hypotheses, since their prior probability is not especially large (compared to the true hypothesis), but might still be non-negligible. Moreover, it is not clear whether the source code can realistically have enough information to enable an attack, but the opposite is not entirely obvious.
To account for this I propose the designate class I systems which don’t admit this attack vector. For the potential sense, it means that either (i) the system’s design is too simple to enable inferring much about the physical universe, or (ii) there is no access to past actions (including opponent actions for self-play) or (iii) the label space is small, which means an attack requires making many distinct errors, and such errors are penalized quickly. And ofc it requires no direct access to the source code.
We can maybe imagine an attack vector even for class I systems, if most metacosmologically plausible universes are sufficiently similar, but this is not very likely. Nevertheless, we can reserve the label class 0 for systems that explicitly rule out even such attacks.
I find it interesting to build simple toy models of the human utility function. In particular, I was thinking about the aggregation of value associated with other people. In utilitarianism this question is known as “population ethics” and is infamously plagued with paradoxes. However, I believe that is the result of trying to be impartial. Humans are very partial and this allows coherent ways of aggregation. Here is my toy model:
Let Alice be our viewpoint human. Consider all social interactions Alice has, categorized by some types or properties, and assign a numerical weight to each type of interaction. Let it(A,B)>0 be the weight of the interaction person A had with person B at time t (if there was no interaction at this time then it(A,B)=0). Then, we can define Alice’s affinity to Bob as
afft(Alice,Bob):=t∑s=−∞αt−sis(Alice,Bob)
Here α∈(0,1) is some constant. Ofc αt−s can be replaced by many other functions.
Now, we can the define the social distance of Alice to Bob as
dt(Alice,Bob):=infp1…pn:p1=Alice,pn=Bobn−1∑k=1afft(pk,pk+1)−β
Here β>0 is some constant, and the power law was chosen rather arbitrarily, there are many functions of aff that can work. Dead people should probably count in the infimum, but their influence wanes over time since they don’t interact with anyone (unless we count consciously thinking about a person as an interaction, which we might).
This is a time-dependent metric (or quasimetric, if we allow for asymmetric interactions such as thinking about someone or admiring someone from afar) on the set of people. If i is bounded and there is a bounded number of people Alice can interact with at any given time, then there is some C>1 s.t. the number of people within distance r from Alice is O(Cr). We now define the reward as
rt(Alice):=∑pλdt(Alice,p)wt(p)
Here λ∈(0,1C) is some constant and wt(p) is the “welfare” of person p at time t, or whatever is the source of value of people for Alice. Finally, the utility function is a time discounted sum of rewards, probably not geometric (because hyperbolic discounting is a thing). It is also appealing to make the decision rule to be minimax-regret over all sufficiently long time discount parameters, but this is tangential.
Notice how the utility function is automatically finite and bounded, and none of the weird paradoxes of population ethics and infinitary ethics crop up, even if there is an infinite number of people in the universe. I like to visualize people space a tiling of hyperbolic space, with Alice standing in the center of a Poincare or Beltrami-Klein model of it. Alice’s “measure of caring” is then proportional to volume in the model (this probably doesn’t correspond to exactly the same formula but it’s qualitatively right, and the formula is only qualitative anyway).
Some thoughts about embedded agency.
From a learning-theoretic perspective, we can reformulate the problem of embedded agency as follows: What kind of agent, and in what conditions, can effectively plan for events after its own death? For example, Alice bequeaths eir fortune to eir children, since ey want them be happy even when Alice emself is no longer alive. Here, “death” can be understood to include modification, since modification is effectively destroying an agent and replacing it by different agent[1]. For example, Clippy 1.0 is an AI that values paperclips. Alice disabled Clippy 1.0 and reprogrammed it to value staples before running it again. Then, Clippy 2.0 can be considered to be a new, different agent.
First, in order to meaningfully plan for death, the agent’s reward function has to be defined in terms of something different than its direct perceptions. Indeed, by definition the agent no longer perceives anything after death. Instrumental reward functions are somewhat relevant but still don’t give the right object, since the reward is still tied to the agent’s actions and observations. Therefore, we will consider reward functions defined in terms of some fixed ontology of the external world. Formally, such an ontology can be an incomplete[2] Markov chain, the reward function being a function of the state. Examples:
The Markov chain is a representation of known physics (or some sector of known physics). The reward corresponds to the total mass of diamond in the world. To make this example work, we only need enough physics to be able to define diamonds. For example, we can make do with quantum electrodynamics + classical gravity and have the Knightian uncertainty account for all nuclear and high-energy phenomena.
The Markov chain is a representation of people and social interactions. The reward correspond to concepts like “happiness” or “friendship” et cetera. Everything that falls outside the domain of human interactions is accounted by Knightian uncertainty.
The Markov chain is Botworld with some of the rules left unspecified. The reward is the total number of a particular type of item.
Now we need to somehow connect the agent to the ontology. Essentially we need a way of drawing Cartesian boundaries inside the (a priori non-Cartesian) world. We can accomplish this by specifying a function that assigns an observation and projected action to every state out of some subset of states. Entering this subset corresponds to agent creation, and leaving it corresponds to agent destruction. For example, we can take the ontology to be Botworld + marked robot and the observations and actions be the observations and actions of that robot. If we don’t want marking a particular robot as part of the ontology, we can use a more complicated definition of Cartesian boundary that specifies a set of agents at each state plus the data needed to track these agents across time (in this case, the observation and action depend to some extent on the history and not only the current state). I will leave out the details for now.
Finally, we need to define the prior. To do this, we start by choosing some prior over refinements of the ontology. By “refinement”, I mean removing part of the Knightian uncertainty, i.e. considering incomplete hypotheses which are subsets of the “ontological belief”. For example, if the ontology is underspecified Botworld, the hypotheses will specify some of what was left underspecified. Given such a “objective” prior and a Cartesian boundary, we can construct a “subjective” prior for the corresponding agent. We transform each hypothesis via postulating that taking an action that differs from the projected action leads to “Nirvana” state. Alternatively, we can allow for stochastic action selection and use the gambler construction.
Does this framework guarantee effective planning for death? A positive answer would correspond to some kind of learnability result (regret bound). To get learnability, will first need that the reward is either directly on indirectly observable. By “indirectly observable” I mean something like with semi-instrumental reward functions, but accounting for agent mortality. I am not ready to formulate the precise condition atm. Second, we need to consider an asymptotic in which the agent is long lived (in addition to time discount being long-term), otherwise it won’t have enough time to learn. Third (this is the trickiest part), we need the Cartesian boundary to flow with the asymptotic as well, making the agent “unspecial”. For example, consider Botworld with some kind of simplicity prior. If I am a robot born at cell zero and time zero, then my death is an event of low description complexity. It is impossible to be confident about what happens after such a simple event, since there will always be competing hypotheses with different predictions and a probability that is only lower by a factor of Ω(1). On the other hand, if I am a robot born at cell 2439495 at time 9653302 then it would be surprising if the outcome of my death would be qualitatively different from the outcome of the death of any other robot I observed. Finding some natural, rigorous and general way to formalize this condition is a very interesting problem. Of course, even without learnability we can strive for Bayes-optimality or some approximation thereof. But, it is still important to prove learnability under certain conditions to test that this framework truly models rational reasoning about death.
Additionally, there is an intriguing connection between some of these ideas and UDT, if we consider TRL agents. Specifically, a TRL agent can have a reward function that is defined in terms of computations, exactly like UDT is often conceived. For example, we can consider an agent whose reward is defined in terms of a simulation of Botworld, or in terms of taking expected value over a simplicity prior over many versions of Botworld. Such an agent would be searching for copies of itself inside the computations it cares about, which may also be regarded as a form of “embeddedness”. It seems like this can be naturally considered a special case of the previous construction, if we allow the “ontological belief” to include beliefs pertaining to computations.
Unless it’s some kind of modification that we treat explicitly in our model of the agent, for example a TRL agent reprogramming its own envelope.
“Incomplete” in the sense of Knightian uncertainty, like in quasi-Bayesian RL.
Learning theory distinguishes between two types of settings: realizable and agnostic (non-realizable). In a realizable setting, we assume that there is a hypothesis in our hypothesis class that describes the real environment perfectly. We are then concerned with the sample complexity and computational complexity of learning the correct hypothesis. In an agnostic setting, we make no such assumption. We therefore consider the complexity of learning the best approximation of the real environment. (Or, the best reward achievable by some space of policies.)
In offline learning and certain varieties of online learning, the agnostic setting is well-understood. However, in more general situations it is poorly understood. The only agnostic result for long-term forecasting that I know is Shalizi 2009, however it relies on ergodicity assumptions that might be too strong. I know of no agnostic result for reinforcement learning.
Quasi-Bayesianism was invented to circumvent the problem. Instead of considering the agnostic setting, we consider a “quasi-realizable” setting: there might be no perfect description of the environment in the hypothesis class, but there are some incomplete descriptions. But, so far I haven’t studied quasi-Bayesian learning algorithms much, so how do we know it is actually easier than the agnostic setting? Here is a simple example to demonstrate that it is.
Consider a multi-armed bandit, where the arm space is [0,1]. First, consider the follow realizable setting: the reward is a deterministic function r:[0,1]→[0,1] which is known to be a polynomial of degree d at most. In this setting, learning is fairly easy: it is enough to sample d+1 arms in order to recover the reward function and find the optimal arm. It is a special case of the general observation that learning is tractable when the hypothesis space is low-dimensional in the appropriate sense.
Now, consider a closely related agnostic setting. We can still assume the reward function is deterministic, but nothing is known about its shape and we are still expected to find the optimal arm. The arms form a low-dimensional space (one-dimensional actually) but this helps little. It is impossible to predict anything about any arm except those we already tested, and guaranteeing convergence to the optimal arm is therefore also impossible.
Finally, consider the following quasi-realizable setting: each incomplete hypothesis in our class states that the reward function is lower-bounded by a particular polynomial f:[0,1]→[0,1] of degree d at most. Our algorithm needs to converge to a reward which is at least the maximum of maxima of correct lower bounds. So, the desideratum is weaker than in the agnostic case, but we still impose no hard constraint on the reward function. In this setting, we can use the following algorithm. On each step, fit the most optimistic lower bound to those arms that were already sampled, find its maximum and sample this arm next. I haven’t derived the convergence rate, but it seems probable the algorithm will converge rapidly (for low d). This is likely to be a special case of some general result on quasi-Bayesian learning with low-dimensional priors.
Master post for alignment protocols.
Other relevant shortforms:
Autocalibrated quantilized debate
Hippocratic principle
IDA variants
Dialogic RL
More dialogic RL
Precursor Detection, Classification and Assistance (PreDCA)
Infra-Bayesian physicalism provides us with two key building blocks:
Given a hypothesis about the universe, we can tell which programs are running. (This is just the bridge transform.)
Given a program, we can tell whether it is an agent, and if so, which utility function it has[1] (the “evaluating agent” section of the article).
I will now outline how we can use these building blocks to solve both the inner and outer alignment problem. The rough idea is:
For each hypothesis in the prior, check which agents are precursors of our agent according to this hypothesis.
Among the precursors, check whether some are definitely neither humans nor animals nor previously created AIs.
If there are precursors like that, discard the hypothesis (it is probably a malign simulation hypothesis).
If there are no precursors like that, decide which of them are humans.
Follow an aggregate of the utility functions of the human precursors (conditional on the given hypothesis).
Detection
How to identify agents which are our agent’s precursors? Let our agent be G and let H be another agents which exists in the universe according to hypothesis Θ[2]. Then, H is considered to be a precursor of G in universe Θ when there is some H-policy σ s.t. applying the counterfactual ”H follows σ” to Θ (in the usual infra-Bayesian sense) causes G not to exist (i.e. its source code doesn’t run).
A possible complication is, what if Θ implies that H creates G / doesn’t interfere with the creation of G? In this case H might conceptually be a precursor, but the definition would not detect it. It is possible that any such Θ would have a sufficiently large description complexity penalty that it doesn’t matter. On the second hand, if Θ is unconditionally Knightian uncertain about H creating G then the utility will be upper bounded by the scenario in which G doesn’t exist, which is liable to make Θ an effectively falsified hypothesis. On the third hand, it seems plausible that the creation of G by H would be contingent on G’s behavior (Newcomb-style, which we know how it works in infra-Bayesianism), in which case Θ is not falsified and the detection works. In any case, there is a possible variant of the definition to avoid the problem: instead of examining only Θ we also examine coarsenings of Θ which are not much more complex to describe (in the hope that some such coarsening would leave the creation of G uncertain).
Notice that any agent whose existence is contingent on G’s policy cannot be detected as a precursor: the corresponding program doesn’t even “run”, because we don’t apply a G-policy-counterfactual to the bridge transform.
Classification
How to decide which precursors are which? One tool we have is the g parameter and the computational resource parameters in the definition of intelligence. In addition we might be able to create a very rough neuroscience-based model of humans. Also, we will hopefully have a lot of information about other AIs that can be relevant. Using these, it might be possible to create a rough benign/malign/irrelevant classifier, s.t.
Humans are classified as “benign”.
Most (by probability mass) malign simulation hypotheses contain at least one precursor classified as “malign”.
Non-human agents that exist in the causal past of our AI in the null (non-simulation) hypothesis are classified as “irrelevant”.
Assistance
Once we detected and classified precursors in each hypothesis, we discard all hypotheses that contain malign precursors. In the remaining hypotheses, we perform some kind of aggregation on the utility functions of the benign precursors (for example, this). The utility functions from different hypotheses are somehow normalized to form the overall utility function. Alternatively, we do a maximal lottery vote for the policy, where each hypothesis is a voter with weight proportional to its prior probability mass.
Inner Alignment
Why can this solve inner alignment? In any model-based approach, the AI doesn’t train the policy directly. Instead, it trains models and uses them to compute the policy. I suspect that the second step cannot create mesa-optimizers, since it only involves control and not learning[3]. Hence, any mesa-optimizer has to originate from the first step, i.e. from the model/hypothesis. And, any plausible physicalist hypothesis which contains a mesa-optimizer has to look like a malign simulation hypothesis.
Outer Alignment
Why can this solve outer alignment? Presumably, we are aggregating human utility functions. This doesn’t assume humans are perfect agents: g can be less than infinity. I suspect that when g<∞ the utility function becomes somewhat ambiguous, but the ambiguity can probably be resolved arbitrarily or maybe via a risk-averse method. What if the AI modifies the humans? Then only pre-modification humans are detected as precursors, and there’s no problem.
Moreover, the entire method can be combined with the Hippocratic principle to avoid catastrophic mistakes out of ignorance (i.e. to go from intent alignment to impact alignment).
We do need a lot more research to fully specify this “utility reconstruction” and check that it satisfies reasonable desiderata. But, the existence of a natural utility-function-dependent measure of intelligence suggests it is possible.
I’m ignoring details like “what if H only exists with certain probability”. The more careful analysis is left for later.
In modern deep RL systems, there might not be a clear line between learning and control. For example, if we use model-free RL to produce the policy for a given hypothesis, then there is learning happening there as well. In such an architecture, the value function or Q-function should be regarded as part of the hypothesis for our purpose.
Can you please explain how does this not match the definition? I don’t yet understand all the math, but intuitively, if H creates G / doesn’t interfere with the creation of G, then if H instead followed policy “do not create G/ do interfere with the creation of G”, then G’s code wouldn’t run?
Can you please give an example of a precursor that does match the definition?
The problem is that if Θ implies that H creates G but you consider a counterfactual in which H doesn’t create G then you get an inconsistent hypothesis i.e. a HUC which contains only 0. It is not clear what to do with that. In other words, the usual way of defining counterfactuals in IB (I tentatively named it “hard counterfactuals”) only makes sense when the condition you’re counterfactualizing on is something you have Knightian uncertainty about (which seems safe to assume if this condition is about your own future action but not safe to assume in general). In a child post I suggested solving this by defining “soft counterfactuals” where you consider coarsenings of Θ in addition to Θ itself.
Thank you.
Some additional thoughts.
Non-Cartesian Daemons
These are notoriously difficult to deal with. The only methods I know are that applicable to other protocols are homomorphic cryptography and quantilization of envelope (external computer) actions. But, in this protocol, they are dealt with the same as Cartesian daemons! At least if we assume a non-Cartesian attack requires an envelope action, the malign hypotheses which are would-be sources of such actions are discarded without giving an opportunity for attack.
Weaknesses
My main concerns with this approach are:
The possibility of major conceptual holes in the definition of precursors. More informal analysis can help, but ultimately mathematical research in infra-Bayesian physicalism in general and infra-Bayesian cartesian/physicalist multi-agent interactions in particular is required to gain sufficient confidence.
The feasibility of a good enough classifier. At present, I don’t have a concrete plan for attacking this, as it requires inputs from outside of computer science.
Inherent “incorrigibility”: once the AI becomes sufficiently confident that it correctly detected and classified its precursors, its plans won’t defer to the users any more than the resulting utility function demands. On the second hand, I think the concept of corrigibility is underspecified so much that I’m not sure it is solved (rather than dissolved) even in the Book. Moreover, the concern can be ameliorated by sufficiently powerful interpretability tools. It is therefore desirable to think more of how to achieve interpretability in this context.
A question that often comes up in discussion of IRL: are agency and values purely behavioral concepts, or do they depend on how the system produces its behavior? The cartesian measure of agency I proposed seems purely behavioral, since it only depends on the policy. The physicalist version seems less so since it depends on the source code, but this difference might be minor: this role of the source is merely telling the agent “where” it is in the universe. However, on closer examination, the physicalist g is far from purely behaviorist, and this is true even for cartesian Turing RL. Indeed, the policy describes not only the agent’s interaction with the actual environment but also its interaction with the “envelope” computer. In a sense, the policy can be said to reflects the agent’s “conscious thoughts”.
This means that specifying an agent requires not only specifying its source code but also the “envelope semantics” C (possibly we also need to penalize for the complexity of C in the definition of g). Identifying that an agent exists requires not only that its source code is running, but also, at least that its history h is C-consistent with the α∈2Γ variable of the bridge transform. That is, for any y∈α we must have dCy for some destiny d⊐h. In other words, we want any computation the agents ostensibly runs on the envelope to be one that is physically manifest (it might be this condition isn’t sufficiently strong, since it doesn’t seem to establish a causal relation between the manifesting and the agent’s observations, but it’s at least necessary).
Notice also that the computational power of the envelope implied by C becomes another characteristic of the agent’s intelligence, together with g as a function of the cost of computational resources. It might be useful to come up with natural ways to quantify this power.
Here’s a video of a talk I gave about PreDCA.
Two more remarks.
User Detection
It can be useful to identify and assist specifically the user rather than e.g. any human that ever lived (and maybe some hominids). For this purpose I propose the following method. It also strengthens the protocol by relieving some pressure from other classification criteria.
Given two agents G and H, which can ask which points on G‘s timeline are in the causal past of which points of H‘s timeline. To answer this, consider the counterfactual in which G takes a random action (or sequence of actions) at some point (or interval) on G‘s timeline, and measure the mutual information between this action(s) and H‘s observations at some interval on H’s timeline.
Using this, we can effectively construct a future “causal cone” emanating from the AI’s origin, and also a past causal cone emanating from some time t on the AI’s timeline. Then, “nearby” agents will meet the intersection of these cones for low values of t whereas “faraway” agents will only meet it for high values of t or not at all. To first approximation, the user would be the “nearest” precursor[1] agent i.e. the one meeting the intersection for the minimal t.
More precisely, we expect the user’s observations to have nearly maximal mutual information with the AI’s actions: the user can e.g. see every symbol the AI outputs to the display. However, the other direction is less clear: can the AI’s sensors measure every nerve signal emanating from the user’s brain? To address this, we can fix t to a value s.t. we expect only the user the meet the intersection of cones, and have the AI select the agent which meets this intersection for the highest mutual information threshold.
This probably does not make the detection of malign agents redundant, since AFAICT a malign simulation hypothesis might be somehow cleverly arranged to make a malign agent the user.
More on Counterfactuals
In the parent post I suggested “instead of examining only Θ we also examine coarsenings of Θ which are not much more complex to describe”. A possible elegant way to implement this:
Consider the entire portion ¯Θ of our (simplicity) prior which consists of coarsenings of Θ.
Apply the counterfactual to ¯Θ.
Renormalize the result from HUC to HUD.
We still need precursor detection, otherwise the AI can create some new agent and make it the nominal “user”.
Causality in IBP
There seems to be an even more elegant way to define causal relationships between agents, or more generally between programs. Starting from a hypothesis Θ∈□(Γ×Φ), for Γ=ΣR, we consider its bridge transform B∈□(Γ×2Γ×Φ). Given some subset of programs Q⊆R we can define Δ:=ΣQ then project B to BΔ∈□(Γ×2Δ)[1]. We can then take bridge transform again to get some C∈□(Γ×2Γ×2Δ). The 2Γ factor now tells us which programs causally affect the manifestation of programs in Q. Notice that by Proposition 2.8 in the IBP article, when Q=R we just get all programs that are running, which makes sense.
Agreement Rules Out Mesa-Optimization
The version of PreDCA without any explicit malign hypothesis filtering might be immune to malign hypotheses, and here is why. It seems plausible that IBP admits an agreement theorem (analogous to Aumann’s) which informally amounts to the following: Given two agents Alice and Bobcat that (i) share the same physical universe, (ii) have a sufficiently tight causal relationship (each can see what the other sees), (iii) have unprivileged locations inside the physical universe, (iv) start from similar/compatible priors and (v) [maybe needed?] similar utility functions, they converge to similar/compatible beliefs, regardless of the complexity of translation between their subjective viewpoints. This is plausible because (i) as opposed to the cartesian framework, different bridge rules don’t lead to different probabilities and (ii) if Bobcat considers a simulation hypothesis plausible, and the simulation is sufficiently detailed to fool it indefinitely, then the simulation contains a detailed simulation of Alice and hence Alice must also consider this to be plausible hypothesis.
If the agreement conjecture is true, then the AI will converge to hypotheses that all contain the user, in a causal relationship with the AI that affirms them as the user. Moreover, those hypotheses will be compatible with the user’s own posterior (i.e. the differences can be attributed the AIs superior reasoning). Therefore, the AI will act on the user’s behalf, leaving no room for mesa-optimizers. Any would-be mesa-optimizer has to take the shape of a hypothesis that the user should also believe, within which the pointer-to-values still points to the right place.
Two nuances:
Maybe in practice there’s still room for simulation hypotheses of the AI which contain coarse-grained simulations of the user. In this case, the user detection algorithm might need to allow for coarsely simulated agents.
If the agreement theorem needs condition v, we get a self-referential loop: if the AI and the user converge to the same utility function, the theorem guarantees them to converge to the same utility function, but otherwise it doesn’t. This might make the entire thing a useless tautology, or there might be a way to favorably resolve the self-reference, vaguely analogously to how Loeb’s theorem allows resolving the self-reference in prisoner dilemma games between FairBots.
There are actually two ways to do this, corresponding to the two natural mappings Γ×2Γ→Γ×2Δ. The first is just projecting the subset of Γ to a subset of Δ, the second is analogous to what’s used in Proposition 2.16 of the IBP article. I’m not entirely sure what’s correct here.
Hi Vanessa! Thanks again for your previous answers. I’ve got one further concern.
Are all mesa-optimizers really only acausal attackers?
I think mesa-optimizers don’t need to be purely contained in a hypothesis (rendering them acausal attackers), but can be made up of a part of the hypotheses-updating procedures (maybe this is obvious and you already considered it).
Of course, since the only way to change the AGI’s actions is by changing its hypotheses, even these mesa-optimizers will have to alter hypothesis selection. But their whole running program doesn’t need to be captured inside any hypothesis (which would be easier for classifying acausal attackers away).
That is, if we don’t think about how the AGI updates its hypotheses, and just consider them magically updating (without any intermediate computations), then of course, the only mesa-optimizers will be inside hypotheses. If we actually think about these computations and consider a brute-force search over all hypotheses, then again they will only be found inside hypotheses, since the search algorithm itself is too simple and provides no further room for storing a subagent (even if the mesa-optimizer somehow takes advantage of the details of the search). But if more realistically our AGI employs more complex heuristics to ever-better approximate optimal hypotheses update, mesa-optimizers can be partially or completely encoded in those (put another way, those non-optimal methods can fail / be exploited). This failure could be seen as a capabilities failure (in the trivial sense that it failed to correctly approximate perfect search), but I think it’s better understood as an alignment failure.
The way I see PreDCA (and this might be where I’m wrong) is as an “outer top-level protocol” which we can fit around any superintelligence of arbitrary architecture. That is, the superintelligence will only have to carry out the hypotheses update (plus some trivial calculations over hypotheses to find the best action), and given it does that correctly, since the outer objective we’ve provided is clearly aligned, we’re safe. That is, PreDCA is an outer objective that solves outer alignment. But we still need to ensure the hypotheses update is carried out correctly (and that’s everything our AGI is really doing).
I don’t think this realization rules out your Agreement solution, since if truly no hypothesis can steer the resulting actions in undesirable ways (maybe because every hypothesis with a user has the human as the user), then obviously not even optimizers in hypothesis update can find malign hypotheses (although they can still causally attack hacking the computer they’re running on etc.). But I think your Agreement solution doesn’t completely rule out any undesirable hypothesis, but only makes it harder for an acausal attacker to have the user not be the human. And in this situation, an optimizer in hypothesis update could still select for malign hypotheses in which the human is subtly incorrectly modelled in such a precise way that has relevant consequences for the actions chosen. This can again be seen as a capabilities failure (not modelling the human well enough), but it will always be present to some degree, and it could be exploited by mesa-optimizers.
There’s a class of AI risk mitigation strategies which relies on the users to perform the pivotal act using tools created by AI (e.g. nanosystems). These strategies are especially appealing if we want to avoid human models. Here is a concrete alignment protocol for these strategies, closely related to AQD, which we call autocalibrating quantilized RL (AQRL).
First, suppose that we are able formulate the task as episodic RL with a formally specified reward function. The reward function is necessarily only a proxy for our true goal, since it doesn’t contain terms such as “oh btw don’t kill people while you’re building the nanosystem”. However, suppose the task is s.t. accomplishing it in the intended way (without Goodharting or causing catastrophic side effects) is easier than performing any attack. We will call this the “relative difficulty assumption” (RDA). Then, there exists a value for the quantilization parameter s.t. quantilized RL performs the task in the intended way.
We might not know how to set the quantilization parameter on our own, but we can define a performance goal for the task (in terms of expected total reward) s.t. the RDA holds. This leads to algorithms which gradually tune the quantilization parameter until the performance goal is met, while maintaining a proper balance between safety and sample complexity. Here it is important to keep track of epistemic vs. aleatoric uncertainty: the performance goal is the expectation of total reward relatively to aleatoric uncertainty (i.e. the stochasticity of a given hypothesis), whereas the safety goal is a bound on the expected cost of overshooting the optimal quantilization parameter relatively to both aleatoric and epistemic uncertainty (i.e. uncertainty between different hypotheses). This secures the system against malign hypotheses that are trying to cause an overshoot.
Notice the hardening the system (i.e. making it more secure using conventional methods) increases the likelihood of the RDA. That is, although it is infeasible to manually harden the system against superintelligent attacks, it can very well be feasible to harden it against attacks available at the minimal level of optimization power (= quantilization parameter) required to meet the performance goal, at least to such an extent that the intended solution becomes more appealing for the AI.
AQRL involves the risk that the RDA is false, even though it seems true to the designers. To mitigate this risk, we can deploy honeypots. That is, we add auxiliary goals to our reward function s.t. (i) if the goal is reached, maximal total reward is awarded (ii) the goal is harmless in itself (iii) reaching the goal is still harder than the intended solution, but by a smaller margin than performing attacks (iv) we can detect the event of reaching the goal. For example, suppose that the AI is not supposed to have Internet access, so gaining Internet access is a path to a variety of possible attacks. Then, we can add an auxiliary goal of the form “send such-and-such innocuous packet to such-and-such IP address”. If the RDA is false and the AI succeeds gaining Internet access earlier (on the quantilization parameter scale) than reaching the performance goal, once the AI sent the packet it will randomize all its remaining actions (since it is operating at the earliest quantilization parameter which allows this). The users will then be alerted that security was breached and rethink their next steps.
Now, suppose that we can only specify the reward function via concepts that have to be learned from examples. This opens the risk of generalization failure. Such risk is not necessarily a problem since we are still protected by quantilization. However, it needs to be factored into our safety bounds. An especially concerning possibility is that the AI will interpret the concepts as “would be labeled by humans as such-and-such” instead of the intended interpretation, creating incentives to deceive. Moreover, this encourages learning human models which puts more pressure on the RDA by making it easier to attack.
In order to avoid learning human models, we can use methods along these lines. Specifically, suppose that, in addition to the dataset for learning out task-relevant concepts, we supply a dataset designed specifically for learning human models. We can then design an algorithm that produces models of the task-relevant concepts s.t. the human-centric dataset cannot be compressed w.r.t. them. That is, the algorithm makes sure that access to the models it produces doesn’t help with learning the human-centric dataset, implying that the concept models don’t contain human models. Moreover, we can extend this approach to the entire process, s.t. human models are not learned from any other source of information as well.
Notice that although this method excludes human models from the actual RL process, it encourages them in intermediate computations, since we are actively supplying a human-centric dataset to the system. But, this is not dangerous if the intermediate computation is hardened against non-Cartesian daemons (a big “if” but one we need to deal with anyway).
Much of the orthodox LessWrongian approach to rationality (as it is expounded in Yudkowsky’s Sequences and onwards) is grounded in Bayesian probability theory. However, I now realize that pure Bayesianism is wrong, instead the right thing is quasi-Bayesianism. This leads me to ask, what are the implications of quasi-Bayesianism on human rationality? What are the right replacements for (the Bayesian approach to) bets, calibration, proper scoring rules et cetera? Does quasi-Bayesianism clarify important confusing issues in regular Bayesianism such as the proper use of inside and outside view? Is there rigorous justification to the intuition that we should have more Knightian uncertainty about questions with less empirical evidence? Does any of it influence various effective altruism calculations in surprising ways? What common LessWrongian wisdom does it undermine, if any?
Master post for ideas about infra-Bayesianism.
In the anthropic trilemma, Yudkowsky writes about the thorny problem of understanding subjective probability in a setting where copying and modifying minds is possible. Here, I will argue that infra-Bayesianism (IB) leads to the solution.
Consider a population of robots, each of which in a regular RL agent. The environment produces the observations of the robots, but can also make copies or delete portions of their memories. If we consider a random robot sampled from the population, the history they observed will be biased compared to the “physical” baseline. Indeed, suppose that a particular observation c has the property that every time a robot makes it, 10 copies of them are created in the next moment. Then, a random robot will have c much more often in their history than the physical frequency with which c is encountered, due to the resulting “selection bias”. We call this setting “anthropic RL” (ARL).
The original motivation for IB was non-realizability. But, in ARL, Bayesianism runs into issues even when the environment is realizable from the “physical” perspective. For example, we can consider an “anthropic MDP” (AMDP). An AMDP has finite sets of actions (A) and states (S), and a transition kernel T:A×S→Δ(S∗). The output is a string of states instead of a single state, because many copies of the agent might be instantiated on the next round, each with their own state. In general, there will be no single Bayesian hypothesis that captures the distribution over histories that the average robot sees at any given moment of time (at any given moment of time we sample a robot out of the population and look at their history). This is because the distributions at different moments of time are mutually inconsistent.
[EDIT: Actually, given that we don’t care about the order of robots, the signature of the transition kernel should be T:A×S→ΔNS]
The consistency that is violated is exactly the causality property of environments. Luckily, we know how to deal with acausality: using the IB causal-acausal correspondence! The result can be described as follows: Murphy chooses a time moment n∈N and guesses the robot policy π until time n. Then, a simulation of the dynamics of (π,T) is performed until time n, and a single history is sampled from the resulting population. Finally, the observations of the chosen history unfold in reality. If the agent chooses an action different from what is prescribed, Nirvana results. Nirvana also happens after time n (we assume Nirvana reward 1 rather than ∞).
This IB hypothesis is consistent with what the average robot sees at any given moment of time. Therefore, the average robot will learn this hypothesis (assuming learnability). This means that for n≫11−γ≫0, the population of robots at time n has expected average utility with a lower bound close to the optimum for this hypothesis. I think that for an AMDP this should equal the optimum expected average utility you can possibly get, but it would be interesting to verify.
Curiously, the same conclusions should hold if we do a weighted average over the population, with any fixed method of weighting. Therefore, the posterior of the average robot behaves adaptively depending on which sense of “average” you use. So, your epistemology doesn’t have to fix a particular method of counting minds. Instead different counting methods are just different “frames of reference” through which to look, and you can be simultaneously rational in all of them.
Could you expand a little on why you say that no Bayesian hypothesis captures the distribution over robot-histories at different times? It seems like you can unroll an AMDP into a “memory MDP” that puts memory information of the robot into the state, thus allowing Bayesian calculation of the distribution over states in the memory MDP to capture history information in the AMDP.
I’m not sure what do you mean by that “unrolling”. Can you write a mathematical definition?
Let’s consider a simple example. There are two states: s0 and s1. There is just one action so we can ignore it.s0 is the initial state. An s0 robot transition into an s1 robot. An s1 robot transitions into an s0 robot and an s1 robot. How will our population look like?
0th step: all robots remember s0
1st step: all robots remember s0s1
2nd step: 1⁄2 of robots remember s0s1s0 and 1⁄2 of robots remember s0s1s1
3rd step: 1⁄3 of robots remembers s0s1s0s1, 1⁄3 of robots remember s0s1s1s0 and 1⁄3 of robots remember s0s1s1s1
There is no Bayesian hypothesis a robot can have that gives correct predictions both for step 2 and step 3. Indeed, to be consistent with step 2 we must have Pr[s0s1s0]=12 and Pr[s0s1s1]=12. But, to be consistent with step 3 we must have Pr[s0s1s0]=13, Pr[s0s1s1]=23.
In other words, there is no Bayesian hypothesis s.t. we can guarantee that a randomly sampled robot on a sufficiently late time step will have learned this hypothesis with high probability. The apparent transition probabilities keep shifting s.t. it might always continue to seem that the world is complicated enough to prevent our robot from having learned it already.
Or, at least it’s not obvious there is such a hypothesis. In this example, Pr[s0s1s1]Pr[s0s1s0] will converge to the golden ratio at late steps. But, do all probabilities converge fast enough for learning to happen, in general? I don’t know, maybe for finite state spaces it can work. Would definitely be interesting to check.
[EDIT: actually, in this example there is such a hypothesis but in general there isn’t, see below]