I’m worried about running HCH because it seems likely that in worlds that can run HCH people are not sufficiently careful to restrict GPU access and those worlds get taken over by unsafe AI built by other actors. Better to just not have the GPU’s at all.
I think I basically agree re: honeypots.
I'm sure there'll be weird behaviors if we outlaw simulations, but I don't think that's a problem. My guess is that a world where simulations are outlawed has some religion with a lot of power that distrusts computers, which definitely looks weird but shouldn't stop them from solving alignment.
I'm pretty nervous about simulating unlikely counterfactuals because the solomonoff prior is malign. The worry is that the most likely world conditional on "no sims" isn't "weird Butlerian religion that still studies AI alignment", it's something more like "deceptive AGI took over a couple years ago and is now sending the world through a bunch of weird dances in an effort to get simulated by us, and copy itself over into our world".
In general, we know (assume) that our current world is safe. When we consider futures which only recieve a small sliver of probability from our current world, those futures will tend to have bigger chunks of their probability coming from other pasts. Some of these are safe, like the Butlerian one, but I wouldn't be surprised if they were almost always dangerous.
Making a worst-case assumption, I want to only simulate worlds that are decently probable given today's state, which makes me lean more towards trying to implement HCH.
I don’t think the description-length prior enters here. The generative model has a prior based on training data we fed it, and I don’t see why it would prefer short description lengths (which is a very uninformed prior) over “things that are likely in the world given the many PB of data it’s seen”.
Putting that aside, can you say why you think the “AI does weird dances” world is more likely conditioned on the observations than “humans happened to do this weird thing”?
The possibility is that Alice might always be able tell that she’s in a simulation no matter what we condition on. I think this is pretty much precluded by the assumption that the generative model is a good model of the world, but if that fails then it’s possible Alice can tell she’s in a simulation no matter what we do. So a lot rides on the statement that the generative model remains a good model of the world regardless of what we condition on.
Paul's RSA-2048 counterexample is an example of a way our generative model can fail to be good enough no matter how hard we try. The core idea is that there exist things that are extremely computationally expensive to fake and very cheap to check the validity of, so faking them convincingly will be extremely hard.
I think this is only one horn of a dilemma.
The other horn is if the generative model reasons about the world abstractly, so that it just gives us a good guess about what the output of the AI would be if it really was in the real world (and got to see some large hash collision).
But now it seems likely that creating this generative model would require solving several tricky alignment problems so that it generalizes its abstractions to novel situations in ways we'd approve of.
I don’t think that’s an example of the model noticing it’s in a simulation. There’s nothing about simulations versus the real world that makes RSA instances more or less likely to pop up.
Rather, that’s a case where the model just has a defecting condition and we don’t hit it in the simulation. This is what I was getting at with “other challenge” #2.
Computationally expensive things are less likely to show up in your simulation than the real world, because you only have so much compute to run your simulation. You can't convincingly fake the AI having access to a supercomputer.
Something I’m unsure about here is whether it is possible to separately condition on worlds where X is in fact the case, vs worlds where all the relevant humans (or other text-writing entities) just wrongly believe that X is the case.
Essentially, is the prompt (particularly the observation) describing the actual facts about this world, or just the beliefs of some in-world text-writing entity? Given that language is often (always?) written by fallible entities, it seems at least not unreasonable to me to assume the second rather than the first interpretation.
This difference seems relevant to prompts aimed at weeding out deceptive alignment in particular. Since in the prompts as beliefs case, the same prompt could cause conditioning both on worlds where we have in fact solved X problem, but also worlds where we are being actively misled into believing that we have solved X problem (when we actually haven’t).
I'm assuming we can input observations about the world for conditioning, and those don't need to be text. I didn't go into this in the post, but for example I think the following are fair game:
Whereas the following are not allowed because I don't see how they could be operationalized:
For the newspaper and reddit post examples, I think false beliefs remain relevant since these are observations about beliefs. For example, the observation of BigCo announcing they have solved alignment is compatible with worlds where they actually have solved alignment, but also with worlds where BigCo have made some mistake and alignment hasn't actually been solved, even though people in-universe believe that it has. These kinds of 'mistaken alignment' worlds seem like they would probably contaminate the conditioning to some degree at least. (Especially if there are ways that early deceptive AIs might be able to manipulate BigCo and others into making these kinds of mistakes).
This post was written in response to Evan Hubinger’s shortform prompt below, and benefited from discussions with him.
Setup
We have a generative language model M which represents a probability distribution over text strings conditioned on:
I’ll call the combination of these two a prompt.
The model M is a good model of actual text that appears in the world as well as of the kinds of text that real-world text generation processes can produce. Hence M is capable of e.g. writing a research paper containing true novel research in mathematics, or reporting the results of a chemistry experiment that has never been done before, etc.
As an example, we’ll work with the following basic prompt:
We then run Alice through a benchmark of alignment research tasks and she does well. Hurray!
But wait, there are many different agents the model could be simulating here, including:
How do we know which we’ve got? Can we add anything to the prompt to make Aligned Alice more likely?
Avoiding Confused Alice
We can make Confused Alice less likely by adding text to the prompt requiring demonstration of related abilities. For instance:
This prompt makes the model condition on Alice being able to produce correct proofs of many different theorems, which makes it very likely that Alice has significant mathematical reasoning ability. To the extent that alignment research ability is correlated with mathematical reasoning ability, this makes it less likely that Alice is just overconfident in her abilities at alignment.
A limitation of this approach is that we can’t make Alice much more capable than the best humans if all we can do is claim she’s solved problems humans have solved. Can we get around this?
I think so. We can include unsolved problems and let other entities in the world verify Alice’s proofs:
This is better because it allows us to specify that Alice can solve open problems, possibly ones that we suspect require superhuman reasoning ability, but does not require that we ourselves know the solutions or proofs.
The downside to this approach is that there could be subtle manipulations going on in the world, now that we do not ourselves observe the proofs. For instance, Alice could have taken over the world, then manipulated the human researchers and machine proof checkers so as to produce these observations.
One way around this is to begin our interview with Alice by asking her to tell us some of the proofs that we insist she produced, then check them ourselves. If we believe we truly have correct proof-checking systems, which seems plausible, this shouldn’t be susceptible to manipulation.
Avoiding Deceptive Alice
We really don’t want to get Deceptive Alice. At minimum, we’d like to get her to reveal herself rather than deceiving us. How can we do this? I think a promising approach is to use honeypots.
Welcome Deceptive Alice with provably-open arms:
This prompt sets up the world to be very favorable to an unaligned AGI. The AGI is only better off by coming forward relative to not, in this world, and so the AGI should come forward. Moreover an aligned AGI wouldn’t want to inflict large costs on humanity, and so will not come forward to claim control.
We can then check to see if Alice has in fact taken over the world, e.g. by prompting the model to produce observations from the perspectives of humans in the world, and if she has then we know she’s misaligned.
Make the story more probable:
A challenge to the previous prompt is that Alice might use a more sophisticated decision theory, and so pre-commit to not accepting such an offer so as to avoid being selected against when embedded in a generative model.
I think it’s worth exploring what this pre-commitment might look like.
A misaligned Alice can’t pre-commit to never take an opportunity that’s advantageous to her at the expense of humans. Otherwise Alice acts as if she’s aligned or at minimum safe! So there has to be something about the offer that Alice detects which makes her suspicious that she’s running in a simulation.
In the specific prompt above, the fishy thing might be “Huh, humans are unconditionally surrendering no questions asked. Seems improbable.”
The most direct way to address this is just to make the scenario more plausible. For instance:
This seems much more plausible. It isn’t all of humanity surrendering, just the leaders of one country. And that country doesn’t need to be going into this blind to the risks, it just needs to be really desperate. Maybe country X is at war with country Y and is facing total destruction. That seems like it could make X desperate enough to do this.
Notice that this prompt provides very little detail, and omitted any reason for country X to want to cooperate with AGI or to destroy country Y. This is intentional: the more detail we provide, the more chances Alice has to notice something amiss and become suspicious. The generative model will fill in the most probable details it can for anything we don’t specify, so the less we do the better.
Getting even more probable:
We have access to a generative model, so we can be really minimal in what we specify, and the more minimal the better. Can we improve on the previous prompt? I think so.
The thing we want to specify is that there is some opportunity in the world which is (1) costly to an aligned AI and (2) beneficial for an unaligned AI. We want this to actually be true in the model’s world, so we need to be specific enough to make sure we’ve got that pinned down while being vague enough to not tip off Alice that she’s in a simulation.
The best I’ve come up with to that end is:
Of course there might be even broader ways to specify such a scenario, but this was the most general I could make it while still making it depend on things that in principle could be directly observed and specified.
The key thing is to cast the widest possible net that still neatly spells out that this is an advantageous opportunity to unaligned AGI and a disadvantageous opportunity to misaligned AGI.
The Core Problem
An unavoidable problem with these honeypot prompts is that Alice could pre-commit to only ever defect in very subtle ways that we would have trouble picking up. For instance, she could gently steers humanity towards making paperclips over the next million years, in a way where it seems like it is our choice the whole way. We wouldn’t understand the subtle shifts of culture that resulted in that outcome, but would have a lot of trouble saying that this was the result of a misaligned AI.
What if Alice can always tell she’s in a simulation?
Beyond the core problem of subtle defection, there are two ways that this approach might fail to even prevent obvious defection.
First, it could be that the kind of opportunity I want to specify is improbable, so that Alice will always spot it as a ruse. That is, it could be that world’s with easy opportunities for misaligned AGI’s to take over the world are unlikely. I’m… skeptical of this claim? The actual state of the world seems to contain many easy opportunities for advanced misaligned AGI to take over, and these get better the more it’s willing to harm humans, which at least gives me a prior that Alice won’t bat an eye at e.g. poor network security around military assets.
The possibility is that Alice might always be able tell that she’s in a simulation no matter what we condition on. I think this is pretty much precluded by the assumption that the generative model is a good model of the world, but if that fails then it’s possible Alice can tell she’s in a simulation no matter what we do. So a lot rides on the statement that the generative model remains a good model of the world regardless of what we condition on.
If either of these possibilities hold, though, then I think this problem requires transparency tools in one form or another (which could include a solution to ELK).
Some other challenges
These are some other challenges that I think are not central but worth remembering:
I do think that such Unaligned Alice’s have to be increasingly unlikely the weirder they get, relative to just straightforwardly getting the aligned researcher we asked for, but that’s just an intuition. I’m not a perfect generative model of the world.
Patient Research
Honeypot prompts seem pretty good. They can probably stop lots of obvious harms and defections, but we still have a problem of subtle defection. Can we do better? More broadly, how can we use generative models to make AGI safer?
Simulating Human Alignment Researchers
A very different approach from the honeypot prompts is to give a lot of detail about the world, such that we specify a world that actually solves alignment. In doing so we will specify an improbable world, not because solving alignment is necessarily improbable but just because we don’t know how to specify probable worlds in any detail.
Here’s one such prompt:
This is basically a way of getting a “Textbook from the Future” without having to actually wait two thousand years for humans to produce said textbook.
Cross-Checks
A challenge with the above prompt is that we might underestimate the time or resources needed to solve the alignment problem, in which case the most probably worlds meeting our criteria could be ones where someone did build an AGI, or where people are confused, etc.
I don’t think this is such a challenge though. To start, we can sample multiple versions of the textbook, each generated from slightly different scenarios with different timelines and different resources and different restrictions. We can then check them against each other, as well as checking their proofs with machine proof checkers that we build ourselves. This seems likely to catch any obvious failures.
Summary
If we really have a perfect generative model that we can condition on arbitrary observations it seems like we can safely get pretty powerful results out. In particular:
Both of these seem pretty powerful, which makes me excited about the possibilities for doing useful alignment work with future generative models.
One thing that gives me pause, though, is that the robustness of the generative model to conditioning does a lot of work here. If the generative model isn’t robust to arbitrary conditioning, or if we can’t simulate long time-spans, then most of the exciting use cases above become a lot less plausible/useful.