A delayed hot take. This is pretty similar to previous comments from Rohin.
Shard theory alignment requires magic - not in the sense of magic spells, but in the technical sense of steps we need to remind ourselves we don't know how to do. Locating magic is an important step in trying to demystify it.
"Shard theory alignment" means building an AI that does good things and not bad things by encouraging an RL agent to want to do good things, via kinds of reward shaping analogous to the diamond maximizer example.
How might the story go?
- You start out with some unsupervised model of sensory data.
- On top of its representation of the world you start training an RL agent, with a carefully chosen curriculum and a reward signal that you think matches "goodness in general" on that curriculum distribution.
- This cultivates shards that want things in the vicinity of "what's good according to human values."
- These start out as mere bundles of heuristics, but eventually they generalize far enough to be self-reflective, promoting goal-directed behavior that takes into account the training process and the possibility of self-modification.
- At this point the values will lock themselves in, and future behavior will be guided by the abstractions in the learned representation of the world that the shards used to get good results in training, not by what would actually maximize the reward function you used.
There magic here is especially concentrated around how we end up with the right shards.
One magical process is how we pick the training curriculum and reward signal. If the curriculum is only made up only of simple environments, then the RL agent will learn heuristics that don't need to refer to humans. But if you push the complexity up too fast, the RL process will fail, or the AI will be more likely to learn heuristics that are better than nothing but aren't what we intended. Does a goldilocks zone where the agent learns more-or-less what we intended exist? How can we build confidence that it does, and that we've found it?
And what's in the curriculum matters a lot. Do we try to teach the AI to locate "human values" by having it be prosocial towards individuals? Which ones? To groups? Over what timescale? How do we reward it for choices on various ethical dilemmas? Or do we artificially suppress the rate of occurrence of such dilemmas? Different choices will lead to different shards. We wouldn't need to find a unique best way to do things (that's a boondoggle), but we would need to find some way of doing things that we trust enough.
Another piece of magic is how the above process lines up with generalization and self-reflectivity. If the RL agent becomes self-reflective too early, it will lock in simple goals that we don't want. If it becomes self-reflective too late, it will have started exploiting unintended maxima of the reward function. How do we know when we want the AI to lock in its values? How do we exert control over that?
If shard theory alignment seemed like it has few free parameters, and doesn't need a lot more work, then I think you failed to see the magic. I think the free parameters haven't been discussed enough precisely because they need so much more work.
The part of the magic that I think we could start working on now is how to connect curricula and learned abstractions. In order to predict that a certain curriculum will cause an AI to learn what we think is good, we want to have a science of reinforcement learning advanced in both theory and data. In environments of moderate complexity (e.g. Atari, MuJoCo), we can study how to build curricula that impart different generalization behaviors, and try to make predictive models of this process. Even if shard theory alignment doesn't pan out, this sounds like good blue-sky research.
The part of the magic I think we're not ready for is self-reflectivity. Surely there's an in-principle solution to lining up the timings of desired shard formation and value lock-in, but there doesn't have to be a way for us to learn this solution in a timely manner. At best we need more abstract thought about this issue in order to figure out what an approach might even look like, and at worst I think this is a problem the necessitates a different approach.
The question of "which values?" is in a similar spot. In the story I gave above of shard theory alignment, we directly train an RL agent to learn values from some curriculum. But that's not necessarily the only solution. Yes, maybe we could build a curriculum for some good-enough interpretation of human values, informed by a future advanced science of RL. But we could also train for some value-finding process at a higher meta-level, for example. I think to a large extent, we're not ready for this question because we don't know what's easier to do with future science of RL - it's counting our chickens before they're hatched. But also, this is another reason to keep your eyes open for other approaches.
I think this title suggests a motte/bailey, and also seems clickbait-y. I think most people scanning the title will conclude you mean it in a perjorative sense, such that shard theory requires impossibilities or unphysical miracles to actually work. I think this is clearly wrong (and I imagine you to agree). As such, I've downvoted for the moment.
AFAICT your message would be better conveyed by "Shard theory alignment has many free parameters."
The things you say about known open questions mostly seem like things I historically have said (which is not at all to say that you haven't brought any new content to the table!). For example,
You might still maintain there should be more discussion. Yeah, sure. [EDIT: clipped a part which I think isn't responding to what you meant to communicate]
I think it's quite plausible that you don't need much more work for shard theory alignment, because value formation really is that easy / robust.
Not obvious to me that there is something like a one-dimensional "goldilocks" zone, buttressed by bad zones. An interesting framing, but not one that feels definitive (if you meant it that way).
At best, shard theory alignment just works as-is, with some thought being taken like in the diamond alignment post.
I'll have to eat the downvote for now - I think it's worth it to use magic as a term of art, since it's 11 fewer words than "stuff we need to remind ourselves we don't know how to do," and I'm not satisfied with "free parameters."
But how do we learn that fact?
If extremely-confident-you says "the diamond-alignment post would literally work" and I say "what about these magical steps where you make choices without knowing how to build confidence in them beforehand" and extremely-confident-you says "don't worry, most choices work fine because value formation is robust," how did they learn that value formation is robust in that sense?
I think it is unlikely but plausible that shard theory alignment could turn out to be easy, if only we had the textbook from the future. But I don't think it's plausible that getting that textbook is easy. Yes, we have arguments about human values that are suggestive, but I don't see a way to go from "suggestive" to "I am actually confident" that doesn't involve de-mystifying the magic.
11 fewer words, but I don't think it communicates the intended concept!
If you have to say "I don't mean one obvious reading of the title" as the first sentence, it's probably not a good title. This isn't a dig -- titling posts is hard, and I think it's fair to not be satisfied with the one I gave. I asked ChatGPT to generate several new titles; lightly edited:
After considering these, I think that "Reminder: shard theory leaves open important uncertainties" is better than these five, and far better than the current title. I think a better title is quite within reach.
I didn't claim that I assign high credence to alignment just working out, I'm saying that it may as a matter of fact turn out that shard theory doesn't "need a lot more work," because alignment works out as a matter of fact from the obvious setups people try.
"Shard theory doesn't need more work" (in sense 2) could be true as a matter of fact, without me knowing it's true with high confidence. If you're saying "for us to become highly confident that alignment is going to work this way, we need more info", I agree.
But I read you as saying "for this to work as a matter of fact, we need X Y Z additional research":
And I think this is wrong. 2 can just be true, and we won't justifiably know it. So I usually say "It is not known to me that I know how to solve alignment", and not "I don't know how to solve alignment."
Does that make sense?
Since it was evidently A Thing, I have caved to peer pressure :P
Yeah, this is a good point. I do indeed think that just plowing ahead wouldn't work as a matter of fact, even if shard theory alignment is easy-in-the-way-I-think-is-plausible, and I was vague about this.
This is because the way in which I think it's plausible for it to be easy is some case (3) that's even more restricted than (1) or (2). Like 3: If we could read the textbook from the future and use its ontology, maybe it would be easy / robust to build an RL agent that's aligned because of the shard theory alignment story.
To back up: in nontrivial cases, robustness doesn't exist in a vacuum - you have to be robust to some distribution of perturbations. For shard theory alignment to be easy, it hast to be robust to the choices we have to make about building AI, and specifically to the space of different ways we might make those choices. This space of different ways we could make choices depends on the ontology we're using to think about the problem - a good ontology / way of thinking about the problem makes the right degrees of freedom "obvious," and makes it hard to do things totally wrong.
I think in real life, if we think "maybe this doesn't need more work and just we don't know it yet," what's actually going to happen is that for some of the degrees of freedom we need to set, we're going to be using an ontology that allows for perturbations where the thing's not robust, depressing the chances of success exponentially.