This is a post about my own confusions. It seems likely that other people have discussed these issues at length somewhere, and that I am not up with current thoughts on them, because I don’t keep good track of even everything great that everyone writes. I welcome anyone kindly directing me to the most relevant things, or if such things are sufficiently well thought through that people can at this point just correct me in a small number of sentences, I’d appreciate that even more.
~
The traditional argument for AI alignment being hard is that human value is ‘complex’ and‘fragile’. That is, it is hard to write down what kind of future we want, and if we get it even a little bit wrong, most futures that fit our description will be worthless.
The illustrationsI have seen of this involve a person trying to write a description of value conceptual analysis style, and failing to put in things like ‘boredom’ or ‘consciousness’, and so getting a universe that is highly repetitive, or unconscious.
I’m not yet convinced that this is world-destroyingly hard.
Firstly, it seems like you could do better than imagined in these hypotheticals:
These thoughts are from a while ago. If instead you used ML to learn what ‘human flourishing’ looked like in a bunch of scenarios, I expect you would get something much closer than if you try to specify it manually. Compare manually specifying what a face looks like, then generating examples from your description to using modern ML to learn it and generate them.
Even in the manually describing it case, if you had like a hundred people spend a hundred years writing a very detailed description of what went wrong, instead of a writer spending an hour imagining ways that a more ignorant person may mess up if they spent no time on it, I could imagine it actually being pretty close. I don’t have a good sense of how far away it is.
I agree that neither of these would likely get you to exactly human values.
But secondly, I’m not sure about the fragility argument: that if there is basically any distance between your description and what is truly good, you will lose everything.
This seems to be a) based on a few examples of discrepancies between written-down values and real values where the written down values entirely exclude something, and b) assuming that there is a fast takeoff so that the relevant AI has its values forever, and takes over the world.
My guess is that values that are got using ML but still somewhat off from human values are much closer in terms of not destroying all value of the universe, than ones that a person tries to write down. Like, the kinds of errors people have used to illustrate this problem (forget to put in, ‘consciousness is good’) are like forgetting to say faces have nostrils in trying to specify what a face is like, whereas a modern ML system’s imperfect impression of a face seems more likely to meet my standards for ‘very facelike’ (most of the time).
Perhaps a bigger thing for me though is the issue of whether an AI takes over the world suddenly. I agree that if that happens, lack of perfect alignment is a big problem, though not obviously an all value nullifying one (see above). But if it doesn’t abruptly take over the world, and merely becomes a large part of the world’s systems, with ongoing ability for us to modify it and modify its roles in things and make new AI systems, then the question seems to be how forcefully the non-alignment is pushing us away from good futures relative to how forcefully we can correct this. And in the longer run, how well we can correct it in a deep way before AI does come to be in control of most decisions. So something like the speed of correction vs. the speed of AI influence growing.
These are empirical questions about the scales of different effects, rather than questions about whether a thing is analytically perfect. And I haven’t seen much analysis of them. To my own quick judgment, it’s not obvious to me that they look bad.
For one thing, these dynamics are already in place: the world is full of agents and more basic optimizing processes that are not aligned with broad human values—most individuals to a small degree, some strange individuals to a large degree, corporations, competitions, the dynamics of political processes. It is also full of forces for aligning them individually and stopping the whole show from running off the rails: law, social pressures, adjustment processes for the implicit rules of both of these, individual crusades. The adjustment processes themselves are not necessarily perfectly aligned, they are just overall forces for redirecting toward alignment. And in fairness, this is already pretty alarming. It’s not obvious to me that imperfectly aligned AI is likely to be worse than the currently misaligned processes, and even that it won’t be a net boon for the side of alignment.
So then the largest remaining worry is that it will still gain power fast and correction processes will be slow enough that its somewhat misaligned values will be set in forever. But it isn’t obvious to me that by that point it isn’t sufficiently well aligned that we would recognize its future as a wondrous utopia, just not the very best wondrous utopia that we would have imagined if we had really carefully sat down and imagined utopias for thousands of years.This again seems like an empirical question of the scale of different effects, unless there is a an argument that some effect will be totally overwhelming.
I'm trying to make an empirical prediction about what happens. That's very different from what I can guarantee / verify. I'd argue the OP is also speaking in this frame.
That may be the crux. I'm generally of the mindset that "can't guarantee/verify" implies "completely useless for AI safety". Verifying that's it's safe is the whole point of AI safety research. If we were hoping to make something that just happened to be safe even though we couldn't guarantee it beforehand or double-check afterwards, that would just be called "AI".
I'm not saying we need proof-level guarantees for everything. Reasoning from strong enough priors would be ok, but saying "well, it seems like it'll probably be safe, but we can't actually verify our assumptions or reasoning" really doesn't cut it. Especially when we do not understand what the things-of-interest (values) even are, or how to formalize them.
I'm also predicting that vision-models-trained-with-richer-data will have approximately the same tree-concept as humans.
If we're saying that tree-concepts of vision-models-trained-with-richer-data will be similar to the human tree-concept according to humans, then I actually do agree with that. I do not expect it to generalize to values. (Although if we had a way to verify that the concepts match, I would expect the concept-match-verification method to generalize.) Here's a few different views on why I wouldn't expect it to generalize, which feel to me like they're all working around the edges of the same central idea:
In game/decision-theoretic terms, values depend on off-equilibrium behavior. They depend on counterfactual situations which will never actually happen.
In reductive terms, things in images can mostly be expressed as complicated clusters in atom-configuration space. Those clusters are directly relevant to predictive models, and they have predictive power. Values, and agency, aren't like that - we could model and predict the world just fine without assigning agency to any processes in it. (I suspect that a formalization of this distinction drops naturally out of a theory of abstraction, but that's still under construction.)
Humans can generally agree on what a tree is. Disagreements over values - or over what values even are - feel qualitatively different. From a human perspective, it feels like values and trees are defined in qualitatively different ways.
Again, if we had ways to guarantee/verify that a human and an ML system were using the same concepts, or had similar notions of "distance" and "approximation", then I do expect that would generalize from images to values. But I don't expect that methods which find human-similar concepts in images will also generally find human-similar concepts in values.
This is a post about my own confusions. It seems likely that other people have discussed these issues at length somewhere, and that I am not up with current thoughts on them, because I don’t keep good track of even everything great that everyone writes. I welcome anyone kindly directing me to the most relevant things, or if such things are sufficiently well thought through that people can at this point just correct me in a small number of sentences, I’d appreciate that even more.
~
The traditional argument for AI alignment being hard is that human value is ‘complex’ and ‘fragile’. That is, it is hard to write down what kind of future we want, and if we get it even a little bit wrong, most futures that fit our description will be worthless.
The illustrations I have seen of this involve a person trying to write a description of value conceptual analysis style, and failing to put in things like ‘boredom’ or ‘consciousness’, and so getting a universe that is highly repetitive, or unconscious.
I’m not yet convinced that this is world-destroyingly hard.
Firstly, it seems like you could do better than imagined in these hypotheticals:
I agree that neither of these would likely get you to exactly human values.
But secondly, I’m not sure about the fragility argument: that if there is basically any distance between your description and what is truly good, you will lose everything.
This seems to be a) based on a few examples of discrepancies between written-down values and real values where the written down values entirely exclude something, and b) assuming that there is a fast takeoff so that the relevant AI has its values forever, and takes over the world.
My guess is that values that are got using ML but still somewhat off from human values are much closer in terms of not destroying all value of the universe, than ones that a person tries to write down. Like, the kinds of errors people have used to illustrate this problem (forget to put in, ‘consciousness is good’) are like forgetting to say faces have nostrils in trying to specify what a face is like, whereas a modern ML system’s imperfect impression of a face seems more likely to meet my standards for ‘very facelike’ (most of the time).
Perhaps a bigger thing for me though is the issue of whether an AI takes over the world suddenly. I agree that if that happens, lack of perfect alignment is a big problem, though not obviously an all value nullifying one (see above). But if it doesn’t abruptly take over the world, and merely becomes a large part of the world’s systems, with ongoing ability for us to modify it and modify its roles in things and make new AI systems, then the question seems to be how forcefully the non-alignment is pushing us away from good futures relative to how forcefully we can correct this. And in the longer run, how well we can correct it in a deep way before AI does come to be in control of most decisions. So something like the speed of correction vs. the speed of AI influence growing.
These are empirical questions about the scales of different effects, rather than questions about whether a thing is analytically perfect. And I haven’t seen much analysis of them. To my own quick judgment, it’s not obvious to me that they look bad.
For one thing, these dynamics are already in place: the world is full of agents and more basic optimizing processes that are not aligned with broad human values—most individuals to a small degree, some strange individuals to a large degree, corporations, competitions, the dynamics of political processes. It is also full of forces for aligning them individually and stopping the whole show from running off the rails: law, social pressures, adjustment processes for the implicit rules of both of these, individual crusades. The adjustment processes themselves are not necessarily perfectly aligned, they are just overall forces for redirecting toward alignment. And in fairness, this is already pretty alarming. It’s not obvious to me that imperfectly aligned AI is likely to be worse than the currently misaligned processes, and even that it won’t be a net boon for the side of alignment.
So then the largest remaining worry is that it will still gain power fast and correction processes will be slow enough that its somewhat misaligned values will be set in forever. But it isn’t obvious to me that by that point it isn’t sufficiently well aligned that we would recognize its future as a wondrous utopia, just not the very best wondrous utopia that we would have imagined if we had really carefully sat down and imagined utopias for thousands of years. This again seems like an empirical question of the scale of different effects, unless there is a an argument that some effect will be totally overwhelming.