While writing, I realized that this sounds a bit similar to the unilateralist's curse. It's not the same, but it has parallels. I'll discuss that briefly because it's relevant to other aspects of the situation. The unilateralist's curse does not occur specifically due to multiple samplings, it occurs because different actors have different beliefs about the value/disvalue, and this variance in beliefs makes it more likely that one of those actors has a belief above the "do it" threshold. If each draw from the AGI urn had the same outcome, this would look a lot like a unilateralist's curse situation where we care about variance in the actors' beliefs. But I instead think that draws from the AGI urn are somewhat independent and the problem is just that we should incur e.g., a 5% misalignment risk as few times as we have to. 

Interestingly, a similar look at variance is part of what makes the infosecurity situation much worse for multiple projects compared to centralized AGI project: variance is bad here. I expect a single government AGI project to care about and invest in security at least as much as the average AGI company. The AGI companies have some variance in their caring and investment in security, and the lower ones will be easier to steal from. If you assume these multiple projects have similar AGI capabilities (this is a bad assumption but is basically the reason to like multiple projects for Power Concentration reasons so worth assuming here; if the different projects don't have similar capabilities, power is not very balanced), you might then think that any of the companies getting their models stolen is similarly bad to the centralized project getting its models stolen (with a time lag I suppose, because the centralized project got to that level of capability faster). 

If you are hacking a centralized AGI project, say you have a 50% chance of success. If you are hacking 3 different AGI projects, you have 3 different/independent 50% chances of success. They're different because these project have different security measures in place. Now sure, as indicated by one of the points in this blog post, maybe less effort goes into hacking each of the 3 projects (because you have to split your resources, and because there's less overall interest in stealing model weights), maybe that pushes each of these down to 33%. These numbers are obviously made up, and they would get to a 1 – (0.67^3) = 70% chance of success. 

Unilateralist's curse is about variance in beliefs about the value of some action. The parent comment is about taking multiple independent actions that each have a risk of very bad outcomes.

Thanks for writing this, I think it's an important topic which deserves more attention. This post covers many arguments, a few of which I think are much weaker than you all state. But more importantly, I think you all are missing at least one important argument. I've been meaning to write this up, and I'll use this as my excuse. 

TL;DR: More independent AGI efforts means more risky “draws” from a pool of potential good and bad AIs; since a single bad draw could be catastrophic (a key claim about offense/defense), we need fewer, more controlled projects to minimize that danger. 

The argument is basically an application of the Vulnerability World Hypothesis to AI development. You capture part of this argument in the discussion of Racing, but not the whole thing. So the setup is that building any particular AGI is drawing a ball from the urn of potential AIs. Some of these AIs are aligned, some are misaligned — we probably disagree about the proportions here but that's not crucial, and note that the proportion depends on a bunch of other aspects about the world such as how good our AGI alignment research is. More AGI projects means more draws from the urn and a higher likelihood of pulling out misaligned AI systems. Importantly, I think that pulling out a misaligned AGI system is more bad than pulling out an aligned AGI system is good. I think this because I think some of the key components about the world that are offense-favored. 

Key assumption/claim: human extinction and human loss of control are offense-favored — if there were similarly resourced actors trying to destroy humanity as to protect it, humanity would be destroyed. I have a bunch of intuitions for why this is true, to give some sense: 

• Humans are flesh bags that die easily and never come back to life. AIs will not be like this.
• Humans care a lot about not dying, their friends and families not dying, etc., I expect extorting a small number of humans in order to gain control would simply work if one could successfully make the relevant threats.
• Terrorists or others who seek to cause harm often succeed. There are many mass shootings. 8% of US presidents were assassinated in office. I don't actually know what the average death count per attempted terrorist is; I would intuitively guess it's between 0.5 and 10 (This Wikipedia article indicates it's ~10, but I think you should include attempts that totally fail, even though these are not typically counted). Terrorism is very heavy tailed, which I think probably means that more capable terrorists (i.e., AIs that are at least as good as human experts, AGI+) will have high fatality rates.
• There are some emerging technologies that so far seem more offense-favored to me. Maybe not 1000:1, but definitely not 1:1. Bio tech and engineered pandemics seem like this; autonomous weapons seem like this.
• The strategy-stealing assumption seems false to me, partially for reasons listed in the linked post. I note that the linked post includes Paul listing a bunch of convincing-to-me ways in which strategy-stealing is false and then concluding that it's basically true. The claim about offense is easier than defense is sorta just a version of the strategy stealing claim, this bullet point isn't actually another distinct argument, just an excuse to point toward previous thinking and the various arguments there. 

A couple caveats: I think killing all of humanity with current tech is pretty hard; as noted however, I think this is too high a bar because probably things like extortion are sufficient for grabbing power. Also, I think there are some defensive strategies that would actually totally work at reducing the threat from misaligned AGI systems. Most of these strategies look a lot like "centralization of AGI development", e.g., destroying advanced computing infrastructure, controlling who uses advanced computing infrastructure and how they use it, a global treaty banning advanced AI development (which might be democratically controlled but has the effect of exercising central decision making). 

So circling back to the urn, if you pull out an aligned AI system, and 3 months later somebody else pulls out a misaligned AI system, I don't think pulling out the aligned AI system a little in advance buys you that much. The correct strategy to this situation is to try and make the proportion of balls weighted heavily toward aligned, AND to pull out as few as you can. 

More AGI development projects means more draws from the urn because there are more actors doing this and no coordinated decision process to stop. You mention that maybe government can regulate AI developers to reduce racing. This seems like it will go poorly, and in the worlds where it goes well, I think you should maybe just call them "centralization" because they involve a central decision process deciding who can train what models when with what methods. That is, extremely involved regulations seem to effectively be centralization. 

Notably, this is related but not the same as the effects from racing. More AGI projects leads to racing which leads to cutting corners on safety (higher proportion of misaligned AIs in the urn), and racing leads to more draws from the urn because of fear of losing to a competitor. But even without racing, more AGI projects means more draws from the urn. 

The thing I would like to happen instead is that there is a very controlled process for drawing from the urn, where each ball is carefully inspected, and if we draw aligned AIs, we use them to do AI alignment research, i.e., increase the proportion of aligned AIs in the urn. And we don't take more draws from the urn until we're really sure we're quite confident we're not going to pull out a misaligned AI. Again, this is both about reducing the risk of catastrophe each time you take a risky action, and about decreasing the number of times you have to take risky actions. 

Summarizing: If you are operating in a domain where losses are very bad, you want to take less gambles. I think AGI and ASI development are such domains, and decentralized AGI development means more gambles are taken. 

Noting that I spent a couple minutes pondering the quoted passage which I don't think was a good use of time (I basically would have immediately dismissed it if I knew Claude wrote it, and I only thought about it because my prior on Buck saying true things is way higher), and I would have preferred the text not have this. 

I don't see anybody having mentioned it yet, but the recent paper about LLM Introspection seems pretty relevant. I would say that a model which performs very well at introspection (as defined there) would be able to effectively guess which jailbreak strategies were attempted. 

There is now some work in that direction: https://forum.effectivealtruism.org/posts/47RH47AyLnHqCQRCD/soft-nationalization-how-the-us-government-will-control-ai

Sounds like a very successful hackathon! Nice work to everybody involved!

Some prompts I found interesting when brainstorming LLM startups

I spent a little time thinking about making an AI startup. I generally think it would be great if more people were trying to build useful companies that directly add value, rather than racing to build AGI. Here are some of the prompts I found interesting to think about, perhaps they will be useful to other people/AI agents interested in building a startup:

• What are the situations where people will benefit from easy and cheap access to expert knowledge? You’re leveraging that human expert labor is hard to scale to many situations (especially when experts are rare, needs are specific, it’s awkward, it’s too expensive — including both raw cost and the cost of finding/trusting/onboarding an expert). What are all the things you occasionally pay somebody to do, but which requires them coming in person? What is a problem people know they have but they don’t seek out existing solutions (because of perceived cost, awkwardness, unsure how). e.g., dating profile feedback, outfit designer. 
• Solve a problem that exists due to technological development, e.g., preventing the social isolation from social media, reducing various catastrophic risks during and after intelligence explosion.

Some other problem attack surface opened up by LLMs:

• Cheaply carry out simple straightforward tasks.
• Analyze data at scale.
• Do tasks that there was no previous market for (e.g., provided $5 of value but took an hour, and you can’t hire people for $5/hour because they don’t want to work for that little and the overhead is high). Reasons for lack of market: not enough money to be made, can’t trust somebody (not worth the time needed to grow trust, or substantial privacy concerns), communication cost too high (specify task), other overhead too high (travel, finding person), training cost too high compared to salary (imagine it took 8 years to become a barber).
• Provide cheap second opinions, potentially many of them (e.g., reviewing a low-importance piece of writing).

Some other desiderata I had (for prompting LLMs):

• I want to have a clear and direct story for making people's lives better or solving problems they have. So I have a slight preference for B2C over B2B, unless there's a clear story for how we're significantly helping the business in an industry that benefits people.
• We don't want to be obsoleted by the predictable products coming out of AI development companies; for instance a product that just takes ChatGPT and adds a convenient voice feature is not a good idea because that niche is likely to be met by existing developers fairly soon.
• We don't want to work on something that other well resourced efforts are working on. Our edge is having good ideas and creative implementations, not being able to outcompete others according to resource investment. We should play to our strengths and not try to get in a losing battle with strong existing products.
• I mainly don't want to be directly competing with existing products or services, instead I want to be creating a large amount of counterfactual value by solving a problem that nobody else has solved.
• The MVP should be achievable by a team of 5 working for <6 months, ideally even a very basic MVP should be achievable in just a week or two of full-time work.
• I want to be realistic, we won't be able to solve everything or do everything. I want to aim for a fairly niche product, rather than solving a huge-scale problem like fixing medical care. That is, instead of a general medical chatbot, a better idea would be a first-aid tutor that can help people learn first-aid basics and refresh their knowledge later.
• I want to be providing a service people are excited to receive. For instance, a sustainable living advisor isn't a great idea because if it actually got people to make more sustainable decisions, that would be annoying — people don't actually want to hear that they shouldn't fly home to their family for the holidays, even though this is one of the more important sustainability decisions they could make.
• I probably want to provide a service that is not currently provided by a simple google search. For instance, a cooking assistant is pretty much just glorified google search. I want to be providing more value than that. Services which can be provided by a simple google search are likely to be filled in by existing developers.
• I do not want to be pushing the frontier of AI capabilities in dangerous domains such as: synthetic biology, cybersecurity, autonomous weapons, ML engineering and AI development, manipulation and persuasion. Generally pushing the frontier of scientific capabilities may also be in this group due to its effects on the other domains, but it is unclear.

Yep, I basically agree with those being the cruxes!

On how much of the goal reasoning is happening out loud: Nowadays, I think about a lot of AI safety research as being aimed at an AI Control scenario where we are closely supervising what models are thinking about in CoT, and thus malign goal seeking must either happen in individual forward passes (and translated to look harmless most the time) or in an obfuscated way in CoT. (or from rogue deployments outside the control scheme)

Studying the intrinsic goals of the models in naturalistic settings would also be interesting, but I expect this to be hard, and also less relevant to the threat models we're worried about. I'm mainly concerned about models becoming more goal-directed with scale and someone figuring out long-horizon RL training on top of that, leading to models that are qualitatively different from current ones.

By naturalistic, I mean "from a realistic training process, even if that training is designed to create goals". Which sounds like what you said is the main threat model you're worried about? If you have the time, I would push you harder on this: what is a specific story of AI catastrophe that you are trying to study/prevent? 

Some quick thoughts (only skimmed the post, writing quickly), as you asked for feedback:

It looks like the main thing you're testing is some variant of "when prompted to do goal directed behavior, how effective is the model at satisfying the goal?" That's a reasonable thing to investigate, but I'm not sure it would be near the top of the list of "empirical research on goal-directed-ness that I want to see". I'm probably mainly interested in the deceptive alignment motivation, read the rest of this comment as focusing on that. 

Aside: To state it directly, I think the main reason to study goal-directedness in this lower-validity setting (of giving models goals in prompts) is that CoT-based goal-directedness might act as a precursor for in-forward-pass goal directedness (which seems far more worrying re deceptive alignment) — so we can study it earlier. So again, reasonable to study, but if you agree with me that this is the main reason for such experiments being valid, it's an important frame to have when thinking about this kind of work: artificially inducing goal directedness is a model-organism approach rather than a natural experiment. 

Thinking out loud, a list for goal-directedness work I want to see might be; sub-bullets are more detailed ideas:

• Are base models goal directed? Are RLHF finetuned models goal directed? (naturalistic setting)
  • Could look like this recent work on the consistency of model answers to values questions, but more adapted to goals you hypothesize the models to have (like how consistently do models follow a particular behavior outlined in the Model Spec)
  • How do RLHFed models deal with conflicting goals — do they engage in sophisticated reasoning about this or instead seem to follow simple heuristics? 
• To the extent these models are goal directed (including because you induce this via prompting), is anything interesting going on: 
  • Do they goal generalize the way we would expect? Similar to this recent work, but aimed at some risk other than reward hacking, I would be particularly interested in the time horizon over which the world is affected, as this is perhaps a proxy for a model having non-myopic goals. 
  • Is there specification gaming or 'in context reward hacking' across many different settings? 
• As mentioned, maybe CoT and prompting provide an early warning sign for forward-pass goal-directed-ness. Is this true? How much of an early warning sign?

It looks like the settings in this post are sorta a general capability eval for a model accomplishing goals. I wonder if you think they add a ton of value over existing agent benchmarks like SWE-Bench? My intuition says you would be better off on trying to focus in on a narrower question that is particularly relevant to safety, like one of those I mentioned. 

Sorry if this comment was rude or mean, it's been a couple weeks and this post has no feedback even though you asked, I figured something might be better than nothing. It looks to me like your overall approach and ways of thinking about this are good! 

What's the evidence that this document is real / written by Anthropic? 

This sentence seems particularly concerning:

We believe the first two issues can be addressed by focusing on deterrence rather than pre-harm enforcement: instead of deciding what measures companies should take to prevent catastrophes (which are still hypothetical and where the ecosystem is still iterating to determine best practices), focus the bill on holding companies responsible for causing actual catastrophes.

Nice work, these seem like interesting and useful results! 

High level question/comment which might be totally off: one benefit of having a single, large, SAE neuron space that each token gets projected into is that features don't get in each other's way, except insofar as you're imposing sparsity. Like, your "I'm inside a parenthetical" and your "I'm attempting a coup" features will both activate in the SAE hidden layer, as long as they're in the top k features (for some sparsity). But introducing switch SAEs breaks that: if these two features are in different experts, only one of them will activate in the SAE hidden layer (based on whatever your gating learned). 

The obvious reply is "but look at the empirical results you fool! The switch SAEs are pretty good!" And that's fair. I weakly expect what is happening in your experiment is that similar but slightly specialized features are being learned by each expert (a testable hypothesis), and maybe you get enough of this redundancy that it's fine e.g,. the expert with "I'm inside a parenthetical" also has a "Words relevant to coups" feature and this is enough signal for coup detection in that expert. 

Again, maybe this worry is totally off or I'm misunderstanding something.