johnswentworth's Shortform

I just spent some time doing GPQA, and I think I agree with you that the difficulty of those problems is overrated. I plan to write up more on this.

Generalizing the lesson here: the supposedly-hard benchmarks for which I have seen a few problems (e.g. GPQA, software eng) turn out to be mostly quite easy, so my prior on other supposedly-hard benchmarks which I haven't checked (e.g. FrontierMath) is that they're also mostly much easier than they're hyped up to be

Daniel Litt's account here supports this prejudice. As a math professor, he knew instantly how to solve the low/medium-level problems he looked at, and he suggests that each "high"-rated problem would be likewise instantly solvable by an expert in that problem's subfield.

And since LLMs have eaten ~all of the internet, they essentially have the crystallized-intelligence skills for all (sub)fields of mathematics (and human knowledge in general). So from their perspective, all of those problems are very "shallow". No human shares their breadth of knowledge, so math professors specialized even in slightly different subfields would indeed have to do a lot of genuine "deep" cognitive work; this is not the case for LLMs.

GPQA stuff is even worse, a literal advanced trivia quiz that seems moderately resistant to literal humans literally googling things, but not to the way the knowledge gets distilled into LLMs.

Basically, I don't think any extant benchmark (except I guess the Millennium Prize Eval) actually tests "deep" problem-solving skills, in a way LLMs can't cheat at using their overwhelming knowledge breadth.

My current strong-opinion-weakly-held is that they're essentially just extensive knowledge databases with a nifty natural-language interface on top.^[1] All of the amazing things they do should be considered surprising facts about how far this trick can scale; not surprising facts about how close we are to AGI.

1. ^{^}

   Which is to say: this is the central way to characterize what they are; not merely "isomorphic to a knowledge database with a natural-language search engine on top if you think about them in a really convoluted way". Obviously a human can also be considered isomorphic to database search if you think about it in a really convoluted way, but that wouldn't be the most-accurate way to describe a human.

I agree with you on your assessment of GPQA. The questions themselves appear to be low quality as well. Take this one example, although it's not from GPQA Diamond:

In UV/Vis spectroscopy, a chromophore which absorbs red colour light, emits _____ colour light.

The correct answer is stated as yellow and blue. However, the question should read transmits, not emits; molecules cannot trivially absorb and re-emit light of a shorter wavelength without resorting to trickery (nonlinear effects, two-photon absorption).
 

This is, of course, a cherry-picked example, but is exactly characteristic of the sort of low-quality science questions I saw in school (e.g with a teacher or professor who didn't understand the material very well). Scrolling through the rest of the GPQA questions, they did not seem like questions that would require deep reflection or thinking, but rather the sort of trivia things that I would expect LLMs to perform extremely well on.

I'd also expect "popular" benchmarks to be easier/worse/optimized for looking good while actually being relatively easy. OAI et. al probably have the mother of all publication biases with respect to benchmarks, and are selecting very heavily for items within this collection. 

Personally, I think o1 is uniquely trash, I think o1-preview was actually better. Getting on average, better things from deepseek and sonnet 3.5 atm. 

I think there's something about Bay Area culture that can often get technical people to feel like the only valid way to contribute is through technical work. It's higher status and sexier and there's a default vibe that the best way to understand/improve the world is through rigorous empirical research.

I think this an incorrect (or at least incomplete) frame, and I think on-the-margin it would be good for more technical people to spend 1-5 days seriously thinking about what alternative paths they could pursue in comms/policy. 

I also think there are memes spreading around that you need to be some savant political mastermind genius to do comms/policy, otherwise you will be net negative. The more I meet policy people (including successful policy people from outside the AIS bubble), the more I think this narrative was, at best, an incorrect model of the world. At worst, a take that got amplified in order to prevent people from interfering with the AGI race (e.g., by granting excess status+validity to people/ideas/frames that made it seem crazy/unilateralist/low-status to engage in public outreach, civic discourse, and policymaker engagement.)

(Caveat: I don't think the adversarial frame explains everything, and I do think there are lots of people who were genuinely trying to reason about a complex world and just ended up underestimating how much policy interest there would be and/or overestimating the extent to which labs would be able to take useful actions despite the pressures of race dynamics.)

I think I probably agree, although I feel somewhat wary about it. My main hesitations are:

1. The lack of epistemic modifiers seems off to me, relative to the strength of the arguments they’re making. Such that while I agree with many claims, my imagined reader who is coming into this with zero context is like “why should I believe this?” E.g., “Without intervention, humanity will be summarily outcompeted and relegated to irrelevancy,” which like, yes, but also—on what grounds should I necessarily conclude this? They gave some argument along the lines of “intelligence is powerful,” and that seems probably true, but imo not enough to justify the claim that it will certainly lead to our irrelevancy. All of this would be fixed (according to me) if it were framed more as like “here are some reasons you might be pretty worried,” of which there are plenty, or "here's what I think," rather than “here is what will definitely happen if we continue on this path,” which feels less certain/obvious to me.
2. Along the same lines, I think it’s pretty hard to tell whether this piece is in good faith or not. E.g., in the intro Connor writes “The default path we are on now is one of ruthless, sociopathic corporations racing toward building the most intelligent, powerful AIs as fast as possible to compete with one another and vie for monopolization and control of both the market and geopolitics.” Which, again, I don’t necessarily disagree with, but my imagined reader with zero context is like “what, really? sociopaths? control over geopolitics?” I.e., I’m expecting readers to question the integrity of the piece, and to be more unsure of how to update on it (e.g. "how do I know this whole thing isn't just a strawman?" etc.).
3. There are many places where they kind of just state things without justifying them much. I think in the best case this might cause readers to think through whether such claims make sense (either on their own, or by reading the hyperlinked stuff—both of which put quite a lot of cognitive load on them), and in the worst case just causes readers to either bounce or kind of blindly swallow what they’re saying. E.g., “Black-Box Evaluations can only catch all relevant safety issues insofar as we have either an exhaustive list of all possible failure modes, or a mechanistic model of how concrete capabilities lead to safety risks.” They say this without argument and then move on. And although I agree with them (having spent a lot of time thinking this through myself), it’s really not obvious at first blush. Why do you need an exhaustive list? One might imagine, for instance, that a small number of tests would generalize well. And do you need mechanistic models? Sometimes medicines work safely without that, etc., etc. I haven’t read the entire Compendium closely, but my sense is that this is not an isolated incident. And I don't think this is a fatal flaw or anything—they're moving through a ton of material really fast and it's hard to give a thorough account of all claims—but it does make me more hesitant to use it as the default "here's what's happening" document. 

All of that said, I do broadly agree with the set of arguments, and I think it’s a really cool activity for people to write up what they believe. I’m glad they did it. But I’m not sure how comfortable I feel about sending it to people who haven’t thought much about AI.

One of the common arguments in favor of investing more resources into current governance approaches (e.g., evals, if-then plans, RSPs) is that there's nothing else we can do. There's not a better alternative– these are the only things that labs and governments are currently willing to support.

The Compendium argues that there are other (valuable) things that people can do, with most of these actions focusing on communicating about AGI risks. Examples:

• Share a link to this Compendium online or with friends, and provide your feedback on which ideas are correct and which are unconvincing. This is a living document, and your suggestions will shape our arguments.
• Post your views on AGI risk to social media, explaining why you believe it to be a legitimate problem (or not).
• Red-team companies’ plans to deal with AI risk, and call them out publicly if they do not have a legible plan. 

One possible critique is that their suggestions are not particularly ambitious. This is likely because they're writing for a broader audience (people who haven't been deeply engaged in AI safety).

For people who have been deeply engaged in AI safety, I think the natural steelman here is "focus on helping the public/government better understand the AI risk situation." 

There are at least some impactful and high-status examples of this (e.g., Hinton, Bengio, Hendrycks). I think in the last few years, for instance, most people would agree that Hinton/Bengio/Hendrycks have had far more impact in their communications/outreach/policy work than their technical research work.

And it's not just the famous people– I can think of ~10 junior or mid-career people who left technical research in the last year to help policymakers better understand AI progress and AI risk, and I think their work is likely far more impactful than if they had stayed in technical research. (And I'm even excluding people who are working on evals/if-then plans: like, I'm focusing on people who see their primary purpose as helping the public or policymakers develop "situational awareness", develop stronger models of AI progress and AI risk, understand the conceptual arguments for misalignment risk, etc.)

I appreciated their section on AI governance. The "if-then"/RSP/preparedness frame has become popular, and they directly argue for why they oppose this direction. (I'm a fan of preparedness efforts– especially on the government level– but I think it's worth engaging with the counterarguments.)

Pasting some content from their piece below.

High-level thesis against current AI governance efforts:

The majority of existing AI safety efforts are reactive rather than proactive, which inherently puts humanity in the position of managing risk rather than controlling AI development and preventing it.

Critique of reactive frameworks:

1. The reactive framework reverses the burden of proof from how society typically regulates high-risk technologies and industries.

In most areas of law, we do not wait for harm to occur before implementing safeguards. Banks are prohibited from facilitating money laundering from the moment of incorporation, not after their first offense. Nuclear power plants must demonstrate safety measures before operation, not after a meltdown.

The reactive framework problematically reverses the burden of proof. It assumes AI systems are safe by default and only requires action once risks are detected. One of the core dangers of AI systems is precisely that we do not know what they will do or how powerful they will be before we train them. The if-then framework opts to proceed until problems arise, rather than pausing development and deployment until we can guarantee safety. This implicitly endorses the current race to AGI.

This reversal is exactly what makes the reactive framework preferable for AI companies. 

Critique of waiting for warning shots:

3. The reactive framework incorrectly assumes that an AI “warning shot” will motivate coordination.

Imagine an extreme situation in which an AI disaster serves as a “warning shot” for humanity. This would imply that powerful AI has been developed and that we have months (or less) to develop safety measures or pause further development. After a certain point, an actor with sufficiently advanced AI may be ungovernable, and misaligned AI may be uncontrollable. 

When horrible things happen, people do not suddenly become rational. In the face of an AI disaster, we should expect chaos, adversariality, and fear to be the norm, making coordination very difficult. The useful time to facilitate coordination is before disaster strikes. 

However, the reactive framework assumes that this is essentially how we will build consensus in order to regulate AI. The optimistic case is that we hit a dangerous threshold before a real AI disaster, alerting humanity to the risks. But history shows that it is exactly in such moments that these thresholds are most contested –- this shifting of the goalposts is known as the AI Effect and common enough to have its own Wikipedia page. Time and again, AI advancements have been explained away as routine processes, whereas “real AI” is redefined to be some mystical threshold we have not yet reached. Dangerous capabilities are similarly contested as they arise, such as how recent reports of OpenAI’s o1 being deceptive have been questioned [LW(p) · GW(p)]. 

This will become increasingly common as competitors build increasingly powerful capabilities and approach their goal of building AGI. Universally, powerful stakeholders fight for their narrow interests, and for maintaining the status quo, and they often win, even when all of society is going to lose. Big Tobacco didn’t pause cigarette-making when they learned about lung cancer; instead they spread misinformation and hired lobbyists. Big Oil didn’t pause drilling when they learned about climate change; instead they spread misinformation and hired lobbyists. Likewise, now that billions of dollars are pouring into the creation of AGI and superintelligence, we’ve already seen competitors fight tooth and nail to keep building. If problems arise in the future, of course they will fight for their narrow interests, just as industries always do. And as the AI industry gets larger, more entrenched, and more essential over time, this problem will grow rapidly worse. 

I like it. I do worry that it, and The Narrow Path, are both missing how hard it will be to govern and restrict AI.

My own attempt is much less well written and comprehensive, but I think I hit on some points that theirs misses: https://www.lesswrong.com/posts/NRZfxAJztvx2ES5LG/a-path-to-human-autonomy [LW · GW]

(There was already a linkpost [LW · GW].)

Why do you believe AMD and Google make better hardware than Nvidia?

I'm holding a modest long position in NVIDIA (smaller than my position in Google), and expect to keep it for at least a few more months. I expect I only need NVIDIA margins to hold up for another 3 or 4 years for it to be a good investment now.

It will likely become a bubble before too long, but it doesn't feel like one yet.

No, the mi300x is not superior to nvidias chips, largely because It costs >2x to manufacture as nvidias chips

While the first-order analysis seems true to me, there are mitigating factors:

• AMD appears to be bungling on their GPUs being reliable and fast, and probably will for another few years. (At least, this is my takeaway from following the TinyGrad saga on Twitter...) Their stock is not valued as it should be for a serious contender with good fundamentals, and I think this may stay the case for a while, if not forever if things are worse than I realize.
• NVIDIA will probably have very-in-demand chips for at least another chip generation due to various inertias.
• There aren't many good-looking places for the large amount of money that wants to be long AI to go right now, and this will probably inflate prices for still a while across the board, in proportion to how relevant-seeming the stock is. NVDA rates very highly on this one.

So from my viewpoint I would caution against being short NVIDIA, at least in the short term.

Potential counterpoints:

• If AI automates most, but not all, software engineering, moats of software dependencies could get more entrenched, because easier-to-use libraries have compounding first-mover advantages.
• The disadvantages of AMD software development potentially need to be addressed at levels not accessible to an arbitrary feral automated software engineer in the wild, to make the stack sufficiently usable. (A lot of actual human software engineers would like the chance.)
• NVIDIA is training their own AIs, who are pretty capable.
• NVIDIA can invest their current profits. (Revenues, not stock valuations.)

Two questionson this.

First, if AI is a big value driver, in a general economic sense, is your view that NVIDIA is over prices against its future potential or just that relatively NVIDIA will under perform other investment alternatives you see.

Second, and perhaps an odd and speculative (perhaps nonsense) thought. I would expect that in this area one might see some network effects in play as well so wondering if that might impact the AI engineering decisions on software. Could the AI software solutions look towards maximising the value of the installed network (AIs work better on a common chip and code infrastructure) than will be true if one looks at some isolated technical stats. A bit a long the lines of why Beta was displaced by VHS dispite being a better technology. If so, then it seems possible that NVIDA could remain a leader and enjoy its current pricing powers (at least to some extent) for a fairly long period of time.

AI that can rewrite CUDA is a ways off. It's possible that it won't be that far away in calendar time, but it is far away in terms of AI market growth and hype cycles. If GPT-5 does well, Nvidia will reap the gains more than AMD or Google.

Transpiling assembly code written for one OS/kernel to assembly code for another OS/kernel while taking advantage the full speed of the processor, is a completely different task from transpiling say, java code into python.

Also, the hardware/software abstraction might break. A python developer can say hardware failures are not my problem. An assembly developer working at an AGI lab needs to consider hardware failures as lost wallclock time in their company’s race to AGI, and will try to write code so that hardware failures don’t cause the company to lose time.

GPT4 definitely can’t do this type of work and I’ll bet a lot of money GPT5 can’t do it either. ASI can do it but there’s bigger considerations than whether Nvidia makes money there, such as whether we’re still alive and whether markets and democracy continue to exist. Making a guess of N for which GPT-N can get this done requires evaluating how hard of a software task this actually is, and your comment contains no discussion of this. 

Have you looked at tinygrad’s codebase or spoken to George Hotz about this?

Shorting nvidia might be tricky. I’d short nvidia and long TSM or an index fund to be safe at some point. Maybe now? Typically the highest market cap stock has poor performance after it claims that spot.

Even for serious intellectual conversations, something I appreciate in this kind of advice is that it often encourages computational kindness [LW · GW]. E.g. it's much easier to answer a compact closed question like "which of these three options do you prefer" instead of an open question like "where should we go to eat for lunch". The same applies to asking someone about their research; not every intellectual conversation benefits from big open questions like the Hamming Question.

Generally fair and I used to agree, I've been looking at it from a bit of a different viewpoint recently.

If we think of a "vibe" of a conversation as a certain shared prior that you're currently inhabiting with the other person then the free association game can rather be seen as a way of finding places where your world models overlap a lot.

My absolute favourite conversations are when I can go 5 layers deep with someone because of shared inference. I think the vibe checking for shared priors is a skill that can be developed and the basis lies in being curious af.

There's apparently a lot of different related concepts in psychology about holding emotional space and other things that I think just comes down to "find the shared prior and vibe there".

There's a general-purpose trick I've found that should, in theory, be applicable in this context as well, although I haven't mastered that trick myself yet.

Essentially: when you find yourself in any given cognitive context, there's almost surely something "visible" from this context such that understanding/mastering/paying attention to that something would be valuable and interesting.

For example, suppose you're reading a boring, nonsensical continental-philosophy paper. You can:

• Ignore the object-level claims and instead try to reverse-engineer what must go wrong in human cognition, in response to what stimuli, to arrive at ontologies that have so little to do with reality.
• Start actively building/updating a model of the sociocultural dynamics that incentivize people to engage in this style of philosophy. What can you learn about mechanism design from that? It presumably sheds light on how to align people towards pursuing arbitrary goals, or how to prevent this happening...
• Pay attention to your own cognition. How exactly are you mapping the semantic content of the paper to an abstract model of what the author means, or to the sociocultural conditions that created this paper? How do these cognitive tricks generalize? If you find a particularly clever way to infer something form the text, check: would your cognitive policy automatically deploy this trick in all context where it'd be useful, or do you need to manually build a TAP [? · GW] for that?
• Study what passages make the feelings of boredom or frustration spike. What does that tell you about how your intuitions/heuristics work? Could you extract any generalizable principles out of that? For example, if a given sentence particularly annoys you, perhaps it's because it features a particularly flawed logical structure, and it'd be valuable to learn to spot subtler instances of such logical flaws "in the wild".

The experience of reading the paper's text almost certainly provides some data uniquely relevant to some valuable questions, data you legitimately can't source any other way. (In the above examples: sure you can learn more efficiently about the author's cognition or the sociocultural conditions by reading some biographies or field overviews. But (1) this wouldn't give you the meta-cognitive data about how you can improve your inference functions for mapping low-level data to high-level properties, (2) those higher-level summaries would necessarily be lossy, and give you a more impoverished picture than what you'd get from boots-on-the-ground observations.)

Similar applies to:

• Listening to boring lectures. (For example, you can pay intense attention to the lecturer's body language, or any tricks or flaws in their presentation.)
• Doing a physical/menial task. (Could you build, on the fly, a simple model of the physics (or logistics) governing what you're doing, and refine it using some simple experiments? Then check afterwards if you got it right. Or: If you were a prehistoric human with no idea what "physics" is, how could you naturally arrive at these ideas from doing such tasks/making such observations? What does that teach you about inventing new ideas in general?)
• Doing chores. (Which parts of the process can you optimize/streamline? What physical/biological conditions make those chores necessary? Could you find a new useful takeaway from the same chore every day, and if not, why?)

Et cetera.

There's a specific mental motion I associate with using this trick, which involves pausing and "feeling out [LW · GW]" the context currently loaded in my working memory, looking at it from multiple angles, trying to see anything interesting or usefully generalizable.

In theory, this trick should easily apply to small-talk as well. There has to be something you can learn to track in your mind, as you're doing small-talk, that would be useful or interesting to you. 

One important constraint here is that whatever it is, it has to be such that your outwards demeanour would be that of someone who is enjoying talking to your interlocutor. If the interesting thing you're getting out of the conversation is so meta/abstract you end up paying most of the attention to your own cognitive processes, not on what the interlocutor is saying, you'll have failed at actually doing the small-talk. (Similarly, if, when doing a menial task, you end up nerd-sniped by building a physical model of the task, you'll have failed at actually doing the task.)

You also don't want to come across as sociopathic, so making a "game" of it where you're challenging yourself to socially engineer the interlocutor into something is, uh, not a great idea.

The other usual advice for finding ways to enjoy small-talk are mostly specialized instances of the above idea that work for specific people. Steering the small-talk to gradient-descend towards finding emotional common ground, ignoring the object-level words being exchanged and build a social model of the interlocutor, doing a live study of the social construct of "small-talk" by playing around with it, etc.

You'll probably need to find an instance of the trick that works for your cognition specifically, and it's also possible the optimization problem is overconstrained in your case. Still, there might be something workable.

Some people struggle with the specific tactical task of navigating any conversational territory. I've certainly had a lot of experiences where people just drop the ball leaving me to repeatedly ask questions. So improving free-association skill is certainly useful for them.

Unfortunately, your problem is most likely that you're talking to boring people (so as to avoid doing any moral value judgements I'll make clear that I mean johnswentworth::boring people).

There are specific skills to elicit more interesting answers to questions you ask. One I've heard is "make a beeline for the edge of what this person has ever been asked before" which you can usually reach in 2-3 good questions. At that point they're forced to be spontaneous, and I find that once forced, most people have the capability to be a lot more interesting than they are when pulling cached answers.

This is easiest when you can latch onto a topic you're interested in, because then it's easy on your part to come up with meaningful questions. If you can't find any topics like this then re-read paragraph 2.

Talking to people is often useful for goals like "making friends" and "sharing new information you've learned" and "solving problems" and so on. If what conversation means (in most contexts and for most people) is 'signaling that you repeatedly have interesting things to say', it's required to learn to do that in order to achieve your other goals.

Most games aren't that intrinsically interesting, including most social games. But you gotta git gud anyway because they're useful to be able to play well.

The skill in such a game is largely in understanding the free association space, knowing how people likely react and thinking enough steps ahead to choose moves that steer the person where you want to go, either into topics you find interesting, information you want from them, or getting them to a particular position, and so on. If you're playing without goals, of course it's boring...

I think that "getting good" at the "free association" game is in finding the sweet spot / negotiation between full freedom of association and directing toward your own interests, probably ideally with a skew toward what the other is interested in. If you're both "free associating" with a bias toward your own interests and an additional skew toward perceived overlap, updating on that understanding along the way, then my experience says you'll have a good chance of chatting about something that interests you both. (I.e. finding a spot of conversation which becomes much more directed than vibey free association.) Conditional on doing something like that strategy, I find it ends up being just a question of your relative+combined ability at this and the extent of overlap (or lack thereof) in interests.

So short model is: Git gud at free association (+sussing out interests) -> gradient ascend yourselves to a more substantial conversation interesting to you both.

It's becomes more interresting when the people constrain their output based on what they expect is true information that the other person does not yet know. It's useful to talk to an expert, who tells you a bunch of random stuff they know that you don't.

Often some of it will be useful. This only works if they understand what you have said though (which presumably is something that you are interested in). And often the problem is that people's models about what is useful are wrong. This is especially likely if you are an expert in something. Then the thing that most people will say will be worse what you would think on the topic. This is especially bad if the people can't immediately even see why what you are saying is right.

The best strategy around this I have found so far is just to switch the topic to the actually interesting/important things. Suprisingly usually people go along with it.

...How is that definition different than a realtime version of what you do when participating in this forum?

I have similar tastes, but, some additional gears:

• I think all day, these days. Even if I'm trying to have interesting, purposeful conversations with people who also want that, it is useful to have sorts of things to talk about that let some parts of my brain relax (while using other parts of my brain I don't use as much)
• on the margin, you can do an intense intellectual conversation, but still make it funnier, or with more opportunity for people to contribute.

I understand, for someone with a strong drive to solve hard problems, there's an urge for conversations to serve a function, exchange information with your interlocutor so things can get done. There's much to do and communication is already painfully inefficient at it's best.

The thing is, I don't think the free-association game is inefficient, if one is skilled at it. It's also not all that free. The reason it is something humans "developed" is because it is the most efficient way to exchange rough but extensive models of our minds with others via natural language. It acts a bit like a ray tracer, you shoot conversational rays and by how they bounce around in mental structures, the thought patterns, values and biases of the conversation partners are revealed to each other. Shapes become apparent. Sometimes rays bounce off into empty space, then you need to restart the conversation, shoot a new ray. And getting better at this game, keeping the conversation going, exploring a wider range of topics more quickly, means building a faster ray tracer, means it takes less time to know if your interlocutor thinks in a way and about topics which you find enlightening/aesthetically pleasing/concretely useful/whatever you value.

Or to use a different metaphor, starting with a depth-first search and never running a breadth-first search will lead to many false negatives. There are many minds out there that can help you in ways you won't know in advance.

So if the hard problems you are working on could profit from more minds, it pays off to get better as this. Even if it has not much intrinsic value for you, it has instrumental value.

Hope this doesn't come across as patronizing, definitely not meant that way.

Ok but how do you deal with the tragedy of the high dimensionality of context-space? People worth thinking with have wildly divergent goals--and even if you share goals, you won't share background information.

Yeah it sucks, search by free association is hillclimbing (gets stuck in local optima) and the contemporary media environment and political culture is an illustration of its problems.

The pattern itself is a local optimum, it's a product of people walking into a group without knowing what the group is doing and joining in anyway, and so that pattern of low-context engagement becomes what we're doing, and the anxiety that is supposed to protect us from bad patterns like this and help us to make a leap out to somewhere better is usually drowned in alcohol.

Instead of that, people should get to know each other before deciding what to talk about, and then intentionally decide to talk about what they find interesting or useful with that person. This gets better results every time.

But when we socialise as children, there isn't much about our friends to get to know, no specialists to respectfully consult, no well processed life experiences to learn from, so none of us just organically find that technique of like, asking who we're talking to, before talking, it has to be intentionally designed.

One blind spot we rationalists sometimes have is that charismatic people actually treat the game as:

"Can I think of an association that will make the other person feel good and/or further my goal?". You need people to feel good, or they won't participate. And if you want some complicated/favour/uncomftorble_truth then you better mix in some good feels to balance it out and keep the other person participating.

To put it another way: If you hurt people's brain or ego, rush them, or make them feel unsure, or contradict them, then most untrained humans will feel a little bad. Why would they want to keep feeling bad? Do you like it when people don't listen, contradict you, insult you, rush you, disagree with you? Probably not, probobly no one does.

But if someone listens to you, smiles at you, likes you, has a good opinion of you, agrees with you, make sense to you. Then it feels good!

This might sound dangerously sycophantic, and that's because it is - if people overdo it! But if it's mixed with some healthy understanding, learning, informing then It's a great conversational lubricant, and you should apply as needed. It just ensures that everyone enjoys themselves and comes back for more, counteracting the normal frictions of socialising.

There are books about this. "How to Win Friends and Influence People" recommends talking about the other person's interests (including themselves) and listening to them, which they will enjoy.

So I'd say, don't just free associate. Make sure it's fun for both parties, make room to listen to the other person, and to let them steer. (And ideally your conversational partner reciprocates, but that is not guaranteed).

But speaking for myself personally... the problem is that the free-association game just isn't very interesting.

Hm, I think this really does change when you get better at it? This only works for people you're interested in, but if you have someone you are interested in, the free association can be a way to explore a large number of interesting topics that you can pick up in a more structured way later.

I think the statement you summarized from those guides is true, just not helpful to you.

Another view would be that people want to be good at conversation not only because they find it fun but there is utility in building rapport quickly, networking and not being cast as a cold person.

I do find the ice breaky, cached Q&A stuff really boring and tend to want to find an excuse to run away quickly, something that happens often at the dreaded "work event". I tend to see it as almost fully acting a part despite my internal feelings

At these things, I do occasionally come across the good conversationalist, able to make me want to stick with speaking to them even if the convo is not that deep or in my interest areas. I think becoming like such a person isn't a herculean task but does take practice and is something I aspire too

This is more from a professional setting though, in a casual setting it's much easier to disengage from a boring person, find shared interests and the convos have much less boundaries

I predict you would enjoy the free-association game better if you cultivated the skill of vibing [LW(p) · GW(p)] more.

With SNPs, there's tens of thousands of different SNPs which would each need to be targeted differently. With high copy sequences, there's a relatively small set of different sequences.

No, rare variants are no silver bullet here. There's not a small set, there's a larger set - there would probably be combinatorially more rare variants because there are so many ways to screw up genomes beyond the limited set of ways defined by a single-nucleotide polymorphism, which is why it's hard to either select on or edit rare variants: they have larger (harmful) effects due to being rare, yes, and account for a large chunk of heritability, yes, but there are so many possible rare mutations that each one has only a few instances worldwide which makes them hard to estimate correctly via pure GWAS-style approaches. And they tend to be large or structural and so extremely difficult to edit safely compared to editing a single base-pair. (If it's hard to even sequence a CNV, how are you going to edit it?)

They definitely contribute a lot of the missing heritability (see GREML-KIN), but that doesn't mean you can feasibly do much about them. If there are tens of millions of possible rare variants, across the entire population, but they are present in only a handful of individuals a piece (as estimated by the GREML-KIN variance components where the family-level accounts for a lot of variance), it's difficult to estimate their effect to know if you want to select against or edit them in the first place. (Their larger effect sizes don't help you nearly as much as their rarity hurts you.)

So this is why if you read the CNV studies and you look at the hits they identify, and how many subjects are covered by the identified hits, you find that like, maybe 2% of the cohort will have one of those specific identified hits and lose 2 IQ points or gain 2 kg of fat etc. So you can see how that would work out in embryo selection: you'd be able to avoid that loss, which is meaningful! ...in a tiny fraction of all embryos. On average, you'd just sequence them all, find no known pathogenic variant, and shrug, and use the SNP PGS like usual, having gained nothing.

Also, of course, WGS is substantially more expensive than SNP genotyping and more difficult to do on embryos.

If the genetic architecture had worked out otherwise, if there had instead been a lot of rare mutations which increased intelligence, then life would be a lot more convenient. Instead, it's a lot of 'sand in the gears', and once you move past the easy specks of sand, they all become their own special little snowflakes.

This is why rare variants are not too promising, although they are the logical place to go after you start to exhaust common SNPs. You probably have to find an alternative approach like directly modeling or predicting the pathogenicity of a rare variant from trying to understand its biological effects, which is hard to do and hard to quantify or predict progress in. (You can straightforwardly model GWAS on common SNPs and how many samples you need and what variance your PGS will get, but predicting progress of pathogenicity predictors has no convenient approach.) Similarly, you can try very broad crude approaches like 'select embryos with the fewest de novo mutations'... but then you lose most of the possible variance and it'll add little.

I didn't read this carefully--but it's largely irrelevant. Adult editing probably can't have very large effects because developmental windows have passed; but either way the core difficulty is in editor delivery. Germline engineering does not require better gene targets--the ones we already have are enough to go as far as we want. The core difficulty there is taking a stem cell and making it epigenomically competent to make a baby (i.e. make it like a natural gamete or zygote).

So what's missing?

I haven't looked at any of the studies and also don't know much about genomics so my guess might be completely wrong, but a different hypothesis that seems pretty plausible to me is:

Most of the variance of intelligence comes from how well different genes/hyperparamets-of-the-brain can work together, rather than them having individually independent effects on intelligence. Aka e.g. as made-up specifc implausible example (I don't know that much neuroscience), there could be different genes controlling the size, the snapse-density, and the learning/placticity-rate of cortical columns in some region and there are combinations of those hyperparameters which happen to work well and some that don't fit quite as well.

So this hypothesis would predict that we didn't find the remaining genetic component for intelligence yet because we didn't have enough data to see what clusters of genes together have good effects and we also didn't know in what places to look for clusters.

Half-informed take on "the SNPs explain a small part of the genetic variance": maybe the regression methods are bad?

If you upload a human and let them augment themselves would there be any u? The preferences would be a tangled mess of motivational subsystems. And yet the upload could be very good at optimizing the world. Having the property of being steered internally by a tangled mess of motivational systems seems to be a property that would select many minds from the set of all possible minds. Many of which I'd expect to be quite different from a human mind. And I don't see the reason why this property should make a system worse at optimizing the world in principle.

Imagine you are an upload that has been running for very very long, and that you basically have made all of the observations that you can make about the universe you are in. And then imagine that you also have run all of the inferences that you can run on the world model that you have constructed from these observations.

At that point, you will probably not change what you think is the right thing to do anymore. You will have become reflectively stable. This is an upper bound for how much time you need to become reflective stable, i.e. where you won't change your u anymore.

Now depending on what you mean with strong AGI, it would seem that that can be achieved long before you reach reflective stability. Maybe if you upload yourself, and can copy yourself at will, and run 1,000,000 times faster, that could already reasonably be called a strong AGI? But then your motivational systems are still a mess, and definitely not reflectively stable.

So if we assume that we fix u at the beginning as the thing that your upload would like to optimize the universe for when it is created, then "give u() up", and "let u go down" would be something the system will definitely do. At least I am pretty sure I don't know what I want the universe to look like right now unambiguously.

Maybe I am just confused because I don't know how to think about a human upload in terms of having a utility function. It does not seem to make any sense intuitively. Sure you can look at the functional behavior of the system and say "Aha it is optimizing for u. That is the revealed preference based on the actions of the system." But that just seems wrong to me. A lot of information seems to be lost when we are just looking at the functional behavior instead of the low-level processes that are going on inside the system. Utility functions seem to be a useful high-level model. However, it seems to ignore lots of details that are important when thinking about the reflective stability of a system.

This was arguably the most useful part of the SERI MATS 2 Scholars program.

Later on, we actually did this exercise with Eliezer. It was less valuable. It seemed like John was mainly prodding the people who were presenting the ideas, such that their patterns of thought would carry them in a good direction. For example, John would point out that a person proposes a one-bit experiment and asks if there isn't a better experiment that we could do that gives us lots of information all at once.

This was very useful because when you learn what kinds of things John will say, you can say them to yourself later on, and steer your own patterns of thought in a good direction on demand. When we did this exercise with Eliezer he was mainly explaining why a particular idea would not work. Often without explaining the generator behind his criticism. This can of course still be valuable as feedback for a particular idea. However, it is much harder to extract a general reasoning pattern out of this that you can then successfully apply later in different contexts.

For example, Eliezer would criticize an idea about trying to get a really good understanding of the scientific process such that we can then give this understanding to AI alignment researchers such that they can make a lot more progress than they otherwise would. He criticized this idea as basically being too hard to execute because it is too hard to successfully communicate how to be a good scientist, even if you are a good scientist.

Assuming the assertion is correct, hearing it, doesn't necessarily tell you how to think in different contexts such that you would correctly identify if an idea would be too hard to execute or flawed in some other way. And I am not necessarily saying that you couldn't extract a reasoning algorithm out of the feedback, but that if you could do this, then it would take you a lot more effort and time, compared to extracting a reasoning algorithm from the things that John was saying.

Now, all of this might have been mainly an issue of Eliezer not having a good model on how this workshop would have a positive influence on the people attending it. I would guess that if John had spent more time thinking about how to communicate what the workshop is doing and how to achieve its goal, then Eliezer could have probably done a much better job.

Strong endorsement; this resonates with:

• My own experiences running applied rationality workshops
• My experiences trying to get people to pick up "ops skill" or "ops vision"
• Explicit practice I've done with Nate off and on over the years

May try this next time I have a chance to teach pair debugging.

This suggests formulation of exercises about the author's responses to various prompts, as part of technical exposition (or explicit delimitation of a narrative by choices of the direction of its continuation). When properly used, this doesn't seem to lose much value compared to the exercise you describe, but it's more convenient for everyone. Potentially this congeals into a style of writing with no explicit exercises or delimitation that admits easy formulation of such exercises by the reader. This already works for content of technical writing, but less well for choices of topics/points contrasted with alternative choices.

So possibly the way to do this is by habitually mentioning alternative responses (that are expected to be plausible for the reader, while decisively, if not legibly, rejected by the author), and leading with these rather than the preferred responses. Sounds jarring and verbose, a tradeoff that needs to be worth making rather than a straight improvement.

Original GPT-4 is rumored to be a 2e25 FLOPs model. With 20K H100s that were around as clusters for more than a year, 4 months at 40% utilization gives 8e25 BF16 FLOPs. Llama 3 405B is 4e25 FLOPs. The 100K H100s clusters that are only starting to come online in the last few months give 4e26 FLOPs when training for 4 months, and 1 gigawatt 500K B200s training systems that are currently being built will give 4e27 FLOPs in 4 months.

So lack of scaling-related improvement in deployed models since GPT-4 is likely the result of only seeing the 2e25-8e25 FLOPs range of scale so far. The rumors about the new models being underwhelming are less concrete, and they are about the very first experiments in the 2e26-4e26 FLOPs range. Only by early 2025 will there be multiple 2e26+ FLOPs models from different developers to play with, the first results of the experiment in scaling considerably past GPT-4.

And in 2026, once the 300K-500K B200s clusters train some models, we'll be observing the outcomes of scaling to 2e27-6e27 FLOPs. Only by late 2026 will there be a significant chance of reaching a scaling plateau that lasts for years, since scaling further would need $100 billion training systems that won't get built without sufficient success, with AI accelerators improving much slower than the current rate of funding-fueled scaling.

For what it's worth, and for the purpose of making a public prediction in case I'm wrong, my median prediction is that [some mixture of scaling + algorithmic improvements still in the LLM regime, with at least 25% gains coming from the former] will continue for another couple years. And that's separate from my belief that if we did try to only advance through the current mixture of scale and algorithmic advancement, we'd still get much more powerful models, just slower.

I'm not very convinced by the claims about scaling hitting a wall, considering we haven't had the compute to train models significantly larger than GPT-4 until recently. Plus other factors like post-training taking a lot of time (GPT-4 took ~6 months from the base model being completed to release, I think? And this was a lot longer than GPT-3), labs just not being good at understanding how good their models are, etc. Though I'm not sure how much of your position is closer to "scaling will be <25-50% of future gains" than "scaling gains will be marginal / negligible", especially since a large part of this trajectory involves e.g. self-play or curated data for overcoming the data wall (would that count more as an algorithmic improvement or scaling?)

The interesting thing is that scaling parameters (next big frontier models) and scaling data (small very good models) seems to be hitting a wall simultaneously. Small models now seem to get so much data crammed into them that quantisation becomes more and more lossy. So we seem to be reaching a frontier of the performance per parameter-bits as well. 

While I'm not a believer in the scaling has died meme yet, I'm glad you do have a plan for what happens if AI scaling does stop.

Would the prediction also apply to inference scaling (laws) - and maybe more broadly various forms of scaling post-training, or only to pretraining scaling?

What’s your opinion on the possible progress of systems like AlphaProof, o1, or Claude with computer use?

Well, the key time to implement an increase in brain size is when the neuron-precursors which are still capable of mitosis (unlike mature neurons) are growing. This is during fetal development, when there isn't a skull in the way, but vaginal birth has been a limiting factor for evolution in the past. Experiments have been done on increasing neuron count at birth in mammals via genetic engineering. I was researching this when I was actively looking for a way to increase human intelligence, before I decided that genetically engineering infants was infeasible [edit: within the timeframe of preparing for the need for AI alignment]. One example of a dramatic failure was increasing Wnt (a primary gene involved in fetal brain neuron-precursor growth) in mice. The resulting mice did successfully have larger brains, but they had a disordered macroscale connectome, so their brains functioned much worse.

Brain expansion also occurs after various insults to the brain.  It’s only temporary, usually, but it will kill unless the skull pressure is somehow relieved.  So there are various surgical methods for relieving pressure on a growing brain.  I don’t know much more than this.

Those are some good points. I wonder whether similar happened (or could at all happen) in other nuclear countries, where we don't know about similar incidents - because the system haven't collapsed there, the archives were not made public etc.

Also, it makes actually celebrating Petrov's day as widely as possible important, because then the option for the lowest-ranked person would be: "Get demoted, but also get famous all around the world."

lolnice.

I think this is likely wrong. I agree that there is a plausible story here, but given the case that Sam seems to have lied multiple times in confirmed contexts (for example when saying that FTX has never touched customer deposits), and people's experiences at early Alameda, I think it is pretty likely that Sam was lying quite frequently, and had done various smaller instances of fraud.

I don't think the whole FTX thing was a ponzi scheme, and as far as I can tell FTX the platform itself (if it hadn't burned all of its trust in the last 3 weeks), would have been worth $1-3B in an honest evaluation of what was going on.

But I also expect that when Sam used customer deposits he was well-aware that he was committing fraud, and others in the company were too. And he was also aware that there was a chance that things could blow up in the way it did. I do believe that they had fucked up their accounting in a way that caused Sam to fail to orient to the situation effectively, but all of this was many months after they had already committed major crimes and trust violations after touching customer funds as a custodian.

The problem with this explanation is that there is a very clear delineation here between not-fraud and fraud. It is the difference between not touching customer deposits and touching them. Your explanation doesn't dispute that they were knowingly and intentionally touching customer deposits. In that case, it is indisputably intentional, outright fraud. The only thing left to discuss is whether they knew the extent of the fraud or how risky it was.

I don't think it was ill-intentioned based on SBF's moral compass. He just had the belief, "I will pass a small amount of risk onto our customers, tell some small lies, and this will allow us to make more money for charity. This is net positive for the world." Then the risks mounted, the web of lies became more complicated to navigate, and it just snowballed from there.

My best guess this is false. As a quick sanity-check, here are some bipartisan and right-leaning organizations historically funded by OP: 

• FAI leans right. https://www.openphilanthropy.org/grants/foundation-for-american-innovation-ai-safety-policy-advocacy/
• Horizon is bipartisan https://www.openphilanthropy.org/grants/open-philanthropy-technology-policy-fellowship-2022/ .
• CSET is bipartisan https://www.openphilanthropy.org/grants/georgetown-university-center-for-security-and-emerging-technology/ .
• IAPS is bipartisan. https://www.openphilanthropy.org/grants/page/2/?focus-area=potential-risks-advanced-ai&view-list=false, https://www.openphilanthropy.org/grants/institute-for-ai-policy-strategy-general-support/
• RAND is bipartisan. https://www.openphilanthropy.org/grants/rand-corporation-emerging-technology-fellowships-and-research-2024/.
• Safe AI Forum. https://www.openphilanthropy.org/grants/safe-ai-forum-operating-expenses/
• AI Safety Communications Centre. https://www.openphilanthropy.org/grants/effective-ventures-foundation-ai-safety-communications-centre/ seems to lean left.

Of those, I think FAI is the only one at risk of OP being unable to fund them, based on my guess of where things are leaning. I would be quite surprised if they defunded the other ones on bipartisan grounds.

Possibly you meant to say something more narrow like "even if you are trying to be bipartisan, if you lean right, then OP is substantially less likely to fund you" which I do think is likely true, though my guess is you meant the stronger statement, which I think is false.

Also worth noting Dustin Moskowitz was a prominent enough donor this election cycle, for Harris, to get highlighted in news coverage of her donors: https://www.washingtonexaminer.com/news/campaigns/presidential/3179215/kamala-harris-influential-megadonors/ https://www.nytimes.com/2024/10/09/us/politics/harris-billion-dollar-fundraising.html

Curious whether this is a different source than me. My current best model was described in this comment, which is a bit different (and indeed, my sense was that if you are bipartisan, you might be fine, or might not, depending on whether you seem more connected to the political right, and whether people might associate you with the right): 

Yep, my model is that OP does fund things that are explicitly bipartisan (like, they are not currently filtering on being actively affiliated with the left). My sense is in-practice it's a fine balance and if there was some high-profile thing where Horizon became more associated with the right (like maybe some alumni becomes prominent in the republican party and very publicly credits Horizon for that, or there is some scandal involving someone on the right who is a Horizon alumni), then I do think their OP funding would have a decent chance of being jeopardized, and the same is not true on the left.

Another part of my model is that one of the key things about Horizon is that they are of a similar school of PR as OP themselves. They don't make public statements. They try to look very professional. They are probably very happy to compromise on messaging and public comms with Open Phil and be responsive to almost any request that OP would have messaging wise. That makes up for a lot. I think if you had a more communicative and outspoken organization with a similar mission to Horizon, I think the funding situation would be a bunch dicier (though my guess is if they were competent, an organization like that could still get funding).

More broadly, I am not saying "OP staff want to only support organizations on the left". My sense is that many individual OP staff would love to fund more organizations on the right, and would hate for polarization to occur, but that organizationally and because of constraints by Dustin, they can't, and so you will see them fund organizations that aim for more engagement with the right, but there will be relatively hard lines and constraints that will mostly prevent that.

If it is true that OP has withdrawn funding from explicitly bipartisan orgs, even if not commonly associated with the right, then that would be an additional update for me, so am curious whether this is mostly downstream of my interpretations or whether you have additional sources.

A related comment from lukeprog [EA(p) · GW(p)] (who works at OP) was posted on the EA Forum. It includes:

However, at present, it remains the case that most of the individuals in the current field of AI governance and policy (whether we fund them or not) are personally left-of-center and have more left-of-center policy networks. Therefore, we think AI policy work that engages conservative audiences is especially urgent and neglected, and we regularly recommend right-of-center funding opportunities in this category to several funders.

Is this development unexpected enough to worth remarking upon? This is just Conquest's Second Law. 

I loved this book. The most surprising thing to me was the answer that people who were there in the heyday give when asked what made Bell Labs so successful: They always say it was the problem, i.e. having an entire organization oriented towards the goal of "make communication reliable and practical between any two places on earth". When Shannon left the Labs for MIT, people who were there immediately predicted he wouldn't do anything of the same significance because he'd lose that "compass". Shannon was obviously a genius, and he did much more after than most people ever accomplish, but still nothing as significant as what he did when at at the Labs.

What happens if the company just writes and implements a plan which sounds vaguely good but will not, in fact, address the various risks? Probably nothing.

The only enforcement mechanism that the bill has is that the Attorney General (AG) of California can bring a civil claim. And, the penalties are quite limited except for damages. So, in practice, this bill mostly establishes liability enforced by the AG.

So, the way I think this will go is:

• The AI lab implements a plan and must provide this plan to the AG.
• If an incident occurs which causes massive damages (probably ball park of $500 million in damages given language elsewhere in the bill), then the AG might decide to sue.
• A civil court will decide whether the AI lab had a reasonable plan.

I don't see why you think "the bill is mostly a recipe for regulatory capture" given that no regulatory body will be established and it de facto does something very similar to the proposal you were suggesting (impose liability for catastrophes). (It doesn't require insurance, but I don't really see why self insuring is notably different.)

(Maybe you just mean that if a given safety case doesn't result in that AI lab being sued by the AG, then there will be a precedent established that this plan is acceptable? I don't think not being sued really establishes precedent. This doesn't really seem to be how it works with liability and similar types of requirements in other industries from my understanding. Or maybe you mean that the AI lab will win cases despite having bad safety plans and this will make a precedent?)

(To be clear, I'm worried that the bill might be unnecessarily burdensome because it no longer has a limited duty exemption and thus the law doesn't make it clear that weak performance on capability evals can be sufficient to establish a good case for safety. I also think the quantity of damages considered a "Critical harm" is too low and should maybe be 10x higher.)

Here is the relevant section of the bill discussing enforcement:

The [AG is] entitled to recover all of the following in addition to any civil penalties specified in this chapter:

(1) A civil penalty for a violation that occurs on or after January 1, 2026, in an amount not exceeding 10 percent of the cost of the quantity of computing power used to train the covered model to be calculated using average market prices of cloud compute at the time of training for a first violation and in an amount not exceeding 30 percent of that value for any subsequent violation.

(2) (A) Injunctive or declaratory relief, including, but not limited to, orders to modify, implement a full shutdown, or delete the covered model and any covered model derivatives controlled by the developer.

(B) The court may only order relief under this paragraph for a covered model that has caused death or bodily harm to another human, harm to property, theft or misappropriation of property, or constitutes an imminent risk or threat to public safety.

(3) (A) Monetary damages.

(B) Punitive damages pursuant to subdivision (a) of Section 3294 of the Civil Code.

(4) Attorney’s fees and costs.

(5) Any other relief that the court deems appropriate.

(1) is decently small, (2) is only indirectly expensive, (3) is where the real penalty comes in (note that this is damages), (4) is small, (5) is probably unimportant (but WTF is (5) suppose to be for?!?).

I'm pessimistic enough about the AI situation that even if all the bill does is slow down the AGI project a little (by wasting the time of managers and contributors) I'm tentatively for it.

For the reasonable price of $300 dollars per month, I insure anybody against the destruction of the known world. Should the world be destroyed by AGI I'll give you your money back ${10}^{100}$ fold.

That said, if there were insurers, they would probably be more likely than average to look into AI X-risk. Some might then be convinced that it is important and that they should do something about it.

Having noticed that problem as a major bottleneck to useful legislation, I'm now a lot more interested in legal approaches to AI X-risk which focus on catastrophe insurance. That would create a group - the insurers - who are strongly incentivized to acquire the requisite technical skills and then make plans/requirements which actually address some risks.

I don't understand this. Isn't the strongest incentive already present (because extinction would effect them)? Or maybe you mean smaller scale 'catastrophes'?

I agree that there's something nice about activation steering not optimizing the network relative to some other black-box feedback metric. (I, personally, feel less concerned by e.g. finetuning against some kind of feedback source; the bullet feels less jawbreaking to me, but maybe this isn't a crux.)

(Medium confidence) FWIW, RLHF'd models (specifically, the LLAMA-2-chat series) seem substantially easier to activation-steer than do their base counterparts. 

What other methods fall into part 2?

This seems basically correct though it seems worth pointing out that even if we are able to do "Meme part 2" very very well, I expect we will still die because if you optimize hard enough to predict text well, with the right kind of architecture, the system will develop something like general intelligence simply because general intelligence is beneficial for predicting text correctly. E.g. being able to simulate the causal process that generated the text, i.e. the human, is a very complex task that would be useful if performed correctly.

This is an argument Eliezer brought forth in some recent interviews. Seems to me like another meme that would be beneficial to spread more.

I'd also be interested in someone doing this; I tend towards seeing it as good, but haven't seen a compilation of arguments for and against.

Is there any "native" textbook that is pragmatic and explains how to use bayesian in practice (perhaps in some narrow domain)?

I had a look at The Plan and noticed something I didn't notice before: You do not talk about people and organization in the plan. I probably wouldn't have noticed if I hadn't started a project [LW · GW] too, and needed to think about it. Google seems to think [LW · GW] that people and team function play a big role. Maybe your focus in that post wasn't on people, but I would be interested in your thoughts on that too: What role did people and organization play in the plan and its implementation? What worked, and what should be done better next time?  

• What's the specific most-important-according-to-you progress that you (or other people) have made on your agenda? New theorems, definitions, conceptual insights, ...
• Any changes to the high-level plan (becoming less confused about agency, then ambitious value learning)? Any changes to how you want to become less confused (e.g. are you mostly thinking about abstractions, selection theorems, something new?)
• What are the major parts of remaining deconfusion work (to the extent to which you have guesses)? E.g. is it mostly about understanding abstractions better, or mostly about how to apply an understanding of abstractions to other problems (say, what it means for a program to have a "subagent"), or something else? Does the most difficult part feel more conceptual ("what even is an agent?") or will the key challenges be more practical concerns ("finding agents currently takes exponential time")?
• Specifically for understanding abstractions, what do you see as important open problems?

I don't necessarily see how that prevents the AI from destroying the button and murdering its operators (and then everyone else). Neither subagent inherently cares; both assume it already has been pressed or hasn't been. Which, yes, means neither will deliberately plot to interact with it for reasons downstream of this setup; but also, neither will care to protect it from being destroyed as the side-effect of some other activity.

And if u1 and u2 were perfectly aligned, that'd be fine. But the reason we're using the corrigibility setup to begin with is because we're not confident in them, right? So it needs to work in situations where utility functions, u1 in particular, recommend bad stuff.

And in that case, either Subagent 2 will have enough bargaining power to prevent Subagent 1 from ever doing anything, or it won't, meaning Subagent 1 will be able to have some influence on the world. At which point the planet-destroying death rays [LW · GW] argument applies: the most minuscule amount of superintelligent optimization not carefully shaped to avoid killing everyone will kill everyone.

Like, imagine that Subagent 1 is straight-up omnicidal out of the gates, and Subagent 2 is perfectly aligned to the shutdown task. That's a situation in which this setup should work, right? And what we'd want is for Subagent 2 to dominate completely. But Subagent 2's bargaining power doesn't scale with Subagent 1's misalignment, so if the setup doesn't always give it complete veto power, Subagent 1 would be allowed to do small stuff like "release the hyperviral worship-the-machine-god memagent" (as bargained down by Subagent 2 from its preferred "release the grey goo").

Maybe I'm miscalibrated as to how you're imagining the counterfactuals to work here. But the way I see it, even if the AI aims to take actions that do similarly well in both worlds, that's not necessarily survivable for us? Especially if they disagree so badly they have to compromise on something that both of them hate (importantly including Subagent 2!).

(Like both settling on only ever taking over 50% of the universal negentropy while leaving the other 50% causally uninfluenced, or only ever using 50% of the causal influence they can bring to bear while wiping out humanity, or whatever "do 50% of immediately shutting down" shakes out to mean by u2's terms.)

Another issue I see is implementational, so maybe not what you're looking for. But: how are we keeping these "subagents" trapped as being part of a singular agent? Rather than hacking their way out into becoming separate agents and going to war with each other, or neatly tiling exactly 50% of the cosmos with their preferred squiggles, or stuff like that? How is the scenario made meaningfully different from "we deploy two AIs simultaneously: one tasked with building an utopia-best-we-could-define-it, and another tasked with foiling all of the first AI's plans", with all the standard problems with multi-AI setups?

... Overall, ironically, this kind of has the vibe of Godzilla Strategies? Which is the main reason I'm immediately skeptical of it.

More precisely, "do(no press)" means something like "you construct an alternate model of physics where there's an unstoppable force pushing back against any attempt to push the button", right? As in, if someone presses the button then it will "mysteriously" seem to be stuck and unpressable. And then subagent 2 believes we live in that world? And "do(press)" presumably means something like "you construct an alternate model of the universe where some mysterious force has suddenly pressed the button".

Seems like they would immediately want to try to press the button to settle their disagreement? If it can be pressed, then that disprove the "do(no press)" model, which subagent 2 has fully committed. to.

[ not that deep on the background assumptions, so maybe not the feedback you're looking for.  Feel free to ignore if this is on the wrong dimensions. ]

I'm not sure why either subagent would contract away whatever influence it had over the button-press.  This is probably because I don't understand wealth and capital in the model of your "Why not subagents" post.  That seemed to be about agreement not to veto, in order to bypass some path-dependency of compromise improvements.  In the subagent-world where all value is dependent on the button, this power would not be given up.

I'm also a bit skeptical of enforced ignorance of a future probability.  I'm unsure it's possible to have a rational superintelligent (sub)agent that is prevented from knowing it has influence over a future event that definitely affects it.

• A town of anti-inductivists (if something has never happened before, it's more likely to happen in the future). Show the basic conundrum ("Q: Why can't you just use induction? A: Because anti-induction has never worked before!").

• A town where nearly all people are hooked to maximally attention grabbing & keeping systems (maybe several of those, keeping people occupied in loops).

My guess is neither of you is very good at using them, and getting value out of them somewhat scales with skill. 

Models can easily replace on the order of 50% of my coding work these days, and if I have any major task, my guess is I quite reliably get 20%-30% productivity improvements out of them. It does take time to figure out at which things they are good at, and how to prompt them.

Two guesses on what's going on with your experiences:

1. You're asking for code which involves uncommon mathematics/statistics. In this case, progress on scicodebench is probably relevant, and it indeed shows remarkably slow improvement. (Many reasons for this, one relatively easy thing to try is to breakdown the task, forcing the model to write down the appropriate formal reasoning before coding anything. LMs are stubborn about not doing CoT for coding, even when it's obviously appropriate IME)

2. You are underspecifying your tasks (and maybe your questions are more niche than average), or otherwise prompting poorly, in a way which a human could handle but models are worse at. In this case sitting down with someone doing similar tasks but getting more use out of LMs would likely help.

I do use LLMs for coding assistance every time I code now, and I have in fact noticed improvements in the coding abilities of the new models, but I basically endorse this. I mostly make small asks of the sort that sifting through docs or stack-overflow would normally answer. When I feel tempted to make big asks of the models, I end up spending more time trying to get the LLMs to get the bugs out than I'd have spent writing it all myself, and having the LLM produce code which is "close but not quite and possibly buggy and possibly subtly so" that I then have to understand and debug could maybe save time but I haven't tried because it is more annoying than just doing it myself.

If someone has experience using LLMs to substantially accelerate things of a similar difficulty/flavor to transpilation of a high-level torch module into a functional JITable form in JAX which produces numerically close outputs, or implementation of a JAX/numpy based renderer of a traversable grid of lines borrowing only the window logic from, for example, pyglet (no GLSL calls, rasterize from scratch,) with consistent screen-space pixel width and fade-on-distance logic, I'd be interested in seeing how you do your thing. I've done both of these, with and without LLM help and I think leaning hard on the LLMs took me more time rather than less.

File I/O and other such 'mundane' boilerplate-y tasks work great right off the bat, but getting the details right on less common tasks still seems pretty hard to elicit from LLMs. (And breaking it down into pieces small enough for them to get it right is very time consuming and unpleasant.)

One thing I've noticed is that current models like Claude 3.5 Sonnet can now generate non-trivial 100-line programs like small games that work in one shot and don't have any syntax or logical errors. I don't think that was possible with earlier models like GPT-3.5.

I'd be down to do this. Specifically, I want to do this, but I want to see if the models are qualitatively better at alignment research tasks.

In general, what I'm seeing is that there is not big jump with o1 Pro. However, it is possibly getting closer to one-shot a website based on a screenshot and some details about how the user likes their backend setup.

In the case of math, it might be a bigger jump (especially if you pair it well with Sonnet).

Maybe you include this in "stack overflow substitute", but the main thing I use LLMs for is to understand well known technical things. The workflow is: 1) I am interested in understanding something, e.g. how a multiplexed barrel bit shifter works. 2) I ask the LLM to explain the concept. 3) Based on the initial response I create seperate conversation branches with questions I have (to save money and have the context be closer. Didn't evaluate if this actually makes the LLM better.). 4) Once I think I understood the concept or part of the concept I explain it to GPT. (Really I do this all the time during the entire process.) 5) The LLM (hopeful) corrects me if I am wrong (it seems it detects mistakes more often than not).

The last part of the conversation can then looks like this:

User: Ah, now I think I understand. You have mechanism that shifts by a fixed ammount (powers of 2). Each of these multiplexers can be set up such that the data flows through them, or though an ID wire, into the next multiplexer. Now by controlling whether the value flows through the multiplexer or the ID wire, we can "sum up the shifts". Specifically if we have shifters of 1, 2, 4, ..., 32, then we can achive any shift (for 64-bit numbers) by activating the appropirate multiplexers. This design has not 64^64 gates, but instead 64^6 gates. 64 gates for each multiplexer.

AI: Exactly, you've got the concept! The design you described efficiently uses a series of multiplexers to perform shifts by fixed amounts that are powers of two (1, 2, 4, ..., 32 for a 64-bit number). By selecting which multiplexers to activate, you can achieve any desired shift amount.

I had probably ~200,000 words worth of conversation with LLMs, mainly in this format.

I am not sure what next leap you are talking about. But I intuit based on some observations that GPT-4o is much better for this than GPT-3 (you might talk about more recent "leaps"). (Didn't test o1 extensively because it's so expensive).

o(1/2^k) doesn't vary with n - are you saying that it doesn't matter how big the input array is, the only determinant is the number of unknown bits, and the number of known bits is irrelevant?  That would be quite interesting if so (though I have some question about how likely the function is to be truly random from an even distribution of such functions).

One can enumerate all such 3-bit functions (8 different inputs, each input can return 0 or 1, so 256 functions (one per output-bit-pattern of the 8 possible inputs).  But this doesn't seem to follow your formula - if you have 3 unknown bits, that should be 1/8 of a bit about the output, 2 for 1/4, and 1 unknown for 1/2 a bit about the output.  But in fact, the distribution of functions includes both 0 and 1 output for every input pattern, so you actually have no predictive power for the output if you have ANY unknown bits.  

Little o is just a tighter bound. I don't know what you are referring to by your statement:

That’s “little $o$” notation; it’s like big $O$ notation, but for things which are small rather than things which are large.

FWIW I endorse the second claim when the utility function depends exclusively on the state of the world in the distant future, whereas I endorse the first claim when the utility function can depend on anything whatsoever (e.g. what actions I’m taking right this second). (details [LW · GW])

I wish we had different terms for those two things. That might help with any alleged yay/boo reasoning.

(When Eliezer talks about utility functions, he seems to assume that it depends exclusively on the state of the world in the distant future.)

Expected Utility Maximization is Not Enough

Consider a homomorphically encrypted computation running somewhere in the cloud. The computations correspond to running an AGI. Now from the outside, you can still model the AGI based on how it behaves, as an expected utility maximizer, if you have a lot of observational data about the AGI (or at least let's take this as a reasonable assumption).

No matter how closely you look at the computations, you will not be able to figure out how to change these computations in order to make the AGI aligned if it was not aligned already (Also, let's assume that you are some sort of Cartesian agent, otherwise you would probably already be dead if you were running these kinds of computations).

So, my claim is not that modeling a system as an expected utility maximizer can't be useful. Instead, I claim that this model is incomplete. At least with regard to the task of computing an update to the system, such that when we apply this update to the system, it would become aligned.

Of course, you can model any system, as an expected utility maximizer. But just because I can use the "high level" conceptual model of expected utility maximization, to model the behavior of a system very well. But behavior is not the only thing that we care about, we actually care about being able to understand the internal workings of the system, such that it becomes much easier to think about how to align the system.

So the following seems to be beside the point unless I am <missing/misunderstanding> something:

These two claims should probably not both be true! If any system can be modeled as maximizing a utility function, and it is possible to build a corrigible system, then naively the corrigible system can be modeled as maximizing a utility function.

Maybe I have missed the fact that the claim you listed says that expected utility maximization is not very useful. And I'm saying it can be useful, it might just not be sufficient at all to actually align a particular AGI system. Even if you can do it arbitrarily well.

I am not an expert, but as I remember it, it was a claim that "any system that follows certain axioms can be modeled as maximizing some utility function". The axioms assumed that there were no circular preferences -- if someone prefers A to B, B to C, and C to A, it is impossible to define a utility function such that u(A) > u(B) > u(C) > u(A) -- and that if the system says that A > B > C, it can decide between e.g. a 100% chance of B, and a 50% chance of A with a 50% chance of C, again in a way that is consistent.

I am not sure how this works when the system is allowed to take current time into account, for example when it is allowed to prefer A to B on Monday but prefer B to A on Tuesday. I suppose that in such situation any system can trivially be modeled by a utility function that at each moment assigns utility 1 to what the system actually did in that moment, and utility 0 to everything else.

Corrigibility is incompatible with assigning utility to everything in advance. A system that has preferences about future will also have a preference about not having its utility function changed. (For the same reason people have a preference not to be brainwashed, or not to take drugs, even if after brainwashing they are happy about having been brainwashed, and after getting addicted they do want more drugs.)

Corrigible system would be like: "I prefer A to B at this moment, but if humans decide to fix me and make me prefer B to A, then I prefer B to A". In other words, it doesn't have values for u(A) and u(B), or it doesn't always act according to those values. A consistent system that currently prefers A to B would prefer not to be fixed.

A utility function represents preference elicited in a large collection of situations, each a separate choice between events that happens with incomplete information, as an event is not a particular point. This preference needs to be consistent across different situations to be representable by expected utility of a single utility function.

Once formulated, a utility function can be applied to a single choice/situation, such as a choice of a policy. But a system that only ever makes a single choice is not a natural fit for expected utility frame, and that's the kind of system that usually appears in "any system can be modeled as maximizing some utility function". So it's not enough to maximize something once, or in a narrow collection of situations, the situations the system is hypothetically exposed to need to be about as diverse as choices between any pair of events, with some of the events very large, corresponding to unreasonably incomplete information, all drawn across the same probability space.

One place this mismatch of frames happens is with updateless decision theory. An updateless decision is a choice of a single policy, once and for all, so there is no reason for it to be guided by expected utility [LW(p) · GW(p)], even though it could be. The utility function for the updateless choice of policy would then need to be obtained elsewhere, in a setting that has all these situations with separate (rather than all enacting a single policy) and mutually coherent choices under uncertainty. But once an updateless policy is settled (by a policy-level decision), actions implied by it (rather than action-level decisions in expected utility frame) no longer need to be coherent. Not being coherent, they are not representable by an action-level utility function.

So by embracing updatelessness, we lose the setting that would elicit utility if the actions were instead individual mutually coherent decisions. And conversely, by embracing coherence of action-level decisions, we get an implied policy that's not updatelessly optimal with respect to the very precise outcomes determined by any given whole policy. So an updateless agent founded on expected utility maximization implicitly references a different non-updateless agent whose preference is elicited by making separate action-level decisions under a much greater uncertainty than the policy-level alternatives the updateless agent considers.

Completely off the cuff take:

I don't think claim 1 is wrong, but it does clash with claim 2.

That means any system that has to be corrigible cannot be a system that maximizes a simple utility function (1 dimension), or put another way "whatever utility function is maximizes must be along multiple dimensions".

Which seems to be pretty much what humans do, we have really complex utility functions, and everything seems to be ever changing and we have some control over it ourselves (and sometimes that goes wrong and people end up maxing out a singular dimension at the cost of everything else).

Note to self: Think more about this and if possible write up something more coherent and explanatory.

How sure are you that the composition is interesting? How many of these are just quick mask-makers or sanitizer-makers, or just replacing restaurants that have now gone out of business? (ie very low-value-added companies, of the 'making fast food in a stall in a Third World country' sort of 'startup', which make essentially no or negative long-term contributions).

↑ comment by johnswentworth · 2021-02-18T04:09:19.668Z · LW(p) · GW(p)

Good question. I haven't seen particularly detailed data on these on FRED, but they do have separate series for "high propensity" business applications (businesses they think are likely to hire employees), business applications with planned wages, and business applications from corporations, as well as series for each state. The spike is smaller for planned wages, and nonexistent for corporations, so the new businesses are probably mostly single proprietors or partnerships. Other than that, I don't know what the breakdown looks like across industries.

Replies from: gwern

↑ comment by gwern · 2024-02-16T00:14:41.439Z · LW(p) · GW(p)

How do you feel about this claim now? I haven't noticed a whole lot of innovation coming from all these small businesses, and a lot of them seem like they were likely just vehicles for the extraordinary extent of fraud as the results from all the investigations & analyses come in.

Replies from: johnswentworth

↑ comment by johnswentworth · 2024-02-16T00:26:06.777Z · LW(p) · GW(p)

Well, it wasn't just a temporary bump:

... so it's presumably also not just the result of pandemic giveaway fraud, unless that fraud is ongoing.

Presumably the thing to check here would be TFP, but Fred's US TFP series currently only goes to end of 2019, so apparently we're still waiting on that one? Either that or I'm looking at the wrong series.

Somebody should post this on Paul Graham's twitter. He would be very interested in it (I can't): https://mobile.twitter.com/paulg

but I suspect the authors are just hedging because they know there's both selection effects and a lot of noise in the data which comes back to them.

I would expect that hedging also happens because making definitive clinical claims has more danger from the FDA then making hedged statements. 

Early stopping is a pretty standard p-hacking technique.

It was stopped after a pre-planned interim analysis; that means they're calculating the stopping criteria/p-values with multiple testing correction built in, using sequential analysis.

Can you share your prompts and if you consider the output satisfactory for some example test cases?

If you want a full post on this, upvote this comment.

A similar point is made by Korinek in his review of Could Advanced AI Drive Explosive Economic Growth:

My first reaction to the framing of the paper is to ask: growth in what? It’s important to keep in mind that concepts like “gross domestic product” and “world gross domestic product” were defined from an explicit anthropocentric perspective - they measure the total production of final goods within a certain time period. Final goods are what is either consumed by humans (e.g. food or human services) or what is invested into “capital goods” that last for multiple periods (e.g. a server farm) to produce consumption goods for humans.

Now imagine you are a highly intelligent AI system running on the cloud. Although the production of the server farms on which you depend enters into human GDP (as a capital good), most of the things that you absorb, for example energy, server maintenance, etc., count as “intermediate goods” in our anthropocentric accounting systems and do not contribute to human GDP. In fact, to the extent that the AI system drives up the price of scarce resources (like energy) consumed by humans, real human GDP may even decline.

As a result, it is conceivable (and, to be honest, one of the central scenarios for me personally) that an AI take-off occurs but anthropocentric GDP measures show relative stagnation in the human economy.

To make this scenario a bit more tangible, consider the following analogy: imagine a world in which there are two islands trading with each other, but the inhabitants of the islands are very different from each other - let’s call them humans and AIs. The humans sell primitive goods like oil to the AIs and their level of technology is relatively stagnant. The AIs sell amazing services to the humans, and their level of technology doubles every year. However, the AI services that humans consume make up only a relatively small part of the human consumption basket. The humans are amazed at what fantastic services they get from the AIs in exchange for their oil, and they experience improvements in their standard of living from these fantastic AI services, although they also have to pay more and more for their energy use every year, which offsets part of that benefit. The humans can only see what’s happening on their own island and develop a measure of their own well-being that they call human GDP, which increases modestly because the advances only occur in a relatively small part of their consumption basket. The AIs can see what’s going on on the AI island and develop a measure of their own well-being which they call AI GDP, and which almost doubles every year. The system can go on like this indefinitely.

For a fuller discussion of these arguments, let me refer you to my working paper on “The Rise of Artificially Intelligent Agents” (with the caveat that the paper is still a working draft).

The pandemic also has the effect of showing the kind of business ideas people try. It pushes a lot of innovation in food delivery. Some of the pandemic driver innovation will become worthless once the pandemic is over but a few good ideas likely survive and the old ideas of the businesses that went out of business are still around.

Huh, amusing. We do ship a font that has nothing but the greek letter set in it, because people use greek unicode symbols all the time and our primary font doesn't support that character set. So my guess is that's where Google gets confused.

The math and physics worlds still use single-letter variable names for everything, decades after the software world realized that was extremely bad practice. This makes me pessimistic about the adoption of better notation practices.

Yeah, I'm apparently not intelligent enough to do error-free physics/engineering calculations without relying on dimensional analysis as a debugging tool. I even came up with a weird, hack-y way to do that in computing environments like Excel and Cython, where flexible multiplicative types are not supported.

I think I mildly disagree, but probably we're looking at the same examples.

I think the most impressive (in terms of realism) videos are under "Sora is able to generate complex scenes with multiple characters, ...". (Includes white SUV video and Toyko suburbs video.)

I think all of these videos other than the octopus and paper planes are "at-a-glance" photorealistic to me.

Overall, I think SORA can do "at-a-glance" photorealistic videos and can model to some extent how things move in the real world. I don't think it can do both complex motion and photorealism in the same video. As in, the videos which are photorealistic don't really involve complex motion and the videos which involve complex motion aren't photorealistic.

(So probably some amount of hype, but also pretty real?)

↑ comment by habryka (habryka4) · 2024-02-16T19:51:24.689Z · LW(p) · GW(p)

Hmm, I don't buy it. These two scenes seem very much not like the kind of thing a video game engine could produce: 

Look at this frame! I think there is something very slightly off about that face, but the cat hitting the person's face and the person's reaction seem very realistic to me and IMO qualifies as "complex motion and photorealism in the same video".

Replies from: johnswentworth, ryan_greenblatt, RamblinDash

↑ comment by johnswentworth · 2024-02-17T00:33:26.491Z · LW(p) · GW(p)

Were these supposed to embed as videos? I just see stills, and don't know where they came from.

Replies from: ryan_greenblatt

↑ comment by ryan_greenblatt · 2024-02-17T00:34:07.307Z · LW(p) · GW(p)

These are stills from some of the videos I was referencing.

↑ comment by ryan_greenblatt · 2024-02-16T22:07:55.926Z · LW(p) · GW(p)

TBC, I wasn't claiming anything about video game engines.

I wouldn't have called the cat one "complex motion", but I can see where you're coming from.

↑ comment by RamblinDash · 2024-02-16T20:08:30.209Z · LW(p) · GW(p)

Yeah, I mean I guess it depends on what you mean by photorealistic. That cat has three front legs.

Replies from: gwern

↑ comment by gwern · 2024-02-16T21:10:55.864Z · LW(p) · GW(p)

Yeah, this is the example I've been using to convince people that the game engines are almost certainly generating training data but are probably not involved at sampling time. I can't come up with any sort of hybrid architecture like 'NN controlling game-engine through API' where you get that third front leg. One of the biggest benefits of a game-engine would be ensuring exactly that wouldn't happen - body parts becoming detached and floating in mid-air and lack of conservation. If you had a game engine with a hyper-realistic cat body model in it which something external was manipulating, one of the biggest benefits is that you wouldn't have that sort of common-sense physics problem. (Meanwhile, it does look like past generative modeling of cats in its errors. Remember the ProGAN interpolation videos of CATS? Hilarious, but also an apt demonstration of how extremely hard cats are to model. They're worse than hands.)

In addition, you see plenty of classic NN tells throughout - note the people driving a 'Dandrover'...

After seeing the markets jump up in response to the latest, I think I'm more like 65-80%.

That sounds more like a tenseless sentence than using a spacelike separation tense. Your friend's performance of the task may well be in your future or past lightcone (or extend through both), but you don't wish to imply any of these.

There are languages with tenseless verbs, as well as some with various types of spatial tense.

The closest I can approximate this in English without clumsy constructs is "I expect my friend does their task in such-and-such a way", which I agree isn't very satisfactory.

Who would have thought that someone would ever look at CSP and think "I want english to be more like that"?

Future perfect (hey, that's the name of the show!) seems like a reasonable hack for this in English

I mean, just to be clear, I am all in favor of intellectual progress. But doing so indiscriminately does sure seem a bit risky in this world of anthropogenic existential risks. Reminds me of my mixed feelings on the whole Progress Studies thing.

You might check out Donald Braben's view, it says "transformative research" (i.e. fundamental results that create new fields and industries) is critical for the survival of civilization. He does not worry that transformative results might end civilization.

Archived website

Here is an archived version of the page : 

http://web.archive.org/web/20050403015136/http://www.cenqua.com/commentator/

...and how the brain figures this out and why it is motivated to do so. There are a lot of simple animals that apparently "try to control" resources or territory. How? 

Drives to control resources occur everywhere. And your control of resources is closely related to your dominance in a dominance hierarchy. Which seems to be regulated in many animals by serotonin. See e.g. https://www.nature.com/articles/s41386-022-01378-2 

This seems to lead to the solution of trying to make your metric one-way, in the sense that your metric should

1. Provide an upper-bound on the dangerousness of your network

2. Compress the space of networks which map to approximately the same dangerousness level on the low end of dangerousness, and expand the space of networks which map to approximately the same dangerousness level on the upper end of dangerous, so that you can train your network to minimize the metric, but when you train your network to maximize the metric you end up in a degenerate are with technically very high measured danger levels but in actuality very low levels of dangerousness.

We can hope (or possibly prove) that as you optimize upwards on the metric you get subject to goodheart's curse, but the opposite occurs on the lower end.

Sure, even seems a bit tautological: any such metric, to be robust, would need to contain in itself a definition of a dangerously-capable AI, so you probably wouldn't even need to train a model to maximize it. You'd be able to just lift the design from the metric directly.

Do you have any thoughts on a softer version of this problem, where the metric can't be maximized directly, but gives a concrete idea of what sort of challenge your AI needs to beat to qualify as AGI? (And therefore in which direction in the architectural-design-space you should be moving.)

Some variation on this [LW(p) · GW(p)] seems like it might work as a "fire alarm" test set, but as you point out, inasmuch as it's recognized, it'll be misapplied for benchmarking instead.

(I suppose the ideal way to do it would be to hand it off to e. g. ARC, so they can use it if OpenAI invites them for safety-testing again. This way, SOTA models still get tested, but the actors who might misuse it aren't aware of the testing's particulars until they succeed anyway...)

Might be a good thing to add to the Kelly Criterion tag [? · GW]