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Sooo, Big Yud appeared on Lex Fridman for 3 hours, a few scattered thoughts:
Jesus Christ his mannerisms are weird. His face scrunches up and he shows all his teeth whenever he seems to be thinking especially hard about anything, I didn't remember him being this way in the public talks he gave a decade ago, so this must either only be happening in conversations, or something changed. He wasn't like this on the bankless podcast he did a while ago. It also became clear to me that Eliezer cannot become the public face of AI safety, his entire image, from the fedora, to the cheap shirt, facial expressions and flabby small arms oozes "I'm a crank" energy, even if I mostly agree with his arguments.
Eliezer also appears to very sincerely believe that we're all completely screwed beyond any chance of repair and all of humanity will die within 5 or 10 years. GPT4 was a much bigger jump in performance from GPT3 than he expected, and in fact he thought that the GPT series would saturate to a level lower than GPT4's current performance, so he doesn't trust his own model of how Deep Learning capabilities will evolve. He sees GPT4 as the beginning of the final stretch: AGI and SAI are in sight and will be achieved soon... followed by everyone dying. (in an incredible twist of fate, him being right would make Kurzweil's 2029 prediction for AGI almost bang on)
He gets emotional about what to tell the children, about physicists wasting their lives working on string theory, and I can see real desperation in his voice when he talks about what he thinks is really needed to get out of this (global cooperation about banning all GPU farms and large LLM training runs indefinitely, on the level of even stricter nuclear treaties). Whatever you might say about him, he's either fully sincere about everything or has acting ability that stretches the imagination.
Lex is also a fucking moron throughout the whole conversation, he can barely even interact with Yud's thought experiments of imagining yourself being someone trapped in a box, trying to exert control over the world outside yourself, and he brings up essentially worthless viewpoints throughout the whole discussion. You can see Eliezer trying to diplomatically offer suggested discussion routes, but Lex just doesn't know enough about the topic to provide any intelligent pushback or guide the audience through the actual AI safety arguments.
Eliezer also makes an interesting observation/prediction about when we'll finally decide that AIs are real people worthy of moral considerations: that point is when we'll be able to pair midjourney-like photorealistic video generation of attractive young women with chatGPT-like outputs and voice synthesis. At that point he predicts that millions of men will insist that their waifus are actual real people. I'm inclined to believe him, and I think we're only about a year or at most two away from this actually being a reality. So: AGI in 12 months. Hang on to your chairs people, the rocket engines of humanity are starting up, and the destination is unknown.
Was the gap between GPT-4 and GPT-3 bigger than the gap between GPT-3 and GPT-2?
It feels smarter than an average person. Which is to say, smarter than the weighted average of its dataset.
I think Yud expected it to be a «blurry JPEG of the web», a mere interpolation of the training data, like many naysayers believe about transformers – simply more comprehensive than 3.5, multimodal, maybe nicer in other negligible consumer-friendly ways. If so, its sharpness naturally alarmed him.
Didn't alarm me though, it's all expected iterative enhancement since GPT-2.
Yud just doesn't have the first idea about neural networks and is dismissive of the premise that you can get this smart on mere general human data, no «architecture of the mind», no recursive self-improvement. Doesn't believe in the Bitter Lesson too.
Wait, Yudkowsky doesn't believe in the scaling hypothesis? That's super interesting to me! Has he written about this? That could put a hamper on any kind of FOOM scenario. Rather than crack some kind of grand theory of intelligence, a super intelligence would need to hijack trillions of dollars worth of computing resources to gain a competitive advantage and a correspondingly huge amount of training data.
I believe that's partially Altman's argument for accelerating in the moment, the «short timelines – slow takeoff» policy as he puts it. LLMs are not nearly the perfect way to build intelligence – they're perhaps as clunky as we are, only in a different manner. But that's a blessing. They are decidedly non-agentic, they have trouble with qualitative self-improvement, they can interpret their code no more than we do, and they are fat. (Though they sure can supervise training, and LLaMA tunes show you might not need a lot of data to transfer a meaningfully different character with enhanced capabilities, especially if these things proliferate – a tiny LoRA plus a prompt and external address for extra info to include in context will allow a some new «Sydney» to spread like wildfire).
As for Yud: like @georgioz says, it's more sophisticated. Now he admits that scaling (and other tricks) clearly suffices to achieve some nontrivial capabilities – hence his recent insistence on shutting it all down, of course; he expects GPT-5-class models to be dangerous, if not FOOMing yet. In that fragment he says that, at least circa 2006, he did not distinguish neural networks, expert systems and evolutionary algorithms, which probably explains why he acted (and still acts) as a maverick tackling hitherto-unforeseen problems: if you ditch the lion's share of GOFAI and connectionism, you aren't left with a ton of prior art. Less charitably, he was just ignorant.
Recapitulating human brain evolution is computationally intractable, far as we know, so his retroactive concession is rather stingy. People like Hinton apparently knew in advance, for all this time, that with a million times more compute neural networks will learn well enough. But all Yud had to say back then was that it's stupid to hope to build intelligence «without understanding how intelligence works» and all he has to say now is that it's a «stupid thing for a species to do». His notion of understanding intelligence is not much more sophisticated than his political propositions – I gather he thinks it's to be some sort of modular crap with formulas (probably Bayes rule as the centerpiece) written out explicitly for some rudimentary machine to interpret, the mathematically rigorous Utility function capturing personal moral code of the developers, and so on, basically babby's first golem.
Back when he hoped to actually build something, he thought the following:
(From another document)
And:
Then a massive list of subdomains that is is basically a grab-bag of insight porn we've been awash in for the last two decades, presumably cultivating sparks of AGI in lesswrong and /r/slatestarcodex regulars.
Unfortunately it seems like linear algebra is just about enough.
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In this specific interview Yud said something along the lines that of course scaling is capable of producing general intelligence - in the end evolution did that blindly with human brain so it should be possible. He was just more sceptical regarding gains by more compute. Needless to say, he is less sceptical now.
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People will never believe this, but the general vibe when the GPT-3 paper came out was that it was a disappointment. I think all of the tools and tricks that made it useful were developed after initial release.
GPT-3 was something like an autistic-savant toddler.
Like, if you showed it a chessboard and how the pieces moved, it could get the general idea and 'play' the game, but it would make stupid or illegal moves, and clearly couldn't have a 'goal' in mind.
it could talk coherently but was horrible at conveying meaning.
GPT-4 appears to have jumped straight to autistic-savant teenager.
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I am someone with little to no technical no-how but my intuitive sense having played around a little with all these models is that the leap from 3 to 4 didn't seem nearly as massive as some of last winter's hype would have suggested.
3 to 4, or 3.5 to 4?
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Very difficult to tell. The only actual training metric is the average log probability of getting the next word correct, and in that metric the gap between GPT3 and GPT2 is larger than that between 4 and 3, but understanding how that metric maps onto our own intuitive notions of "performance" is really hard. And human perceptions of intelligence are really only sensitive to small changes around the human average, I think GPT2 was too dumb in an unusual way for people to really get a good sense of its capabilities.
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