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shokwave comments on Closet survey #1 - Less Wrong
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Observing, thinking, having ideas, and communicating with other people doing these things. Nothing surprising there. No-one has yet come up with a general algorithm for discovering new and interesting truths; if they did it would be an AGI.
Taking a wider view of this, it has been observed that every time some advance is made in the mathematics or technology of information processing, the new development is seized on as a model for how minds work, and since the invention of computers, a model for how minds might be made. The ancient Greeks compared it to a steam-driven machine. The Victorians compared it to a telephone exchange. Freud and his contemporaries drew on physics for their metaphors of psychic energies and forces. When computers were invented, it was a computer. Then holograms were invented and it was a hologram. Perceptrons fizzled because they couldn't even compute an XOR, neural networks achieved Turing-completeness but no-one ever made a brain out of them, and logic programming is now just another programming style.
Bayesian inference is just the latest in that long line. It may be the one true way to reason about uncertainty, as predicate calculus is the one true way to reason about truth and falsity, but that does not make of it a universal algorithm for thinking.
I didn't get the impression that Bayesian inference itself was going to produce intelligence; the impression I have is that Bayesian inference is the best possible interface with reality. Attach a hypothesis-generating module to one end and a sensor module to the other and that thing will develop the correctest-possible hypotheses. We just don't have any feasible hypothesis-generators.
I do get that impression from people who blithely talk of "Bayesian superintelligences". Example. What work is the word "Bayesian" doing there?
In this example, a Bayesian superintelligence is conceived as having a prior distribution over all possible hypotheses (for example, a complexity-based prior) and using its observations to optimally converge on the right one. You can even make a theoretically optimal learning algorithm that provably converges on the best hypothesis. (I forget the reference for this.) Where this falls down is the exponential explosion of hypothesis space with complexity. There no use in a perfect optimiser that takes longer than the age of the universe to do anything useful.