I’d be interested in the relationship between this and Implicit Gradient Regularization and the sharp/flat minima lit.The basic idea there is to compare the continuous gradient flow on the original objective, to the path followed by SGD due to discretization. They show that the latter can be re-interpreted as optimizing a modified objective which favors flat minima (low sensitivity to parameter perturbations). This isn’t clearly the same as what you’re analyzing here, since you’re looking at variance due to sampling instead, but they might be related under appropriate regularity conditions.
Sam Smith also has this nice paper on sharp/flat minima which links a lot of previous observations together, and has some similarities to your approach here.
Haven’t thought about any of this too closely, so my apologies if these aren’t useful! Seems close enough that it might be of interest though.
Thanks for the great post. I found this collection of stories and framings very insightful.
1. Strong +1 to “Problems before solutions.” I’m much more focused when reading this story (or any threat model) on “do I find this story plausible and compelling?” (which is already a tremendously high bar) before even starting to get into “how would this update my research priorities?”
2. I wanted to add a mention to Katja Grace’s “Misalignment and Misuse” as another example discussing how single-single alignment problems and bargaining failures can blur together and exacerbate each other. The whole post is really short, but I’ll quote anyways:
In the post’s story, both “misalignment” and “misuse” seem like two different, both valid, frames on the problem.
3. I liked the way this point is phrased on agent-agnostic and agent-centric (single-single alignment-focused) approaches as complementary.
At one extreme end, in the world where we could agree on what constitutes an acceptable level of xrisk, and could agree to not build AI systems which exceed this level, and give ourselves enough time to figure out the alignment issues in advance, we’d be fine! (We would still need to do the work of actually figuring out a bunch of difficult technical and philosophical questions, but importantly, we would have the time and space to do this work.) To the extent we can’t do this, what are the RAAPs, such as intense competition, which prevent us from doing so?
And at the other extreme, if we develop really satisfying solutions to alignment, we also shouldn’t end up in worlds where we have “little human insight” or factories “so pervasive, well-defended, and intertwined with our basic needs that we are unable to stop them from operating.”
I think Paul often makes this point in the context of discussing an alignment tax. We can both decrease the size of the tax, and make the tax more appealing/more easily enforceable.
4. I expect to reconsider many concepts through the RAAPs lens in the next few months. Towards this end, it’d be great to see a more detailed description of what the RAAPs in these stories are. For example, a central example here is “the competitive pressure to produce.” We could also maybe think about “a systemic push towards more easily quantifiable metrics (e.g. profit vs. understanding or global well-being)” which WFLL1 talks about or “strong societal incentives for building powerful systems without correspondingly strong societal incentives for reflection on how to use them”. I’m currently thinking about all these RAAPs as a web (or maybe a DAG), where we can pull on any of these different levers to address the problem, as opposed to there being a single true RAAP; does that seem right to you?
Relatedly, I’d be very interested in a post investigating just a single RAAP (what is the cause of the RAAP? what empirical evidence shows the RAAP exists? how does the RAAP influence various threat models?). If you have a short version too, I think that’d help a lot in terms of clarifying how to think about RAAPs.
5. My one quibble is that there may be some criticism of the AGI safety community which seems undeserved. For example, when you write “That is, outside the EA / rationality / x-risk meme-bubbles, lots of AI researchers think about agent-agnostic processes,” it seems to imply that inside this community, researchers don’t think about RAAPs (though perhaps this is not what you meant!) It seems that many inside these circles think about agent-agnostic processes too! (Though not framed in these terms, and I expect this additional framing will be helpful.) Your section on “Successes in our agent-agnostic thinking” gives many such examples.
This is a quibble in the sense that, yes, I absolutely agree there is lots of room for much needed work on understanding and addressing RAAPs, that yes, we shouldn’t take the extreme physical and economic competitiveness of the world for granted, and yes, we should work to change these agent-agnostic forces for the better. I’d also agree this should ideally be a larger fraction of our “portfolio” on the margin (acknowledging pragmatic difficulties to getting here). But I also think the AI safety community has had important contributions on this front.