I consider this system to be superhuman, and the problem of aligning it to be "alignment-complete" in the sense that if you solve any of the problems in this class, you essentially solve alignment down the line and probably avoid x-risk,
I find this line of reasoning (and even mentioning it) not useful. Any alignment solution will be alignment complete so it’s tautological.
I think you’ve defined alignment as a hard problem, which no one will disagree with, but you also define any steps taken towards solving the alignment problem as alignment complete, and thus impossible unless they also seem infeasibly hard. Can there not be an iterative way to solve alignment? I think we can construct some trivial hypotheticals where we iteratively solve it.
For the sake of argument say I created a superhuman math theorem solver, something that can solve IMO problems written in lean with ease. I then use it to solve a lot of important math problems within alignment. This in turn affords us strong guarantees about certain elements of alignment or gradient descent. Can you convince me that the solution to getting a narrow AI useful for alignment is as hard as aligning a generally superhuman AI?
What if we reframe it to some real world example. The proof for the Riemann hypothesis begins with a handful of difficult but comparatively simple lemmas. Solving those lemmas is not as hard as solving the Reimann hypothesis. And we can keep decomposing this proof into parts that are simpler than the whole.
A step in a process being simpler than the end result of the process is not an argument against that step.
what happens if we automatically evaluate plans generated by superhuman AIs using current LLMs and then launch plans that our current LLMs look at and say, "this looks good".
The obvious failure mode is that LLM is not powerful enough to predict consequences of the plan. The obvious fix is to include human-relevant description of the consequences. The obvious failure modes: manipulated description of the consequences, optimizing for LLM jail-breaking. The obvious fix: ...
I won't continue, but shallow rebuttals is not that convincing, but deep ones is close to capability research, so I don't expect to find interesting answers.
I don't know anyone in the community who'd say it's a bad thing that leads to extinction if a CEV-aligned superintelligence grabs control.
Hi there. I am a member of the community, and I expect that any plan that looks like "some people build a system that they believe to be a CEV-aligned superintelligence and tell it to seize control" will end in a way that is worse and different than "utopia". If the "seize control" strategy is aggressive enough, "extinction" is one of the "worse and different" outcomes that is on the table, though I expect that the modal outcome is more like "something dumb doesn't work, and the plan fails before anything of particular note has even happened", and the 99th percentile large effect outcome is something like "your supposed CEV-aligned superintelligence breaks something important enough that people notice you were trying to seize control of the world, and then something dumb doesn't work".
Note that evolution has had "white-box" access to our architecture, optimising us for inclusive genetic fitness, and getting something that optimizes for similar collections of things.
Would you mind elaborating on what exactly you mean by the terms "white-box" and "optimizing for", in the above statement (and, particularly, whether you mean the same thing by your first usage of "optimizing" and your second usage).
I think the argument would be clearer if it distinguished between the following meanings of the term "optimizer":
So if we use those terms, the traditional IGF argument looks something like this:
Evolution optimized (selective shaping) humans to be reproductively successful, but despite that humans do not optimize (deliberative maximization) for inclusive genetic fitness.
Thanks for the comment!
any plan that looks like "some people build a system that they believe to be a CEV-aligned superintelligence and tell it to seize control"
People shouldn’t be doing anything like that; I’m saying that if there is actually a CEV-aligned superintelligence, then this is a good thing. Would you disagree?
what exactly you mean by the terms "white-box" and "optimizing for"
I agree with “Evolution optimized humans to be reproductively successful, but despite that humans do not optimize for inclusive genetic fitness”, and the point I was making was that the stuff that humans do optimize for is similar to the stuff other humans optimize for. Were you confused by what I said in the post or are you just suggesting a better wording?
People shouldn’t be doing anything like that; I’m saying that if there is actually a CEV-aligned superintelligence, then this is a good thing. Would you disagree?
I think an actual CEV-aligned superintelligence would probably be good, conditional on being possible, but also that I expect that anyone who thinks they have a plan to create one is almost certainly wrong about that and so plans of that nature are a bad idea in expectation, and much more so if that plan looks like "do a bunch of stuff that would be obviously terrible if not for the end goal in the name of optimizing the universe".
Were you confused by what I said in the post or are you just suggesting a better wording?
I was specifically unsure which meaning of "optimize for" you were referring to with each usage of the term.
To solve the problem of aligning superhuman systems, you need some amount of complicated human thought/hard high-level work. If a system can output that much hard high-level work in a short amount of time, I consider this system to be superhuman, and the problem of aligning it to be "alignment-complete" in the sense that if you solve any of the problems in this class, you essentially solve alignment down the line and probably avoid x-risk, but solving any of these problems requires a lot of hard human work, and safely automating so much the hard work is an alignment-complete problem.
There needs to be an argument for why one can successfully use a subhuman system to control a complicated superhuman system, as otherwise, having generations of controllable subhuman systems doesn't matter.
Thinking carefully about these things (rather than rehashing MIRI-styled arguments a bit carelessly) is actually important, because it can change the strategic (alignment-relevant) landscape; e.g. from Before smart AI, there will be many mediocre or specialized AIs:
Assuming that much of this happens “behind the scenes”, a human interacting with this system might just perceive it as a single super-smart AI. Nevertheless, I think this means that AI will be more alignable at a fixed level of productivity. (Eventually, we’ll face the full alignment problem — but “more alignable at a fixed level of productivity” helps if we can use that productivity for something useful, such as giving us more time or helping us with alignment research.)
Most obviously, the token-by-token output of a single AI system should be quite easy for humans to supervise and monitor for danger. It will rarely contain any implicit cognitive leaps that a human couldn’t have generated themselves. (C.f. visible thoughts project and translucent thoughts hypothesis.)
A specialised AI can speed up Infra-Bayesianism by the same amount random mathematicians can, by proving theorems and solving some math problems. A specialised AI can’t actually understand the goals of the research and contribute to the part that require the hardest kind of human thinking. There’s a requirement for some amount of problem-solving of the kind hardest human thinking produces to go into the problem. I claim that if a system can output enough of that kind of thinking to meaningfully contribute, then it’s going to be smart enough to be dangerous. I further claim that there’s a number of hours of complicated-human-thought such that making a safe system that can output work corresponding to that number in less than, e.g., 20 years, requires at least that number of hours of complicated human thought. Safely getting enough productivity out of these systems for it to matter is impossible IMO. If you think a system can solve specific problems, then please outline these problems (what are the hardest problem you expect to be able to safely solve with your system?) and say how fast the system is going to solve it and how many people will be supervising its “thoughts”. Even putting aside object-level problems with these approaches, this seems pretty much hopeless.
'A specialised AI can speed up Infra-Bayesianism by the same amount random mathematicians can, by proving theorems and solving some math problems. A specialised AI can’t actually understand the goals of the research and contribute to the part that require the hardest kind of human thinking.' 'I claim that if a system can output enough of that kind of thinking to meaningfully contribute, then it’s going to be smart enough to be dangerous.'-> what about MINERVA, GPT-4, LATS, etc.? Would you say that they're specialized / dangerous / can't 'contribute to the part that require the hardest kind of human thinking'? If the latter, what is the easiest benchmark/task displaying 'complicated human thought' a non-dangerous LLM would have to pass/do for you to update?
'I further claim that there’s a number of hours of complicated-human-thought such that making a safe system that can output work corresponding to that number in less than, e.g., 20 years, requires at least that number of hours of complicated human thought.' -> this seems obviously ridiculously overconfident, e.g. there are many tasks for which verification is easier/takes less time than generation; e.g. (peer) reviewing alignment research; I'd encourage you to try to operationalize this for a prediction market.
'Safely getting enough productivity out of these systems for it to matter is impossible IMO.' -> this is a very strong claim backed by no evidence; I'll also note that 'for it to matter' should be a pretty low bar, given the (relatively) low amount of research work that has gone into (especially superintelligence) alignment and the low number of current FTEs.
'If you think a system can solve specific problems, then please outline these problems (what are the hardest problem you expect to be able to safely solve with your system?) and say how fast the system is going to solve it and how many people will be supervising its “thoughts”.' -> It seems to me a bit absurd to ask for these kinds of details (years, number of people) years in advance; e.g. should I ask of various threat models how many AI researchers would work for how long on the (first) AI that takes over, before I put any credence on them? But yes, I do expect automated alignment researchers to be able to solve a wide variety of problems very safely (on top of all the automated math research which is easily verifiable), including scalable oversight (e.g. see RLAIF already) and automated mech interp (e.g. see OpenAI's recent work and automated circuit discovery). More generally, I expect even if you used systems ~human-level on a relatively broad set of tasks (so as to have very high confidence you fully cover everything a median human alignment researcher can do), the takeover risks from only using them internally for automated alignment research (for at least months of calendar time) could relatively easily be driven << 0.1%, even just through quite obvious/prosaic safety/alignment measures, like decent evals and red-teaming, adversarial training, applying safety measures like those from recent safety work by Redwood (e.g. removing steganography + monitoring forced intermediate text outputs), applying the best prosaic alignment methods at the time (e.g. RLH/AIF variants as of today), unlearning (e.g. of ARA, cyber, bio capabilities), etc.