EDIT: Due to the incoming administration's ties to tech investors, I no longer think an AI crash is so likely. Several signs IMHO point to "they're gonna go all-in on racing for AI, regardless of how 'needed' it actually is".

For more details on (the business side of) a potential AI crash, see recent articles by the blog Where's Your Ed At, which wrote the sorta-well-known post "The Man Who Killed Google Search".

For his AI-crash posts, start here and here and click on links to his other posts. Sadly, the author falls into the trap of "LLMs will never get to reasoning because they don't, like, know stuff, man", but luckily his core competencies (the business side, analyzing reporting) show why an AI crash could still very much happen.

Further context on the Scott Adams thing lol: He claims to have taken hypnosis lessons decades ago and has referred to using it multiple times. His, uh, personality also seems to me like it'd be more susceptible to hypnosis than average (and even he'd probably admit this in a roundabout way).

Further observation about that second sentence.

I think deeply understanding top tier capabilities researchers' views on how to achieve AGI is actually extremely valuable for thinking about alignment. Even if you disagree on object level views, understanding how very smart people come to their conclusions is very valuable.

I think the first sentence is true (especially for alignment strategy), but the second sentence seems sort of... broad-life-advice-ish, instead of a specific tip? It's a pretty indirect help to most kinds of alignment.

Otherwise, this comment's points really do seem like empirical things that people could put odds or ratios on. Wondering if a more-specific version of those "AI Views Snapshots" would be warranted, for these sorts of "research meta-knowledge" cruxes. Heck, it might be good to have lots of AI Views Snapshot DLC Mini-Charts, from for-specific-research-agendas(?) to internal-to-organizations(?!?!?!?).

I can't make this one, but I'd love to be at future LessOnline events when I'm less time/budget-constrained! :)

First link is broken.

"But my ideas are likely to fail! Can I share failed ideas?": If you share a failed idea, that saves the other person time/effort they would've spent chasing that idea. This, of course, speeds up that person's progress, so don't even share failed ideas/experiments about AI, in the status quo.

"So where do I privately share such research?" — good question! There is currently no infrastructure for this. I suggest keeping your ideas/insights/research to yourself. If you think that's difficult for you to do, then I suggest not thinking about AI, and doing something else with your time, like getting into factorio 2 or something.

"But I'm impatient about the infrastructure coming to exist!": Apply for a possibly-relevant grant and build it! Or build it in your spare time. Or be ready to help out if/when someone develops this infrastructure.

"But I have AI insights and I want to convert them into money/career-capital/personal-gain/status!": With that kind of brainpower/creativity, you can get any/all of those things pretty efficiently without publishing AI research, working at a lab, advancing a given SOTA, or doing basically (or literally) anything that differentially speeds up AI capabilities. This, of course, means "work on the object-level problem, without routing that work through AI capabilities", which is often as straightforward "do it yourself".

"But I'm wasting my time if I don't get involved in something related to AGI!": "I want to try LSD, but it's only available in another country. I could spend my time traveling to that country, or looking for mushrooms, or even just staying sober. Therefore, I'm wasting my time unless I immediately inject 999999 fentanyl."

How scarce are tickets/"seats"?

I will carefully hedge my investment in this company by giving it $325823e7589245728439572380945237894273489, in exchange for a board seat so I can keep an eye on it.

I have over 5 Twitter followers, I'll take my board seat when ur ready

Giving up on transhumanism as a useful idea of what-to-aim-for or identify as, separate from how much you personally can contribute to it.

More directly: avoiding "pinning your hopes on AI" (which, depending on how I'm supposed to interpret this, could mean "avoiding solutions that ever lead to aligned AI occurring" or "avoiding near-term AI, period" or "believing that something other than AI is likely to be the most important near-future thing", which are pretty different from each other, even if the end prescription for you personally is (or seems, on first pass, to be) the same.), separate from how much you personally can do to positively affect AI development.

Then again, I might've misread/misinterpreted what you wrote. (I'm unlikely to reply to further object-level explanation of this, sorry. I mainly wanted to point out the pattern. It'd be nice if your reasoning did turn out correct, but my point is that its starting-place seems/seemed to be rationalization as per the pattern.)

Yes, I think this post / your story behind it, is likely an example of this pattern.

That's technically a different update from the one I'm making. However, I also update in favor of that, as a propagation of the initial update. (Assuming you mean "good enough" as "good enough at pedagogy".)

This sure does update me towards "Yudkowsky still wasn't good enough at pedagogy to have made 'teach people rationality techniques' an 'adequately-covered thing by the community'".

• Person tries to work on AI alignment.
• Person fails due to various factors.
• Person gives up working on AI alignment. (This is probably a good move, when it's not your fit, as is your case.)
• Danger zone: In ways that sort-of-rationalize-around their existing decision to give up working on AI alignment, the person starts renovating their belief system around what feels helpful to their mental health. (I don't know if people are usually doing this after having already tried standard medical-type treatments, or instead of trying those treatments.)
• Danger zone: Person announces this shift to others, in a way that's maybe and/or implicitly prescriptive (example).

There are, depressingly, many such cases of this pattern. (Related post with more details on this pattern.)

Group Debugging is intriguing...

How many times has someone expressed "I'm worried about 'goal-directed optimizers', but I'm not sure what exactly they are, so I'm going to work on deconfusion."? There's something weird about this sentiment, don't you think?

I disagree, and I will take you up on this!

"Optimization" is a real, meaningful thing to fear, because:

• We don't understand human values, or even necessarily meta-understand them.
• Therefore, we should be highly open to the idea that a goal (or meta-goal) that we encode (or meta-encode) would be bad for anything powerful to base-level care about.
• And most importantly, high optimization power breaks insufficiently-strong security assumptions. That, in itself, is why something like "security mindset" is useful without necessarily thinking of a powerful AI as an "enemy" in war-like terms.
  • Here "security assumptions" is used in a broad sense, the same way that "writing assumptions" (the ones needed to design a word-processor software) could include seemingly-trivial things like "there is an input device we can access" and "we have the right permissions on this OS".

Ah, yeah that's right.

If it helps clarify: I (and some others) break down the alignment problem into "being able to steer it at all" and "what to steer it at". This post is about the danger of having the former solved, without the latter being solved well (e.g. through some kind of CEV).

Love this event series! Can't come this week, but next one I can!

No worries! I make similar mistakes all the time (just check my comment history ;-;)

And I do think your comment is useful, in the same way that Rohin's original comment (which my post is responding to) is useful :)

FWIW, I have an underlying intuition here that's something like “if you're going to go Dark Arts, then go big or go home”, but I don't really know how to operationalize that in detail and am generally confused and sad. In general, I think people who have things like “logical connectives are relevant to the content of the text” threaded through enough of their mindset tend to fall into a trap analogous to the “Average Familiarity” xkcd or to Hofstadter's Law when they try truly-mass communication unless they're willing to wrench things around in what are often very painful ways to them, and (per the analogies) that this happens even when they're specifically trying to correct for it.

I disagree with the first sentence, but agree strongly with the rest of it. My whole point is that it may be literally possible to make:

1. mass-audience arguments
2. about extinction risk from AI
3. that don't involve lying.

Maybe we mean different things by "Dark Arts" here? I don't actually consider (going hard with messaging like) "This issue is complicated, but you [the audience member] understandably don't want to deal with it, so we should go harder on preventing risk for now based on the everyday risk-avoidance you probably practice yourself." as lying or manipulation. You could call it Dark Arts if you drew the "Dark Arts" cluster really wide, but I would disagree with that cluster-drawing.

Now, I do separately observe a subset of more normie-feeling/working-class people who don't loudly profess the above lines and are willing to e.g. openly use some generative-model art here and there in a way that suggests they don't have the same loud emotions about the current AI-technology explosion. I'm not as sure what main challenges we would run into with that crowd, and maybe that's whom you mean to target.

That's... basically what my proposal is? Yeah? People that aren't already terminally-online about AI, but may still use chatGPT and/or StableDiffusion for fun or even work. Or (more common) those who don't even have that much interaction, who just see AI as yet another random thingy in the headlines.

Yeah, mostly agreed. My main subquestion (that led me to write the review, besides this post being referenced in Leake's work) was/sort-of-still-is "Where do the ratios in value-handshakes come from?". The default (at least in the tag description quote from SSC) is uncertainty in war-winning, but that seems neither fully-principled nor nice-things-giving (small power differences can still lead to huge win-% chances, and superintelligences would presumably be interested in increasing accuracy). And I thought maybe ROSE bargaining could be related to that.

The relation in my mind was less ROSE --> DT, and more ROSE --?--> value-handshakes --> value-changes --?--> DT.

(On my beliefs, which I acknowledge not everyone shares, expecting something better than "mass delusion of incorrect beliefs that implies that AGI is risky" if you do wide-scale outreach now is assuming your way out of reality.)

I'm from the future, January 2024, and you get some Bayes Points for this!

The "educated savvy left-leaning online person" consensus (as far as I can gather) is something like: "AI art is bad, the real danger is capitalism, and the extinction danger is some kind of fake regulatory-capture hype techbro thing which (if we even bother to look at the LW/EA spaces at all) is adjacent to racists and cryptobros".

Still seems too early to tell whether or not people are getting lots of false beliefs that are still pushing them towards believing-AGI-is-an-X-risk, especially since that case seems to be made (in the largest platform) indirectly in congressional hearings that nobody outside tech/politics actually watches.

But it really doesn't seem great that my case for wide-scale outreach being good is "maybe if we create a mass delusion of incorrect beliefs that implies that AGI is risky, then we'll slow down, and the extra years of time will help". So overall my guess is that this is net negative.

To devil's steelman some of this: I think there's still an angle that few have tried in a really public way. namely, ignorance and asymmetry. (There is definitely a better term or two for what I'm about to describe, but I forgot it. Probably from Taleb or something.)

A high percentage of voting-eligible people in the US... don't vote. An even higher percentage vote in only the presidential elections, or only some presidential elections. I'd bet a lot of money that most of these people aren't working under a Caplan-style non-voting logic, but instead under something like "I'm too busy" or "it doesn't matter to me / either way / from just my vote".

Many of these people, being politically disengaged, would not be well-informed about political issues (or even have strong and/or coherent values related to those issues). What I want to see is an empirical study that asks these people "are you aware of this?" and "does that awareness, in turn, factor into you not-voting?".

I think there's a world, which we might live in, where lots of non-voters believe something akin to "Why should I vote, if I'm clueless about it? Let the others handle this lmao, just like how the nice smart people somewhere make my bills come in."

In a relevant sense, I think there's an epistemically-legitimate and persuasive way to communicate "AGI labs are trying to build something smarter than humans, and you don't have to be an expert (or have much of a gears-level view of what's going on) to think this is scary. If our smartest experts still disagree on this, and the mistake-asymmetry is 'unnecessary slowdown VS human extinction', then it's perfectly fine to say 'shut it down until [someone/some group] figures out what's going on'".

To be clear, there's still a ton of ways to get this wrong, and those who think otherwise are deluding themselves out of reality. I'm claiming that real-human-doable advocacy can get this right, and it's been mostly left untried.

EXTRA RISK NOTE: Most persuasion, including digital, is one-to-many "broadcast"-style; "going viral" usually just means "some broadcast happened that nobody heard of", like an algorithm suggesting a video to a lot of people at once. Given this, plus anchoring bias, you should expect and be very paranoid about the "first thing people hear = sets the conversation" thing. (Think of how many people's opinions are copypasted from the first classy video essay mass-market John Oliver video they saw about the subject, or the first Fox News commentary on it.)

Not only does the case for X-risk need to be made first, but it needs to be right (even in a restricted way like my above suggestion) the first time. Actually, that's another reason why my restricted-version suggestion should be prioritized, since it's more-explicitly robust to small issues.

(If somebody does this in real life, you need to clearly end on something like "Even if a minor detail like [name a specific X] or [name a specific Y] is wrong, it doesn't change the underlying danger, because the labs are still working towards Earth's next intelligent species, and there's nothing remotely strong about the 'safety' currently in place.")

So there's a sorta-crux about how much DT alignment researchers would have to encode into the-AI-we-want-to-be-aligned, before that AI is turned on. Right now I'm leaning towards "an AI that implements CEV well, would either turn-out-to-have or quickly-develop good DT on its own", but I can see it going either way. (This was especially true yesterday when I wrote this review.)

And I was trying to think through some of the "DT relevance to alignment" question, and I looked at relevant posts by [Tamsin Leake](https://www.lesswrong.com/users/tamsin-leake) (whose alignment research/thoughts I generally agree with). And that led me to thinking more about value-handshakes, son-of-CDT (see Arbital), and systems like ROSE bargaining. Any/all of which, under certain assumptions, could determine (or hint at) answers to the "DT relevance" thing.

Selection Bias Rules (Debatably Literally) Everything Around Us

Currently, I think this is a big crux in how to "do alignment research at all". Debatably "the biggest" or even "the only real" crux.

(As you can tell, I'm still uncertain about it.)

Decision theory is hard. In trying to figure out why DT is useful (needed?) for AI alignment in the first place, I keep running into weirdness, including with bargaining.

Without getting too in-the-weeds: I'm pretty damn glad that some people out there are working on DT and bargaining.

Still seems too early to tell if this is right, but man is it a crux (explicit or implicit).

Terence Tao seems to have gotten some use out of the most recent LLMs.

if you take into account the 4-5 staff months these cost to make each year, we net lost money on these

For the record, if each book-set had cost $40 or even $50, I still would have bought them, right on release, every time. (This was before my financial situation improved, and before the present USD inflation.)

I can't speak for everyone's financial situation, though. But I (personally) mentally categorize these as "community-endorsement luxury-type goods", since all the posts are already online anyway.

The rationality community is unusually good about not selling ingroup-merch when it doesn't need or want to. These book sets are the perfect exceptions.

to quote a fiend, "your mind is a labyrinth of anti-cognitive-bias safeguards, huh?"

[emphasis added]

The implied context/story this is from sure sounds interesting. Mind telling it?

I don't think of governments as being... among other things "unified" enough to be superintelligences.

Also, see "Things That Are Not Superintelligence" and "Maybe The Real Superintelligent AI Is Extremely Smart Computers".

The alignment research that is done will be lower quality due to less access to compute, capability knowhow, and cutting edge AI systems.

I think this is false, though it's a crux in any case.

Capabilities withdrawal is good because we don't need big models to do the best alignment work, because that is theoretical work! Theoretical breakthroughs can make empirical research more efficient. It's OK to stop doing capabilities-promoting empirical alignment, and focus on theory for a while.

(The overall idea of "if all alignment-knowledgeable capabilities people withdraw, then all capabilities will be done by people who don't know/care about alignment" is still debatable, but distinct. One possible solution: safety-promoting AGI labs stop their capabilities work, but continue to hire capabilities people, partly to prevent them from working elsewhere. This is complicated, but not central to my objection above.)

I see this asymmetry a lot and may write a post on it:

If theoretical researchers are wrong, but you do follow their caution anyway, then empirical alignment goes slower... and capabilities research slows down even more. If theoretical researchers are right, but you don't follow their caution, you continue or speedup AI capabilities to do less-useful alignment work.

Good catch! Most of it is hunches to be tested (and/or theorized on, but really tested) currently. Fixed

"Exfohazard" is a quicker way to say "information that should not be leaked". AI capabilities has progressed on seemingly-trivial breakthroughs, and now we have shorter timelines.

The more people who know and understand the "exfohazard" concept, the safer we are from AI risk.

More framings help the clarity of the discussion. If someone doesn't understand (or agree with) classic AI-takeover scenarios, this is one of the posts I'd use to explain them.

Funny thing, I had a similar idea to this (after reading some Sequences and a bit about pedagogy). That was the sort-of-multi-modal-based intuition behind Mathopedia.

Is any EA group *funding* adult human intelligence augmentation? It seems broadly useful for lots of cause areas, especially research-bottlenecked ones like AI alignment.

Why hasn't e.g. OpenPhil funded this project?: https://www.lesswrong.com/posts/JEhW3HDMKzekDShva/significantly-enhancing-adult-intelligence-with-gene-editing

Seems to usually be good faith. People can still be biased of course (and they can't all be right on the same questions, with the current disagreements), but it really is down to differing intuitions, which background-knowledge posts have been read by which people, etc.

To add onto other people's answers:

People have disagreements over what the key ideas about AI/alignment even are.

People with different basic-intuitions notoriously remain unconvinced by each other's arguments, analogies, and even (the significance of) experiments. This has not been solved yet.

Alignment researchers usually spend most time on their preferred vein of research, rather than trying to convince others.

To (try to) fix this, the community's added concepts like "inferential distance" and "cruxes" to our vocabulary. These should be be discussed and used explicitly.

One researcher has some shortform notes (here and here) on how hard it is to communicate about AI alignment. I myself wrote some longer, more emotionally-charged notes on why we'd expect this.

But there's hope yet! This chart format makes it easier to communicate beliefs on key AI questions. And better ideas can always be lurking around the corner...

I relate to this quite a bit ;-;

People's minds are actually extremely large things that you fundamentally can't fully model

Is this "fundamentally" as in "because you, the reader, are also a bounded human, like them"? Or "fundamentally" as in (something more fundamental than that)?

If timelines weren't so short, brain-computer-based telepathy would unironically be a big help for alignment.

(If a group had the money/talent to "hedge" on longer timelines by allocating some resources to that... well, instead of a hivemind, they first need to run through the relatively-lower-hanging fruit. Actually, maybe they should work on delaying capabilities research, or funding more hardcore alignment themselves, or...)

This point could definitely be its own post. I'd love to see you write this! (I'd of course be willing to proofread/edit it, title it, etc.)

And the AGI, if it's worth the name, would not fail to exploit this.

This sentence is a good short summary of some AI alignment ideas. Good writing!

Someone may think "Anomalous worlds imply the simulation-runners will save us from failing at alignment!"

My reply is: Why are they running a simulation where we have to solve alignment?

At a first pass, if we're in a simulation, it's probably for research, rather than e.g. a video game or utopia. (H/t an IRL friend for pointing this out).

Therefore, if we observe ourselves needing to solve AI alignment (and not having solved it yet), the simulation-runners potentially also need AI alignment to get solved. And if history is any guide, we should not rely on any such beings "saving us" before things cross a given threshold of badness.

(There are other caveats I can respond to about this, but please DM me about them if you think of them, since they may be infohazard-leaning and (thus) should not be commented publicly, pending more understanding.)

But you wouldn't study ... MNIST-classifier CNNs circa 2010s, and claim that your findings generalize to how LLMs circa 2020s work.

This particular bit seems wrong; CNNs and LLMs are both built on neural networks. If the findings don't generalize, that could be called a "failure of theory", not an impossibility thereof. (Then again, maybe humans don't have good setups for going 20 steps ahead of data when building theory, so...)

(To clarify, this post is good and needed, so thank you for writing it.)

I'm most willing to hear meta-level arguments about internal consistency, or specific existing evidence that I don't know about (especially "secret" evidence). Less certain about the governance sections and some of the exact-wordings.