Prediction market for whether someone will strengthen our results or prove something about the nonindependent case:
Downvoted, this is very far from a well-structured argument, and doesn't give me intuitions I can trust either
I'm fairly sure you can get a result something like "it's not necessary to put positive probability mass on two different functions that can't be distinguished by observing only s bits", so some functions can get zero probability, e.g. the XOR of all combinations of at least s+1 bits.
edit: The proof is easy. Let , be two such indistinguishable functions that you place positive probability on, F be a random variable for the function, and F' be F but with all probability mass for replaced by . Then ....
I don't have enough time to reply in depth, but the factors in favor of weight vectors and activation vectors both seem really complicated, and the balance still seems in favor of activation vectors, though I have reasonably high uncertainty.
I think to solve alignment, we need to develop our toolbox of "getting AI systems to behave in ways we choose". Not in the sense of being friendly or producing economic value, but things that push towards whatever cognitive properties we need for a future alignment solution. We can make AI systems do some things we want e.g. GPT-4 can answer questions with only words starting with "Q", but we don't know how it does this in terms of internal representations of concepts. Current systems are not well-characterized enough that we can predict what they do far O...
This is the most impressive concrete achievement in alignment I've seen. I think this post reduces my p(doom) by around 1%, and I'm excited to see where all of the new directions uncovered lead.
Edit: I explain this view in a reply.
Edit 25 May: I now think RLHF is more impressive in terms of what we can get systems to do, but I still think activation editing has opened up more promising directions.
What other concrete achievements are you considering and ranking less impressive than this? E.g. I think there's a case for more alignment progress having come from RLHF, debate, some mechanistic interpretability, or adversarial training.
SGD has inductive biases, but we'd have to actually engineer them to get high rather than high when only trained on . In the Gao et al paper, optimization and overoptimization happened at the same relative rate in RL as in conditioning, so I think the null hypothesis is that training does about as well as conditioning. I'm pretty excited about work that improves on that paper to get higher gold reward while only having access to the proxy reward model.
I think the point still holds in mainline shard theory world, which in m...
That section is even more outdated now. There's nothing on interpretability, Paul's work now extends far beyond IDA, etc. In my opinion it should link to some other guide.
This seems good if it could be done. But the original proposal was just a call for labs to individually pause their research, which seems really unlikely to work.
Also, the level of civilizational competence required to compensate labs seems to be higher than for other solutions. I don't think it's a common regulatory practice to compensate existing labs like this, and it seems difficult to work out all the details so that labs will feel adequately compensated. Plus there might be labs that irrationally believe they're undervalued. Regulations similar to the nuclear or aviation industry feel like a more plausible way to get slowdown, and have the benefit that they actually incentivize safety work.
I'm worried that "pause all AI development" is like the "defund the police" of the alignment community. I'm not convinced it's net bad because I haven't been following governance-- my current guess is neutral-- but I do see these similarities:
I'm planning to write a post called "Heavy-tailed error implies hackable proxy". The idea is that when you care about and are optimizing for a proxy , Goodhart's Law sometimes implies that optimizing hard enough for causes to stop increasing.
A large part of the post would be proofs about what the distributions of and must be for , where X and V are independent random variables with mean zero. It's clear that
Suppose an agent has this altruistic empowerment objective, and the problem of getting an objective into the agent has been solved.
Wouldn't it be maximized by forcing the human in front of a box that encrypts its actions and uses the resulting stream to determine the fate of the universe? Then the human would be maximally "in control" of the universe but unlikely to create a universe that's good by human preferences.
I think this reflects two problems:
FWIW this was basically cached for me, and if I were better at writing and had explained this ~10 times before like I expect Eliezer has, I'd be able to do about as well. So would Nate Soares or Buck or Quintin Pope (just to pick people in 3 different areas of alignment), and Quintin would also have substantive disagreements.
not Nate or a military historian, but to me it seems pretty likely for a ~100 human-years more technologically advanced actor to get decisive strategic advantage over the world.
Why should we expect that True Names useful for research exist in general? It seems like there are reasons why they don't:
Were any cautious people trying empirical alignment research before Redwood/Conjecture?
Do you have thoughts on when there are two algorithms that aren’t “doing the same thing” that fall within the same loss basin?
It seems like there could be two substantially different algorithms which can be linearly interpolated between with no increase in loss. For example, the model is trained to classify fruit types and ripeness. One module finds the average color of a fruit (in an arbitrary basis), and another module uses this to calculate fruit type and ripeness. The basis in which color is expressed can be arbitrary, since the second module can compe...
The ultimate goal of John Wentworth’s sequence "Basic Foundations for Agent Models" is to prove a selection theorem of the form:
John has not yet proved su...
Note that the particular form of "nonexistence of a representative agent" John mentions is an original result that's not too difficult to show informally, but hasn't really been written down formally either here or in the economics literature.
Ryan Kidd and I did an economics literature review a few weeks ago for representative agent stuff, and couldn't find any results general enough to be meaningful. We did find one paper that proved a market's utility function couldn't be of a certain restricted form, but nothing about proving the lack of a coherent util...
Again analogizing from the definition in “Risks From Learned Optimization”, “corrigible alignment” would be developing a motivation along the lines of “whatever my subcortex is trying to reward me for, that is what I want!” Maybe the closest thing to that is hedonism? Well, I don’t think we want AGIs with that kind of corrigible alignment, for reasons discussed below.
At first this claim seemed kind of wild, but there's a version of it I agree with.
It seems like conditional on the inner optimizer being corrigible, in the sense of having a goal that's a poin...
I think a lot of commenters misunderstand this post, or think it's trying to do more than it is. TLDR of my take: it's conveying intuition, not suggesting we should model preferences with 2D vector spaces.
The risk-neutral measure in finance is one way that "rotations" between probability and utility can be made:
As far as I can tell, this is the entire point. I don't see this 2D vector space actually being used in modeling agents, and I don't think Abram does either.
I largely agree. In retrospect, a large part of the point of this post for me is that it's practical to think of decision-theoretic agents as having expected value estimates for everything without having a utility function anywhere, which the expected values are "expectations of".
A utility function is a gadget for turning probability distributions into expected values. This object makes sense in ...
I think we need to unpack "sufficiently aligned"; here's my attempt. There are A=2^10000 10000-bit strings. Maybe 2^1000 of them are coherent English text, and B=2^200 of these are alignment proposals that look promising to a human reviewer, and C=2^100 of them are actually correct and will result in aligned AI.The thesis of the post requires that we can make a "sufficiently aligned" AI that, conditional on a proposal looking promising, is likely to be actually correct.
I haven't heard this. What's the strongest criticism?
How do you think "agent" should be defined?