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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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