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Scott Alexander, the guy who hosted the Culture War Thread on his blog slatestarcodex before that landed him in so much heat (from the woke left) that it was moved to reddit under the name /r/theMotte (and from there, subsequently, to themotte.org to preempt a reddit crackdown) comes usually across as a kind and considerate person to me. Charitable interpretations are very much his thing. While he has always engaged in CW topics on occasion, his forays deep into the trenches are rare (and in my impression have become rarer compared to the SSC days).
Apparently, there is a person called Steven Pinker. I have been vaguely aware that there was a book called The Better Angels of Our Nature which was probably authored by someone, but he was not someone I really had on my radar.
From the WP article, he seems solidly Grey Tribe. Believes in a computational theory of mind, Ashkenazi Intelligence Hypothesis, critical of both the woke left (cancellations) and the MAGA right (foreign student restrictions).
The main topic where he disagrees with Scott Alexander is AI x-risk.
Scott has now responded, and his reponse is a bit less than maximum charitable.
While Scott is diligent enough with his technical points, it is clear that he is personally hurt by Pinker's decision to ridicule AI x-risk. So his post is also a broadside fired against him. He cites Garfinkel et al 2017 who "proved" that machines will never be larger than humans using common philosophical arguments for AI being harmless.
Scott points out that while Pinker is busy berating the LW crowd for distracting from the more mundane risks of AI, x-risk advocates are willing to work hand in hand with groups with a more practical focus to combat pro-AI PACs like Leading the Future.
On model welfare (which is certainly not the core topic of the x-risk advocates):
He accuses Pinker of endorsing the "lowest quality voices", quoting Claire Lehmann:
Or
It ends with Scott seeming seriously willing to duel Pinker:
Scott has admitted in the comments that this is already the toned-down version of the post he originally wrote. I think that while he held back verbally (nobody get's called a "Vogon spy in a skin suit" this time), he uses his craft well to show his frustration.
For what it's worth, I think that Pinker is correct that Eliezer is in fact over-confident in p(doom|default-ASI). Just like Pinker is when he declares that any fear of doom is ridiculous.
The main difference is that for practical purposes, it does not matter much if your p(doom) is 0.99 or 0.05. Both would be pressing problems whose solution is the most important thing in the world.
Eliezer was bullish on AI capabilities when that was far out of the Overton window, back when the machines could beat humans at chess at most. Steven Pinker was tweeting that "deep learning has probably peaked" in 2019.
This might be giving too much credit to Pinker, but here is Clarke's first law:
I'd say that I lost some respect for both of them through this argument, but to be honest I don't think I ever had much respect for Pinker. The ignorant and strawmannish Enlightenment Now really ended any sympathy I had for him as a thinker. Nonetheless, Pinker's letter to Scott was courteous and concise; I think you can tell that Pinker is a better or at least more professional writer.
Scott's response... to be honest, it reminded me of point six here. The world Scott is in is small and homogenous - there's an awful lot of citing Omohundro and Hinton, and after a while one starts to think that that's because he's only got a handful of people, and is shocked and appalled that anybody disagrees with them. Scott does land some real hits on Pinker - the RAND study mistake is an embarrassing mis-step by Pinker - but also avoids some of Pinker's stronger arguments, and sets up his own straw men.
I'm not particularly interested in nitpicking many of the specific disagreements, especially because plenty of them leave ample room for both to be, in a sense, right. Pinker is correct that 'intelligence' can't be modelled as a single variable; Scott is correct that nonetheless it is reasonable to talk about 'intelligence' in a general sense.
What stood out to me, personally, was the way that the mask came down a bit, and setting aside all disputes about facts, Scott is dedicated to normative commitments that I think are repulsive, or frankly insane. Consider:
I'm sorry, but this is lunatic. We need to invest in language models or human civilisation will be permanently stunted? What? He's got some sort of science fiction eschatology in mind that seems both absurd on the factual merits and, perhaps more importantly, morally blinkered. While I did always know that Scott had some of those bizarre transhumanist beliefs (and I'm certainly not a convert to Phil-Torres-like conspiracism), it is, at least, a useful reminder that he approaches the whole issue of technology from a bizarre starting point that I cannot sympathise with.
This is a fair point. Based on people's answers to questions like "does AI pose an existential threat to the human race?", you should not be able to predict their opinions about completely unrelated topics like polyamory, gender ideology and factory farming. But in practice, you can, and with remarkable accuracy.
If I pointed this out to anyone in this Bay Area bubble, they would scoff and say that believing 2+2=4 is heavily correlated with belief in gravity and natural selection: the reason we all have the same opinions about AI, polyamory and gender ideology is because smart people who believe one obviously true thing tend to believe in other obviously true things as well. We're the "reality-based community"!* (Feel free to make your own "rationalist"-themed lawn sign.)
The less charitable explanation is that the Bay Area has become an ideological echo chamber, and that this is bad. A lack of viewpoint diversity will probably hurt the AI doomer movement in the long run, in ways that ironically mirror what's going to happen to future LLMs. With so much content (text, images, videos, music) on the internet now being AI-generated, the next generation of LLMs will be heavily trained on content which is itself AI-generated, resulting in incestuous artefacts and mode collapse. Similarly, the opportunity for novel, creative solutions is massively curtailed if everyone is reading the same articles, donating to the same candidates, attending the same house parties and fucking (literally) the same people.**
Scott complains about the way Pinker characterises the AI doomers as a "cult" who recite the "catechism" of their creed. Comparing group X to a religion or a cult is indeed a cheap way to make that group seem ridiculous, and the fact that so many AI doomers have similar ideas and concerns could well be evidence of independent convergence as opposed to top-down indoctrination. But leaving AI aside – surely Scott would agree that for people in this space who live in the Bay Area, there is a catechism, right? There are opinions unrelated to AI that you're expected to hold, opinions you're expected not to hold, and expressing those in the latter group will land you in hot water. I'm not persuaded that a genuinely brilliant AI researcher with novel ideas about how to improve alignment (but unorthodox opinions about gender ideology, which he makes no effort to hide) would be welcome in the Bay Area, and I think that should be cause for concern for anyone who's worried about AI as an existential risk. If nothing else, it makes you seem unserious, in the same way that buying up seafront property makes people reasonably question how serious you are about rising sea levels. "AI IS GOING TO KILL US ALL, ANYONE WITH A MODICUM OF CODING SKILL SHOULD DROP EVERYTHING AND HELP OUT (those exclusively in favour of monogamous relationships need not apply)" is not a good look.
"What – just because AI will kill us all, we have to stop caring about shrimp welfare and throw our trans comrades under the bus?" Part of me wants to say yeschad.jpg. Another part of me says: look, you guys are the ones saying that any intelligent agent with a goal would organically converge on the sub-goal of self-preservation, natural selection be damned. You can't advocate for trans rights if you're dead. So either you don't really think AI will kill us all, or you'd better start holding your nose and tolerating the presence of talented, creative individuals with whom you disagree.
*I'm sure Scott found it suspicious that, in 2020, support for lockdowns was so heavily correlated with support for ignoring those lockdowns to attend BLM protests – but he finds the same phenomenon unremarkable when it afflicts his in-group. The speck in your brother's eye, the plank in yours etc.
**There was an article in the Washington Post where someone is quoted as saying "All of the Zoomers that work for me are bisexual, and all of them have long covid. I’ll believe long covid is real when someone who is not bisexual has it." I saw a post on Substack the other day which edited it to read "I'll believe AI poses a threat to the human race when someone who hasn't had sex with Aella says so."
Why do people in the "Ai is hype camp" always believe every inane conspiracy theory? No model collapse is not coming as a result of ingesting AI generated content. People have been making this claim for several model generations and it's just not happening. Even if it was a real problem classifiers for what text is ai vs human generated work and can easily be deployed to filter AI generated content. More importantly more compute is now spent on post training than the main training run, you only need so good a base model to then scale RL.
It is, but its not called Model Collapse, the specific technical phenomenon is called Mode Collapse. I have no idea if it is being accidentally confused here.
It can occur when synthetic data is less diverse than real data and outnumbers it. The model learns a narrower distribution, which occasionally cascades to mode collapse. Filtering isn't really a solution to deal with it, because the synthetic data is often being deliberately used. That might be a different problem than the LLM use-case assuming they aren't deliberately trying to train on other AI generated data, but I would doubt that if it starts happening its because they "accidentally" ingested too much AI-content vs a deliberate choice to economically generate synthetic data to perform better.
Are you sure? Or maybe I'm misreading your comment. Model collapse is the one where synthetic data swamps the real data but is mitigated by a real external reward signal. Mode collapse has to do with over use of RL.
Mode collapse has, to my recollection, nothing to do with RL. It was first discovered in GANs and has been a staple of generative AI since. It is however not a generational phenomenon, but I suppose it could be a longitudinal trend if each progressive model suffers more and more from it.
Maybe I could try and explain it better. Starting from the knowledge that human writing/art exists as a distribution, with a concentration around the mean and smaller tails. An AI model will tend to generate data closer to the mean, commonly called regression to the mean. If the AI generates too much synthetic data at the mean, and that data is combined with real data, it eventually will shift the mean, making the tails smaller, creating a feedback loop where each successive training run concentrates the data closer to the synthetic mean. At some point it ceases to express the full actual range of human arts/writing. This occurrence in the first model is called mode collapse, and as the models enter into the feedback loop, it gets worse.
The wiki definition isn't bad either:
I don't think I disagree with this definition of mode collapse, but I'm pretty sure what the previous poster was describing was model collapse which is all about synthetic data.
I think you kind of need to stretch the definition of mode collapse to the breaking point for it to fit better than that.
Even when they first said model collapse I couldn't remember which it was but did look it up and I'm still pretty sure model collapse fits better here.
That's probably true, I think model collapse is the specific phenomenon that has been hypothesized. But I would also theorize that model collapse (2024) has inherited its concepts from mode collapse (2014). Mode Collapse is a far more general statistical property, it can occur without synthetic data, and it does not necessarily have to occur with synthetic data that has been forced to represent the full distribution. It just also very commonly occurs in synthetic data trained models.
The model collapse definition's additional nomenclature, gives non-rigorous, non-technical vibes.
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That's what I was getting at, mea culpa.
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