scruiser

So... on strategies for explaining to normies, a personal story often grabs people more than dry facts, so you could focus on the narrative of Eliezer trying big idea, failing or giving up, and moving on to bigger ideas before repeating (stock bot to seed AI to AI programming language to AI safety to shut down all AI)? You'll need the wayback machine, but it is a simple narrative with a clear pattern?

Or you could focus on the narrative arc of someone that previously bought into less wrong? I don't volunteer, but maybe someone else would be willing to take that kind of attention?

I took a stab at both approaches here: https://awful.systems/comment/6885617

This isn't debate club or men of science hour, this is a forum for making fun of idiocy around technology. If you don't like that you can leave (or post a few more times for us to laugh at before you're banned).

As to the particular paper that got linked, we've seen people hyping LLMs misrepresent their research as much more exciting than it actually is (all the research advertising deceptive LLMs for example) many many times already, so most of us weren't going to waste time to track down the actual paper (and not just the marketing release) to pick apart the methods. You could say (raises sunglasses) our priors on it being bullshit were too strong.

Big effort post... reading it will still be less effort than listening to the full Behind the Bastards podcast, so I hope you appreciate it...

To summarize it from a personal angle...

In 2011, I was a high schooler who liked Harry Potter fanfics. I found Harry Potter And The Methods of Rationality a fun story, so I went to the lesswrong website and was hooked on all the neat pop-science explanations. The AGI stuff and cryonics and transhumanist stuff seemed a bit fanciful but neat (after all, the present would seem strange and exciting to someone from a hundred years ago). Fast forward to 2015, HPMOR was finally finishing, I was finishing my undergraduate degree, and in the course of getting a college education I had actually taken some computer science and machine learning courses. Reconsidering lesswrong with my level of education then... I noticed MIRI (the institute Eliezer founded) wasn't actually doing anything with neural nets, they were playing around with math abstractions, and they hadn't actually published much formal writing (well not actually any, but at the time I didn't appreciate peer-review vs. self publishing and preprints), and even the informal lesswrong posts had basically stopped. I had gotten into a related blog, slatestarcodex (written by Scott Alexander), which filled some of the same niche, but in 2016 Scott published a defense of Trump normalizing him, and I realized Scott had an agenda at cross purposes with the "center-left" perspective he portrayed himself as. At around that point, I found the reddit version of sneerclub and it connected a lot of dots I had been missing. Far from the AI expert he presented himself as, Eliezer had basically done nothing but write loose speculation on AGI and pop-science explanations. And Scott Alexander was actually trying to push "human biodiversity" (i.e. racism disguised in pseudoscience) and neoreactionary/libertarian beliefs. From there, it became apparent to me a lot of Eliezer's claims weren't just a bit fanciful, they were actually really really ridiculous, and the community he had setup had a deeply embedded racist streak.

To summarize it focusing on Eliezer....

Late 1990s Eliezer was on various mailing lists, speculating with bright eyed optimism about nanotech and AGI and genetic engineering and cryonics. He tried his hand at getting in on it, first trying to write a stock trading bot... which didn't work, then trying to write up seed AI (AI that would bootstrap to strong AGI and change the world)... which also didn't work; then trying to develop a new programming language for AI... which he never finished. Then he realized he had been reckless, an actually successful AI might have destroyed mankind, so really it was lucky he didn't succeed, he needed to figure out how to align an AI first. So from the mid 2000s on he started getting donors (this is where Thiel comes in) to fund his research. People kind of thought he was a crank, or just didn't seem concerned with his ideas, so he concluded they must not be rational enough, and set about, first on Overcoming bias, then his own blog, lesswrong, writing a sequence of blog posts to fix that (and putting any actual AI research on hold). They got moderate attention which exploded in the early 2010s when a side project of writing Harry Potter fanfiction took off. He used this fame to get more funding and spread his ideas further. Finally, around mid 2010s, he pivoted to actually trying to do AI research again... MIRI has a sparse (compared to number of researchers they hired and how productive good professors in academia are) collection of papers focused on an abstract concept for AI called AIXI, that basically depends on having infinite computing power and isn't remotely implementable in the real world. Last I checked they didn't get any further than that. Eliezer was skeptical of neural network approaches, derisively thinking of them as voodoo science trying to blindly imitate biology with no proper understanding, so he wasn't prepared for NN taking off mid 2012 and leading to GPT and LLM approaches. So when ChatGPT starts looking impressive, he starts panicking, leading to him going on a podcast circuit professing doom (after all if he and his institute couldn't figure out AI alignment, no one can, and we're likely all doomed for reasons he has written tens of thousands of words in blog posts about without being refuted at a quality he believes is valid).

To tie off some side points:

• Peter Thiel was one of the original funders of Eliezer and his institution. It was probably a relatively cheap attempt to buy reputation, and it worked to some extent. Peter Thiel has cut funding since Eliezer went full doomer (Thiel probably wanted Eliezer as a silicon valley hype man, not an apocalypse cult).

• As Scott continued to write posts defending the far-right with a weird posture of being center-left, Slatestarcodex got an increasingly racist audience, culminating in a spin-off forum with full on 14 words white supremacists. He has played a major role in the alt-right pipeline that is some of Trump's most loyal supporters.

• Lesswrong also attracted some of the neoreactionaries (libertarian wackjobs that want a return to monarchy), among them Menicus Moldbug (real name Curtis Yarvin). Yarvin has written about strategies for dismantling the federal government, which DOGE is now implementing

• Eliezer may not have been much of a researcher himself, but he inspired a bunch of people, so a lot of OpenAI researchers buy into the hype and/or doom. Sam Altman uses Eliezer's terminology as marketing hype.

• As for lesswrong itself... what is original isn't good and what's good isn't original. Lots of the best sequences are just a remixed form of books like Kahneman's "Thinking, Fast and Slow". And the worst sequences demand you favor Eliezer's take on bayesianism over actual science, or are focused on the coming AI salvation/doom.

• other organizations have taken on the "AI safety" mantle. They are more productive than MIRI, in that they actually do stuff with actually implemented 'AI', but what they do is typically contrive (emphasis on contrive) scenarios where LLMs will "act" "deceptive" or "power seeking" or whatever scary buzzword you can imagine and then publish papers about it with titles and abstracts that imply the scenarios are much more natural than they really are.

Feel free to ask any follow-up questions if you genuinely want to know more. If you actually already know about this stuff and are looking for a chance to evangelize for lesswrong or the coming LLM God, the mods can smell that out and you will be shown the door, so don't bother (we get one or two people like that every couple of weeks).

The sequence of links hopefully lays things out well enough for normies? I think it it does, but I've been aware of the scene since the mid 2010s, so I'm not the audience that needs it. I can almost feel sympathy for Sam dealing with all the doomers, except he uses the doom and hype to market OpenAI and he lied a bunch so not really. And I can almost feel sympathy for the board, getting lied to and outmaneuvered by a sociopathic CEO, but they are a bunch of doomers from the sound of it so, eh. I would say they deserve each other, its the rest of the world that don't deserve them (from the teacher dealing with the LLM slop plugged into homework, to the Website Admin fending off scrapers, to legitimate ML researchers getting the attention sucked away while another AI winter starts to loom, to the machine cultist not saving a retirement fund and having panic attacks over the upcoming salvation or doom).

As to cryonics... for both LLM doomers and accelerationists, they have no need for a frozen purgatory when the techno-rapture is just a few years around the corner.

As for the rest of the shiny futuristic dreams, they have give way to ugly practical realities:

• no magic nootropics, just Scott telling people to take adderal and other rationalists telling people to micro dose on LSD

• no low hanging fruit in terms of gene editing (as epistaxis pointed out over on reddit) so they’re left with eugenics and GeneSmith’s insanity

• no drexler nanotech so they are left hoping (or fearing) the god-AI can figure it (which is also a problem for ever reviving cryonically frozen people)

• no exocortex, just over priced google glasses and a hallucinating LLM “assistant”

• no neural jacks (or neural lace or whatever the cyberpunk term for them is), just Elon murdering a bunch of lab animals and trying out (temporary) hope on paralyzed people

The future is here, and it’s subpar compared to the early 2000s fantasies. But hey, you can rip off Ghibli’s style for your shitty fanfic projects, so there are a few upsides.

Even without the Sci-fi nonsense, the political elements of the story also feel absurd: the current administration staying on top of the situation and making reasoned (if not correct) responses and keeping things secret feels implausible given current events. It kind of shows the political biases of the authors that they can manage to imagine the Trump administration acting so normally or competently. Oh and the hyper-competent Chinese spies (and the Chinese having no chance at catching up without them) feels like another one of the authors' biases coming through.

Bonus: a recent comment is skeptical:

well, how do I play democracy with AI? It’s already 2025

We're already behind schedule, we're supposed to have AI agents in two months (actually we were supposed to have them in 2022, but ignore the failed bits of earlier prophecy in favor of the parts you can see success for)!

He made some predictions about AI back in 2021 that if you squint hard enough and totally believe the current hype about how useful LLMs are you could claim are relatively accurate.

His predictions here: https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-looks-like

And someone scoring them very very generously: https://www.lesswrong.com/posts/u9Kr97di29CkMvjaj/evaluating-what-2026-looks-like-so-far

My own scoring:

The first prompt programming libraries start to develop, along with the first bureaucracies.

I don't think any sane programmer or scientist would credit the current "prompt engineering" "skill set" with comparison to programming libraries, and AI agents still aren't what he was predicting for 2022.

Thanks to the multimodal pre-training and the fine-tuning, the models of 2022 make GPT-3 look like GPT-1.

There was a jump from GPT-2 to GPT-3, but the subsequent releases in 2022-2025 were not as qualitatively big.

Revenue is high enough to recoup training costs within a year or so.

Hahahaha, no... they are still losing money per customer, much less recouping training costs.

Instead, the AIs just make dumb mistakes, and occasionally “pursue unaligned goals” but in an obvious and straightforward way that quickly and easily gets corrected once people notice

The safety researchers have made this one "true" by teeing up prompts specifically to get the AI to do stuff that sounds scary to people to that don't read their actual methods, so I can see how the doomers are claiming success for this prediction in 2024.

The alignment community now starts another research agenda, to interrogate AIs about AI-safety-related topics.

They also try to contrive scenarios

Emphasis on the word"contrive"

The age of the AI assistant has finally dawned.

So this prediction is for 2026, but earlier predictions claimed we would have lots of actually useful if narrow use-case apps by 2022-2024, so we are already off target for this prediction.

I can see how they are trying to anoint his as a prophet, but I don't think anyone not already drinking the kool aid will buy it.

I think Eliezer has still avoided hard dates? In the Ted talk, I distinctly recall he used the term "0-2 paradigm shifts" so he can claim prediction success for stuff LLMs do, and paradigm shift is vague enough he could still claim success if its been another decade or two and there has only been one more big paradigm shift in AI (that still fails to make it AGI).

Is this the corresponding lesswrong post: https://www.lesswrong.com/posts/TpSFoqoG2M5MAAesg/ai-2027-what-superintelligence-looks-like-1 ?

Committing to a hard timeline at least means making fun of them and explaining how stupid they are to laymen will be a lot easier in two years. I doubt the complete failure of this timeline will actually shake the true believers though. And the more experienced ~~grifters~~ forecasters know to keep things vaguer so they will be able to retroactively reinterpret their predictions as correct.

I can already imagine the lesswronger response: Something something bad comparison between neural nets and biological neurons, something something bad comparison with how the brain processes pain that fails at neuroscience, something something more rhetorical patter, in conclusion: but achkshually what if the neural network does feel pain.

They know just enough neuroscience to use it for bad comparisons and hyping up their ML approaches but not enough to actually draw any legitimate conclusions.