Ah, OK. I think I get you now.
I agree that whether we know the mechanism doesn't matter.
Personally, I'm comfortable saying that gravity is an optimization process. The interesting question is what gravity optimizes for. I might conclude, for example, after watching how gravitational fields affect matter, that gravity optimizes for minimizing the distance between sources of mass.
From that it follows that gravity is not a particularly powerful optimization process. I conclude this because I observe many situations where distance between masses is no longer being minimized because gravity only has a very limited way of arranging its environment to achieve that goal. And I suspect that one of the things you're looking for, in attempting to arrive at a definition that distinguishes gravity from Clippy, is a notion of optimization power similar to this. In other words, it's possible that your question can be rephrased as "how do we measure optimization power?"
Another possible distinction among optimization processes that we often implicitly talk about here is value-independence. That is, when we talk about AGI, what's being evoked is a powerful optimization process that can optimize for paperclips, or shoes, or smileyfaces, or satisfied humans, or whatever it happens to value. It's just as powerful an optimization process either way. Gravity doesn't seem to have this property. Clippy might or might not.
The general assumption around here is that something as effective as Clippy is using algorithms which are generalizable; I'm not sure I've ever seen the idea of a non-generalizable powerful optimization process even discussed here. I suspect this derives in large part from the site's focus on Bayes Theorem, which is entirely domain-independent, as the core of intelligence/optimization.
This focus is in-principle separable from the site's focus on optimizing systems, but in practice the two are not explicitly separated during discussion.
But it seems like then every process can be an optimisation process, and when you measure the optimisation power that's really telling you more about whether the 'optimisation target' you selected as your measure is a good fit for the process you're looking at. It tells you more about your interpretation of the optimisation target than it does about the process itself.
Gravity isn't very powerful for minimising distance between sources of mass, but it is very powerful for "making mass move in straight lines through curved spacetime"[1]. For any pr...
Today's post, Aiming at the Target was originally published on 26 October 2008. A summary (taken from the LW wiki):
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