It’s not just the title but also (what I took to be) the thesis statement “Comparisons are often useful, but in this case, I think the disanalogies are much more compelling than the analogies”, and also “While I think the disanalogies are compelling…” and such.

For comparison:

Question: Are the analogies between me (Steve) and you (David) more compelling, or less compelling, than the disanalogies between me and you?

I think the correct answer to that question is “Huh? What are are you talking about?” For example:

• If this is a conversation about gross anatomy or cardiovascular physiology across the animal kingdom, then you and I are generally extremely similar.
• If this is a conversation about movie preferences or exercise routines, then I bet you and I are pretty different.

So at the end of the day, that question above is just meaningless, right? We shouldn’t be answering it at all.

I don’t think disanalogies between A and B can be “compelling” or “uncompelling”, any more than mathematical operations can be moist or dry. I think a disanalogy between A and B may invalidate a certain point that someone is trying to make by analogizing A to B, or it may not invalidate it, but that depends on what the point is.

FWIW I do think "Biorisk is Often an Unhelpful Analogy for AI Risk," or "Biorisk is Misleading as a General Analogy for AI Risk" are both improvements :)

I agree that in the limit of an extremely structured optimizer, it will work in practice, and it will wind up following strategies that you can guess to some extent a priori.

I also agree that in the limit of an extremely unstructured optimizer, it will not work in practice, but if it did, it will find out-of-the-box strategies that are difficult to guess a priori.

But I disagree that there’s no possible RL system in between those extremes where you can have it both ways.

On the contrary, I think it’s possible to design an optimizer which is structured enough to work well in practice, while simultaneously being unstructured enough that it will find out-of-the-box solutions very different from anything the programmers were imagining.

Examples include:

• MuZero: you can’t predict a priori what chess strategies a trained MuZero will wind up using by looking at the source code. The best you can do is say “MuZero is likely to use strategies that lead to its winning the game”.
• “A civilization of humans” is another good example: I don’t think you can look at the human brain neural architecture and loss functions etc., and figure out a priori that a civilization of humans will wind up inventing nuclear weapons. Right?

This book argues (convincingly IMO) that it’s impossible to communicate, or even think, anything whatsoever, without the use of analogies.

• If you say “AI runs on computer chips”, then the listener will parse those words by conjuring up their previous distilled experience of things-that-run-on-computer-chips, and that previous experience will be helpful in some ways and misleading in other ways.
• If you say “AI is a system that…” then the listener will parse those words by conjuring up their previous distilled experience of so-called “systems”, and that previous experience will be helpful in some ways and misleading in other ways.

Etc. Right?

If you show me an introduction to AI risk for amateurs that you endorse, then I will point out the “rhetorical shortcuts that imply wrong and misleading things” that it contains—in the sense that it will have analogies between powerful AI and things-that-are-not-powerful-AI, and those analogies will be misleading in some ways (when stripped from their context and taken too far). This is impossible to avoid.

Anyway, if someone says:

When it comes to governing technology, there are some areas, like inventing new programming languages, where it’s awesome for millions of hobbyists to be freely messing around; and there are other areas, like inventing new viruses, or inventing new uranium enrichment techniques, where we definitely don’t want millions of hobbyists to be freely messing around, but instead we want to be thinking hard about regulation and secrecy. Let me explain why AI belongs in the latter category…

…then I think that’s a fine thing to say. It’s not a rhetorical shortcut, rather it’s a way to explain what you’re saying, pedagogically, by connecting it to the listener’s existing knowledge and mental models.

This is an 800-word blog post, not 5 words. There’s plenty of room for nuance.

The way it stands right now, the post is supporting conversations like:

Person A: It’s not inconceivable that the world might wildly under-invest in societal resilience against catastrophic risks even after a “warning shot” for AI. Like for example, look at the case of bio-risks—COVID just happened, so the costs of novel pandemics are right now extremely salient to everyone on Earth, and yet, (…etc.).

Person B: You idiot, bio-risks are not at all analogous to AI. Look at this blog post by David Manheim explaining why.

Or:

Person B: All technology is always good, and its consequences are always good, and spreading knowledge is always good. So let’s make open-source ASI asap.

Person A: If I hypothetically found a recipe that allowed anyone to make a novel pandemic using widely-available equipment, and then I posted it on my blog along with clearly-illustrated step-by-step instructions, and took out a billboard out in Times Square directing people to the blog post, would you view my actions as praiseworthy? What would you expect to happen in the months after I did that?

Person B: You idiot, bio-risks are not at all analogous to AI. Look at this blog post by David Manheim explaining why.

Is this what you want? I.e., are you on the side of Person B in both these cases?

Right, and that wouldn’t apply to a model-based RL system that could learn an open-ended model of any aspect of the world and itself, right?

I think your “it is nearly impossible for any computationally tractable optimizer to find any implementation for a sparse/distant reward function” should have some caveat that it only clearly applies to currently-known techniques. In the future there could be better automatic-world-model-builders, and/or future generic techniques to do automatic unsupervised reward-shaping for an arbitrary reward, such that AIs could find out-of-the-box ways to solve hard problems without handholding.

I have to admit that I'm struggling to find these arguments at the moment

I sometimes say things kinda like that, e.g. here.

All the examples of "RL" doing interesting things that look like they involve sparse/distant reward involve enormous amounts of implicit structure of various kinds, like powerful world models.

I guess when you say “powerful world models”, you’re suggesting that model-based RL (e.g. MuZero) is not RL but rather “RL”-in-scare-quotes. Was that your intention?

I’ve always thought of model-based RL is a central subcategory within RL, as opposed to an edge-case.

I wish you had entitled / framed this as “here are some disanalogies between biorisk and AI risk”, rather than suggesting in the title and intro that we should add up the analogies and subtract the disanalogies to get a General Factor of Analogizability between biorisk and AI risk.

We can say that they’re similar in some respects and different in other respects, and if a particular analogy-usage (in context) is leaning on an aspect in which they’re similar, that’s good, and if a particular analogy-usage (in context) is leaning on an aspect in which they’re different, that’s bad. For details and examples of what I mean, see my comments on a different post: here & here.

Let D be the distribution of (reward, trajectory) pairs for every possible trajectory.

Split D into two subsets: D1 where reward > 7 and D2 where reward ≤ 7.

Suppose that, in D, 1-in-a-googol samples is in the subset D1, and all the rest are in D2.

(For example, if a videogame involves pressing buttons for 20 minutes, you can easily have less than 1-in-a-googol chance of beating even the first mini-boss if you press the buttons randomly.)

Now we randomly pick a million samples from D in an attempt to learn the distribution D. But (as expected with overwhelming probability), it turns out that every single one of those million samples are more specifically in the subset D2.

Now consider a point X in D1 (its reward is 30). Question: Is X “out of distribution”?

Arguably no, because we set up a procedure to learn the distribution D, and D contains X.

Arguably yes, because when we ran the procedure, all the points actually used to learn the distribution were in D2, so we were kinda really only learning D2, and D2 doesn’t contain X.

(In the videogame example, if in training you never see a run that gets past the first mini-boss, then certainly intuitively we'd say that runs that do get past it, and that thus get to the next stage of the game, are OOD.)

Anyway, I was gonna say the opposite of what you said—sufficiently hard optimization via conditional sampling works in theory (i.e., if you could somehow learn D and conditionally sample on reward>7, it would work), but not in practice (because reward>7 is so hard to come by that you will never actually learn that part of D by random sampling).

A function that tells your AI system whether an action looks good and is right virtually all of the time on natural inputs isn't safe if you use it to drive an enormous search for unnatural (highly optimized) inputs on which it might behave very differently.

Yeah, you can have something which is “a brilliant out-of-the-box solution to a tricky problem” from the AI’s perspective, but is “reward-hacking / Goodharting the value function” from the programmer’s perspective. You say tomato, I say to-mah-to.

It’s tricky because there’s economic pressure to make AIs that will find and execute brilliant out-of-the-box solutions. But we want our AIs to think outside of some of the boxes (e.g. yes you can repurpose a spare server rack frame for makeshift cable guides), but we want it to definitely stay inside other boxes (e.g. no you can’t take over the world). Unfortunately, the whole idea of “think outside the box” is that we’re not aware of all the boxes that we’re thinking inside of.

The particular failure mode of "leaving one thing out" is starting to seem less likely on the current paradigm. Katja Grace notes that image synthesis methods have no trouble generating photorealistic human faces. Diffusion models don't "accidentally forget" that faces have nostrils, even if a human programmer trying to manually write a face image generation routine might. Similarly, large language models obey the quantity-opinion-size-age-shape-color-origin-purpose adjective order convention in English without the system designers needing to explicitly program that in or even be aware of it, despite the intuitive appeal of philosophical arguments one could make to the effect that "English is fragile."

All three of those examples are of the form “hey here’s a lot of samples from a distribution, please output another sample from the same distribution”, which is not the kind of problem where anyone would ever expect adversarial dynamics / weird edge-cases, right?

(…Unless you do conditional sampling of a learned distribution, where you constrain the samples to be in a specific a-priori-extremely-unlikely subspace, in which case sampling becomes isomorphic to optimization in theory. (Because you can sample from the distribution of (reward, trajectory) pairs conditional on high reward.))

Or maybe you were making a different point in this particular paragraph?