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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Truth and Advantage: Response to a draft of "AI safety seems hard to measure", published by So8res on March 22, 2023 on LessWrong.
Status: This was a response to a draft of Holden's cold take "AI safety seems hard to measure". It sparked a further discussion, that Holden recently posted a summary of.
The follow-up discussion ended up focusing on some issues in AI alignment that I think are underserved, which Holden said were kinda orthogonal to the point he was trying to make, and which didn't show up much in the final draft. I nevertheless think my notes were a fine attempt at articulating some open problems I see, from a different angle than usual. (Though it does have some overlap with the points made in Deep Deceptiveness, which I was also drafting at the time.)
I'm posting the document I wrote to Holden with only minimal editing, because it's been a few months and I apparently won't produce anything better. (I acknowledge that it's annoying to post a response to an old draft of a thing when nobody can see the old draft, sorry.)
Quick take: (1) it's a write-up of a handful of difficulties that I think are real, in a way that I expect to be palatable to a relevant different audience than the one I appeal to; huzzah for that. (2) It's missing some stuff that I think is pretty important.
Slow take:
Attempting to gesture at some of the missing stuff: a big reason deception is tricky is that it is a fact about the world rather than the AI that it can better-achieve various local-objectives by deceiving the operators. To make the AI be non-deceptive, you have three options: (a) make this fact be false; (b) make the AI fail to notice this truth; (c) prevent the AI from taking advantage of this truth.
The problem with (a) is that it's alignment-complete, in the strong/hard sense. The problem with (b) is that lies are contagious, whereas truths are all tangled together. Half of intelligence is the art of teasing out truths from cryptic hints. The problem with (c) is that the other half of intelligence is in teasing out advantages from cryptic hints.
Like, suppose you're trying to get an AI to not notice that the world is round. When it's pretty dumb, this is easy, you just feed it a bunch of flat-earther rants or whatever. But the more it learns, and the deeper its models go, the harder it is to maintain the charade. Eventually it's, like, catching glimpses of the shadows in both Alexandria and Syene, and deducing from trigonometry not only the roundness of the Earth but its circumference (a la Eratosthenes).
And it's not willfully spiting your efforts. The AI doesn't hate you. It's just bumping around trying to figure out which universe it lives in, and using general techniques (like trigonometry) to glimpse new truths. And you can't train against trigonometry or the learning-processes that yield it, because that would ruin the AI's capabilities.
You might say "but the AI was built by smooth gradient descent; surely at some point before it was highly confident that the earth is round, it was slightly confident that the earth was round, and we can catch the precursor-beliefs and train against those". But nope! There were precursors, sure, but the precursors were stuff like "fumblingly developing trigonometry" and "fumblingly developing an understanding of shadows" and "fumblingly developing a map that includes Alexandria and Syene" and "fumblingly developing the ability to combine tools across domains", and once it has all those pieces, the combination that reveals the truth is allowed to happen all-at-once.
The smoothness doesn't have to occur along the most convenient dimension.
And if you block any one path to the insight that the earth is round, in a way that somehow fails to cripple it, then it will find another path later, because truths are interwoven. Tell one lie...
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