Takeoff Speed II: Recalcitrance in AI pathways to Superintelligence.

I’m writing a series of posts clarifying my position on the intelligence explosion hypothesis. Last time I took a look at various non-AI pathways to Superintelligence and concluded that the recalcitrance profile for most of them was moderate to high.

This doesn’t mean it isn’t possible to reach Superintelligence via these routes, but it does indicate that doing so will probably be difficult even by the standards of people who think about building Superintelligences all day long.

AI-based pathways to Superintelligence might have lower recalcitrance than these alternatives, because of a variety of advantages a software mind could have over a biological one.

These advantages have been discussed at length elsewhere, but relevant to the present discussion is that software minds could have far greater introspective access to their own algorithms than humans do.

Of course programmers building such a mind might fear an intelligence explosion and endeavor to prevent this sort of deep introspection. But in principle an AI with such capabilities could become smart enough to start directly modifying and improving its own code.

Humans can only do a weak sort of introspection, and therefore can only do a weak sort of optimization to their thinking patterns. So far, anyway.

At a futurist party recently I was discussing these ideas with someone and they asked me what might happen if a recursively self-improving AI hit diminishing returns on each optimization. Might an intelligence explosion just sort of… fizzle out?

The answer is yes, that might happen. But so far as I can tell there isn’t any good reason to assume that that will happen, and thus the safest bet is to act as though it probably will happen and start thinking hard about how to steer this runaway process in a direction that leads to a valuable future.

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