Hello! I work at Lightcone and like LessWrong :-). I have made some confidentiality agreements I can't leak much metadata about (like who they are with). I have made no non-disparagement agreements.
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I spent some time Thursday morning arguing with Habryka about the intended use of react downvotes. I think I now have a fairly compact summary of his position.
PSA: When to upvote and downvote a react
Upvote a react when you think it's helpful to the conversation (or at least, not antihelpful) and you agree with it. Imagine a react were a comment. If you would agree-upvote it and not karma-downvote it, you can upvote the react.
Downvote a react when you think it's unhelpful for the conversation. This might be because you think the react isn't being used for its intended purpose, because you think people are going through noisily agree reacting to loads of passages in a back-and-forth to create an impression of consensus, or other reasons. If, when you're imagining a react were a comment, you would karma-downvote the comment, you might downvote the react.
2You claim (and I agree) that option control will probably not be viable at extreme intelligence levels. But I also notice that when you list ways that AI systems help with alignment, all but one (maybe two), as I count it, are option control interventions.
evaluating AI outputs during training, labeling neurons in the context of mechanistic interpretability, monitoring AI chains of thought for reward-hacking behaviors, identifying which transcripts in an experiment contain alignment-faking behaviors, classifying problematic inputs and outputs for the purpose of preventing jailbreaks
I think "labeling neurons" isn't option control. Detecting alignment-faking also seems marginal; maybe it's more basic science than option control.
I think mech interp is proving to be pretty difficult, in a similar way to human neuroscience. My guess is that even if we can characterise the low-level behaviour of all neurons and small circuits, we'll be really stuck with trying to figure out how the AI minds work, and even more stuck trying to turn that knowledge into safe mind design, and even more even more stuck trying to turn that knowledge differentially into safe mind design vs capable mind design.
Will we be able to get AIs to help us with this higher-level task as well? The task of putting all the data and experiments together and coming up with a theory that explains how they behave. I think they probably can just if they could do the same for human neuroscience. And my weak guess is that, if there's a substantial sweet spot, they will be able to do the same for human neuroscience.
But I'm not sure how well we'll be able to tell that they have given us a correct theory? They will produce some theory of how the brain or a machine mind works, and I don't know (genuinely don't know) whether we will be able to tell if it's a subtly wrong theory. It does seem pretty hard to produce a small theory, that makes a bunch of correct empirical predictions, but has some (intentional or unintentional) error that is a vector for loss-of-control. So maybe reality will come in clutch with some extra option control at the critical time.
Your taxonomies of the space of worries and orientations to this question are really good, and I think well capture my concerns above. But I wanted to spell out my specific concerns because things will succeed or fail for specific reasons.
I do not think your post is arguing for creating warning shots. I understand it to be advocating for not averting warning shots.
To extend your analogy, there are several houses that are built close to a river, and you think that a flood is coming that will destroy them. You are worried that if you build a dam that would protect the houses currently there, then more people will build by the river and their houses will be flooded by even bigger floods in the future. Because you are worried people will behave in this bad-for-them way, you choose not to help them in the short term. (The bit I mean to point to by "diagonalising" is the bit where you think about what you expect they'll do, and which mistakes you think they'll make, and plan around that).
I expect moderately sized warning shots to increase the chances humanity as a whole takes serious actions and, for example, steps up efforts to align the frontier labs.
It seems naïvely evil to knowingly let the world walk into a medium-sized catastrophe. To be clear, I think that sometimes it is probably evil to stop the world from walking into a catastrophe, if you think that increases the risk of bad things like extinctions. But I think the prior of not diagonalising against others (and of not giving yourself rope with which to trick yourself) is strong.
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1I think you train Claude 3.7 to imitate the paraphrased scratchpad, but I'm a little unsure because you say "distill". Just checking that Claude 3.7 still produces CoT (in the style of the paraphrase) after training, rather than being trained to perform the paraphrased-CoT reasoning in one step?
It's been a long time since I looked at virtual comments, as we never actually merged them in. IIRC, none were great, but sometimes they were interesting (in a kind of "bring your own thinking" kind of way).
They were implemented as a Turing test, where mods would have to guess which was the real comment from a high karma user. If they'd been merged in, it would have been interesting to see the stats on guessability.
Could exciting biotech progress lessen the societal pressure to make AGI?
Suppose we reach a temporary AI development pause. We don't know how long the pause will last; we don't have a certain end date nor is it guaranteed to continue. Is it politically easier for that pause to continue if other domains are having transformative impacts?
I've mostly thought this is wishful thinking. Most people don't care about transformative tech; the absence of an alternative path to a good singularity isn't the main driver of societal AI progress.
But I've updated some here. I think that another powerful technology might make a sustained pause an easier sell. My impression (not super-substantiated) is that advocacy for nuclear has kind of collapsed as solar has more resoundingly outstripped fossil fuels. (There was a recent ACX post that mentioned something like this).
There's a world where the people who care the most to push on AI say something like "well, yeah, it would be overall better if we pushed on AI, but given that we have biotech, we might as well double down on our strengths".
Ofc, there are also a lot of disanalogies between nuclear/solar and AI/bio, but the argument smells a little less like cope to me now.
I think your comment is supposed to be an outside view argument that tempers the gears-level argument in the post. Maybe we could think of it as providing a base-rate prior for the gears-level argument in the post. Is that roughly right? I'm not sure how much I buy into this kind of argument, but I also have some complaints by the outside views lights.
First, let me quickly recap your argument as I understand it.
R&D increases welfare by allowing an increase in consumption. We'll assume that our growth in consumption is driven, in some fraction, by R&D spending. Assuming utility isn't linear in consumption, we need to have some story about how the increase in consumption is distributed. Given a bunch of such assumptions, we can get a net present value of the utility, which is 45% as large as giving cash to people with $500/year.Then, we can look at how the value of interventions are distributed within "causes". Some data suggest that the 97.5th percentile intervention is about 10x as good as the mean (and maybe 100x as good as the 50th percentile), across a few different intervention areas.
Assuming a lognormal fit, there aren't enough R&D ideas for the best R&D dollars to be 10,000x as good as the mean R&D dollar.
But, this says nothing about differences in cost-effectiveness between different "causes". So this argument doesn't bite for, say, shrimp welfare interventions, which could be arbitrarily more impactful than global health, or R&D developments.
I hope that is a roughly correct rendition of your argument.
Here are my even-assuming-outside-view criticisms:
- Even the Davidson model allows that the distribution for interventions that increase the rate/effectiveness of R&D (rather than just purchasing some at the same rate) could be much more effective. I think superresearchers (or even just a large increase in the number of top researchers) are such an intervention
- To the extent we're allowing cause-hopping to enable large multipliers (which we must to think that there are potentially much more impactful opportunities than superbabies), I care about superbabies because of the cause of x-risk reduction! Which I think has much higher cost-effectiveness than growth-based welfare interventions.
I'm inclined to agree, but at least this is an improvement over it only living in Habryka's head. It may be that this + moderation is basically sufficient, as people seem to have mostly caught on to the intended patterns.