Passing the onion test is better than not passing it, but I think the relevant standard is having intent to inform. There's a difference between trying to share relevant information in the hopes that the audience will integrate it with their own knowledge and use it to make better decisions, and selectively sharing information in the hopes of persuading the audience to make the decision you want them to make.

An evidence-filtering clever arguer can pass the onion test (by not omitting information that the audience would be surprised to learn was omitted) and pass the test of not technically lying (by not making false statements) while failing to make a rational argument in which the stated reasons are the real reasons.

going into any detail about it doesn't feel like a useful way to spend weirdness points.

That may be a reasonable consequentialist decision given your goals, but it's in tension with your claim in the post to be disregarding the advice of people telling you to "hoard status and credibility points, and [not] spend any on being weird."

Whatever they're trying to do, there's almost certainly a better way to do it than by keeping Matrix-like human body farms running.

You've completely ignored the arguments from Paul Christiano that Ryan linked to at the top of the thread. (In case you missed it: 1 2.)

The claim under consideration is not that "keeping Matrix-like human body farms running" arises as an instrumental subgoal of "[w]hatever [AIs are] trying to do." (If you didn't have time to read the linked arguments, you could have just said that instead of inventing an obvious strawman.)

Rather, the claim is that it's plausible that the AI we build (or some agency that has decision-theoretic bargaining power with it) cares about humans enough to spend some tiny fraction of the cosmic endowment on our welfare. (Compare to how humans care enough about nature preservation and animal welfare to spend some resources on it, even though it's a tiny fraction of what our civilization is doing.)

Maybe you think that's implausible, but if so, there should be a counterargument explaining why Christiano is wrong. As Ryan notes, Yudkowsky seems to believe that some scenarios in which an agency with bargaining power cares about humans are plausible, describing one example of such as "validly incorporat[ing] most all the hopes and fears and uncertainties that should properly be involved, without getting into any weirdness that I don't expect Earthlings to think about validly." I regard this statement as undermining your claim in the post that MIRI's "reputation as straight shooters [...] remains intact." Withholding information because you don't trust your audience to reason validly (!!) is not at all the behavior of a "straight shooter".

I think I'm judging that schoolwork that's sufficiently similar to the kind of intellectual work that I want to do anyway (or that I can otherwise get selfish benefit out of) gets its cost discounted. (It doesn't have to be exactly the same.) And that commuting on the train with a seat is 70% similar to library time. (I wouldn't even consider a car commute.)

For the fall semester, I'd be looking at "Real Analysis II", "Probability Models", "Applied and Computational Linear Algebra", and (wait for it ...) "Queer Literatures and Media".

That schedule actually seems ... pretty good? "Real Analysis II" with Prof. Schuster is the course I actually want to take, as a legitimate learning resource and challenge, but the other two math courses don't seem worthless and insulting. "Queer Literatures and Media" does seem worthless and insulting, but might present an opportunity to troll the professor, or fodder for my topic-relevant blog and unfinished novella about a young woman hating going to SFSU.

As for judgement, I think I'm integrating a small judgement-density over a large support of time and Society. The immediate trigger for me even considering this might have been that people were arguing about school and Society on Twitter in way that brought up such rage and resentment in me. Somehow, I think I would be more at peace if I could criticize schooling from the position of "... and I have a math degree" rather than "... so I didn't finish." That peace definitely wouldn't be worth four semesters, but it might be worth two.

I think these judgements would benefit from more concreteness: that rather than proposing a dichotomy of "capabilities research" (them, Bad) and "alignment research" (us, Good), you could be more specific about what kinds of work you want to see more and less of.

I agree that (say) Carmack and Sutton are doing a bad thing by declaring a goal to "build AGI" while dismissing the reasons that this is incredibly dangerous. But the thing that makes infohazard concerns so fraught is that there's a lot of work that potentially affects our civilization's trajectory into the machine intelligence transition in complicated ways, which makes it hard to draw a boundary around "trusted alignment researchers" in a principled and not self-serving way that doesn't collapse into "science and technology is bad".

We can agree that OpenAI as originally conceived was a bad idea. What about the people working on music generation? That's unambiguously "capabilities", but it's also not particularly optimized at ending the world that way "AGI for AGI's sake" projects are. If that's still bad even though music generation isn't going to end the world (because it's still directing attention and money into AI, increasing the incentive to build GPUs, &c.), where do you draw the line? Some of the researchers I cited in my most recent post are working on "build[ing] better models of primate visual cognition". Is that wrong? Should Judea Pearl not have published? Turing? Charles Babbage?

In asking these obnoxious questions, I'm not trying to make a reductio ad absurdum of caring about risk, or proposing an infinitely slippery slope where our only choices are between max accelerationism and a destroy-all-computers Butlerian Jihad. I just think it's important to notice that "Stop thinking about AI" kind of does amount to a Butlerian Jihad (and that publishing and thinking are not unrelated)?

I think this is undignified.

I agree that it would be safer if humanity were a collective hivemind that could coordinate to not build AI until we know how to build the best AI, and that people should differentially work on things that make the situation better rather than worse, and that this potentially includes keeping quiet about information that would make things worse.

The problem is—as you say—"[i]t's very rare that any research purely helps alignment"; you can't think about aligning AI without thinking about AI. In order to navigate the machine intelligence transition in the most dignified way, you want your civilization's best people to be doing their best thinking about the problem, and your best people can't do their best thinking under the conditions of paranoid secrecy.

Concretely, I've been studying some deep learning basics lately and have written a couple posts about things I've learned. I think this was good, not bad. I think I and my readers have a slightly better understanding of the technology in question than if I hadn't studied and hadn't written, and that better understanding will help us make better decisions in expectation.

This applies doubly so to work that aims to make AI understandable or helpful, rather than aligned—a helpful AI will help anyone

Sorry, what? I thought the fear was that we don't know how to make helpful AI at all. (And that people who think they're being helped by seductively helpful-sounding LLM assistants are being misled by surface appearances; the shoggoth underneath has its own desires that we won't like when it's powerful enough to persue them autonomously.) In contrast, this almost makes it sound like you think it is plausible to align AI to its user's intent, but that this would be bad if the users aren't one of "us"—you know, the good alignment researchers who want to use AI to take over the universe, totally unlike those evil capabilities researchers who want to use AI to produce economically valuable goods and services.

Sorry, this doesn't make sense to me. The boundary doesn't need to be smooth in an absolute sense in order to exist and be learnable (whether by neural nets or something else). There exists a function from business plans to their profitability. The worry is that if you try to approximate that function with standard ML tools, then even if your approximation is highly accurate on any normal business plan, it's not hard to construct an artificial plan on which it won't be. But this seems like a limitation of the tools; I don't think it's because the space of business plans is inherently fractally complex and unmodelable.

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

Right. I think the optimists would say that conditional sampling works great in practice, and that this bodes well for applying similar techniques to more ambitious domains. There's no chance of this image being in the Stable Diffusion pretraining set:

One could reply, "Oh, sure, it's obvious that you can conditionally sample a learned distribution to safely do all sorts of economically valuable cognitive tasks, but that's not the danger of true AGI." And I ultimately think you're correct about that. But I don't think the conditional-sampling thing was obvious in 2004.

I agree, but I don't see why that's relevant? The point of the "Adversarial Spheres" paper is not that the dataset is realistic, of course, but that studying an unrealistically simple dataset might offer generalizable insights. If the ground truth decision boundary is a sphere, but your neural net learns a "squiggly" ellipsoid that admits adversarial examples (because SGD is just brute-forcing a fit rather than doing something principled that could notice hypotheses on the order of, "hey, it's a sphere"), that's a clue that when the ground truth is something complicated, your neural net is also going to learn something squiggly that admits adversarial examples (where the squiggles in your decision boundary predictably won't match the complications in your dataset, even though they're both not-simple).