Well, yeah, it bothers me that the "bayesian" part of rationalism doesn't seem very bayesian―otherwise we'd be having a lot of discussions about where priors come from, how to best accomplish the necessary mental arithmetic, how to go about counting evidence and dealing with ambiguous counts (if my friends Alice and Bob both tell me X, it could be two pieces of evidence for X or just one depending on what generated the claims; how should I count evidence by default, and are there things I should be doing to find the underlying evidence?)

So―vulnerable in the current culture, but rationalists should strive to be the opposite of the "gishy" dark-epistemic people I have on my mind. Having many reasons to think X isn't necessarily a sin, but dark-epistemic people gather many reasons and have many sins, which are a good guide of what not to do.

TBH, a central object of interest to me is people using Dark Epistemics. People with a bad case of DE typically have "gishiness" as a central characteristic, and use all kinds of fallacies of which motte-and-bailey (hidden or not) is just one. I describe them together just because I haven't seen LW articles on them before. If I were specifically naming major DE syndromes, I might propose the "backstop of conspiracy" (a sense that whatever the evidence at hand doesn't explain is probably still explained by some kind of conspiracy) and projection (a tendency to loudly complain that one's political enemies have whatever negative characteristics you yourself, or your political heroes, are currently exhibiting). Such things seem very effective at protecting the person's beliefs from challenge. I think there's also a social element ("my friends all believe the same thing") but this is kept well-hidden. EDIT: other telltale signs include refusing to acknowledge that one got anything wrong or made any mistake, no matter how small; refusing to acknowledge that the 'opponent' is right about anything, no matter how minor; an allergy to detail (refusing to look at the details of any subtopic); and shifting the playfield repeatedly (changing the topic when one appears to be losing the argument).

The Hidden-Motte-And-Bailey fallacy: belief in a Bailey inspires someone to invent a Motte and write an article about it. The opinion piece describes the Motte exclusively with no mention of the Bailey. Others read it and nod along happily because it supports their cherished Bailey, and finally they share it with others in an effort to help promote the Bailey.

Example: Christian philosopher describes new argument for the existence of a higher-order-infinity God which bears no resemblance to any Abrahamic God, and which no one before the 20th century had ever conceived of.

Maybe the Motte is strong, maybe it isn't, but it feels very strong when combined with the Gish Fallacy: the feeling of safety some people (apparently) get by collecting large numbers of claims and arguments, whether or not they routinely toss them out as Gish Gallops at anyone who disagrees. The Gish Fallacy seems to be the opposite of the mathematician's mindset, for mathematicians know that a single flaw can destroy proofs of any length. While the mathematician is satisfied by a single a short and succinct proof or disproof, the Gish mindset wishes to read a thousand different descriptions of three dozen arguments, with another thousand pithy rejections of their counterarguments, so they're thoroughly prepared to dismiss the evil arguments of the enemies of truth―or, if the enemy made a good point, there are still 999 articles supporting their view, and more where that came from!

Example: my dad

I know this comment was 17 years ago, but nearly half of modern US politics in 2024 is strongly influenced by the idea "don't trust experts". I have listened to a lot of cranks (ex), and the popular ones are quite good at what they do, rhetorically, so most experts have little chance against them in a debate. Plus, if the topic is something important, the public was already primed to believe one side or the other by whatever media they happen to consume, and if the debate winner would otherwise seem close (to a layman) then such preconceptions will dominate perceptions about who won.

One thing these winning cranks take advantage is that the audience (like the cranks themselves!) tend to believe they are "knowledgeable enough to understand all the technical arguments" when they're not. Also, most experts have much worse teaching skills than these cranks (who are popular for a reason) so the expert tends to provide worse explanations, while also overlooking key counterarguments they could make against the crank's case.

So: in today's environment, I see a large fraction of the population as giving too little credence to authority in cases where they only partially evaluate the arguments. Related SSC. Like proofs, arguments can be completely wrong due to a single flaw. A partial evaluation will not necessarily uncover the flaw, but pop cranks are often good at pointing out things that appear as flaws to the uneducated (perhaps because they have a shorter inferential distance to the audience than the experts do, but also because they have more practice in public debate). And needless to say, people who are undecided don't know that they're listening to a crank, and would be offended if you claimed that the one who they thought won the debate was a crank.

I actually think Yudkowsky's biggest problem may be that he is not talking about his models. In his most prominent posts about AGI doom, such as this and the List of Lethalities, he needs to provide a complete model that clearly and convincingly leads to doom (hopefully without the extreme rhetoric) in order to justify the extreme rhetoric. Why does attempted, but imperfect, alignment lead universally to doom in all likely AGI designs*, when we lack familiarity with the relevant mind design space, or with how long it will take to escalate a given design from AGI to ASI?

* I know his claim isn't quite this expansive, but his rhetorical style encourages an expansive interpretation.

I'm baffled he gives so little effort to explaining his model. In List of Lethalities he spends a few paragraphs of preamble to cover some essential elements of concern (-3, -2, -1), then offers a few potentially-reasonable-but-minimally-supported assertions, before spending much of the rest of the article prattling off the various ways AGI can kill everyone. Personally I felt like he just skipped over a lot of the important topics, and so didn't bother to read it to the end.

I think there is probably some time after the first AGI or quasi-AGI arrives, but before the most genocide-prone AGI arrives, in which alignment work can still be done. Eliezer's rhetorical approach confusingly chooses to burn bridges with this world, as he and MIRI (and probably, by association, rationalists) will be regarded as a laughing stock when that world arrives. Various techbros including AI researchers will be saying "well, AGI came and we're all still alive, yet there's EY still reciting his doomer nonsense". EY will uselessly protest "I didn't say AGI would necessarily kill everyone right away" while the techbros retweet old EY quotes that kinda sound like that's what he's saying.

Edit: for whoever disagreed & downvoted: what for? You know there are e/accs on Twitter telling everyone that the idea of x-risk is based on Yudkowsky being "king of his tribe", and surely you know that this is not how LessWrong is supposed to work. The risk isn't supposed to be based on EY's say-so; a complete and convincing model is needed. If, on the other hand, you disagreed that his communication is incomplete and unconvincing, it should not offend you that not everyone agrees. Like, holy shit: you think humanity will cause apocalypse because it's not listening to EY, but how dare somebody suggest that EY needs better communication. I wrote this comment because I think it's very important; what are you here for?

P.S. if I'm wrong about the timeline―if it takes >15 years―my guess for how I'm wrong is (1) a major downturn in AGI/AI research investment and (2) executive misallocation of resources. I've been thinking that the brightest minds of the AI world are working on AGI, but maybe they're just paid a lot because there are too few minds to go around. And when I think of my favorite MS developer tools, they have greatly improved over the years, but there are also fixable things that haven't been fixed in 20 years, and good ideas they've never tried, and MS has created a surprising number of badly designed libraries (not to mention products) over the years. And I know people close to Google have a variety of their own pet peeves about Google.

Are AGI companies like this? Do they burn mountains cash to pay otherwise average engineers who happen to have AI skills? Do they tend to ignore promising research directions because the results are uncertain, or because results won't materialize in the next year, or because they don't need a supercomputer or aren't based mainly on transformers? Are they bad at creating tools that would've made the company more efficient? Certainly I expect some companies to be like that.

As for (1), I'm no great fan of copyright law, but today's companies are probably built on a foundation of rampant piracy, and litigation might kill investment. Or, investors may be scared away by a persistent lack of discoveries to increase reliability / curtail hallucinations.

Doesn't the problem have no solution without a spare block?

Worth noting that LLMs don't see a nicely formatted numeric list, they see a linear sequence of tokens, e.g. I can replace all my newlines with something else and Copilot still gets it:

brief testing doesn't show worse completions than when there are newlines. (and in the version with newlines this particular completion is oddly incomplete.)

Anyone know how LLMs tend to behave on text that is ambiguous―or unambiguous but "hard to parse"? I wonder if they "see" a superposition of meanings "mixed together" and produce a response that "sounds good for the mixture".

I'm having trouble discerning a difference between our opinions, as I expect a "kind-of AGI" to come out of LLM tech, given enough investment. Re: code assistants, I'm generally disappointed with Github Copilot. It's not unusual that I'm like "wow, good job", but bad completions are commonplace, especially when I ask a question in the sidebar (which should use a bigger LLM). Its (very hallucinatory) response typically demonstrates that it doesn't understand our (relatively small) codebase very well, to the point where I only occasionally bother asking. (I keep wondering "did no one at GitHub think to generate an outline of the app that could fit in the context window?")

A title like "some people can notice more imperfections than you (and they get irked)" would be more accurate and less clickbaity, though when written like that it it sounds kind of obvious.

Do you mean the "send us a message" popup at bottom-right?