timtyler comments on Ben Goertzel: The Singularity Institute's Scary Idea (and Why I Don't Buy It) - Less Wrong
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I didn't get the impression that Eliezer's goal was to "build a provably Friendly AI" (in the mathematical sense of "provable"), as Ben puts it. The impression I get is more that Eliezer wants to put off building an AI until we understand enough about morality and human values. Eliezer also cares about mathematical proofs, but more for the purpose of preserving values under self-modification (something that humans don't usually have to deal with).
As an analogy, imagine you're trying to debug some complex and badly written code you were previously unfamiliar with. One approach is to find the bit in the code that seems related to the bug, and modify it locally ( "if DatabaseDown() return False" and the like) until the issue seems fixed. Another approach is to try to understand how the program works to the point where you understand which conceptual mistake caused the bug, and see the right way to fix it.
The second approach takes more time but is also less likely to create another bug somewhere else, or to deteriorate the overall quality of the code. I think most programmers who've worked on sufficiently large codebases have seen examples of both approaches.
Anyway, I get the impression that Eliezer is advocating something like the second approach here (understand how everything works before implementing), and that Ben is describing that as "proving correctness", which seems to be quite different (and much stronger!).
Yes, that's what I was referring to when saying this:
The provability here has to do with the AI proving to itself that modifying itself will preserve it's values (or not cause it to self-destruct or wirehead or whatever), not the designers proving the AI is non-dangerous.
I.e. friendly as "provably non-dangerous AGI" doesn't necessarily mean having a rigorous mathematical proof that the AI is not dangerous; but "merely" having enough understanding of morality when building it (as opposed to some high-level notions whose components haven't been rigorously analyzed).