Zack M. Davis


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how he confidently dismisses ANNs

I don't think this is a fair reading of Yudkowsky. He was dismissing people who were impressed by the analogy between ANNs and the brain. I'm pretty sure it wasn't supposed to be a positive claim that ANNs wouldn't work. Rather, it's that one couldn't justifiably believe that they'd work just from the brain analogy, and that if they did work, that would be bad news for what he then called Friendliness (because he was hoping to discover and wield a "clean" theory of intelligence, as contrasted to evolution or gradient descent happening to get there at sufficient scale).

Consider "Artificial Mysterious Intelligence" (2008). In response to someone who said "But neural networks are so wonderful! They solve problems and we don't have any idea how they do it!", it's significant that Yudkowsky's reply wasn't, "No, they don't" (contesting the capabilities claim), but rather, "If you don't know how your AI works, that is not good. It is bad" (asserting that opaque capabilities are bad for alignment).

I second Rob's unanswered question at 40:12: how is that we ever accomplish anything in practice, if the search space is vast, and things that both work and look like they work are exponentially rare?

How is the "the genome is small, therefore generators of human values (that can't be learned from the environment) are no more complex than tens or hundreds of things on the order of a fuzzy face detector" argument compatible with the complexity of value thesis, or does it contradict it?

As is demonstrated by the Hashlife algorithm, that exploits the redundancies for a massive speedup. That's not possible for things like SHA-256 (by design)!

I can't for the life of me remember what this is called

Shapley value

(Best wishes, Less Wrong Reference Desk)