They tend not to be stable.
Yes, well that is a tautology. What do you mean by stable? I assume you mean value-stable, which can be interpreted as maximizes-the-same-function-over-time. Something which does not behave as a utility maximizer therefore is pretty much by definition not "stable". By technical definition, at least.
My point was more that this "instability" is in fact the desirable outcome -- people wouldn't want technical-stability, they'd want perhaps a heuristic machine with sensible defaults and rational update procedures.
There are other ways of interpreting value stability; a satisficer is one example. But those don't tend to be stable: http://lesswrong.com/lw/854/satisficers_want_to_become_maximisers/
people wouldn't want technical-stability, they'd want perhaps a heuristic machine with sensible defaults and rational update procedures.
And would those defaults and update procedures remain stable themselves?
I'm soon going to go on a two day "AI control retreat", when I'll be without internet or family or any contact, just a few books and thinking about AI control. In the meantime, here is one idea I found along the way.
We often prefer leaders to follow deontological rules, because these are harder to manipulate by those whose interests don't align with ours (you could say the similar things about frequentist statistics versus Bayesian ones).
What about if we applied the same idea to AI control? Not giving the AI deontological restrictions, but programming with a similart goal: to prevent a misalignment of values to be disastrous. But who could do this? Well, another AI.
My rough idea goes something like this:
AI A is tasked with maximising utility function u - a utility function which, crucially, it doesn't know yet. Its sole task is to create AI B, which will be given a utility function v and act on it.
What will v be? Well, I was thinking of taking u and adding some noise - nasty noise. By nasty noise I mean v=u+w, not v=max(u,w). In the first case, you could maximise v while sacrificing u completely, it w is suitable. In fact, I was thinking of adding an agent C (which need not actually exist). It would be motivated to maximise -u, and it would have the code of B and the set of u+noise, and would choose v to be the worst possible option (form the perspective of a u-maximiser) in this set.
So agent A, which doesn't know u, is motivated to design B so that it follows its motivation to some extent, but not to extreme amounts - not in ways that might sacrifice some of the values of some sub-part of its utility function, because that might be part of the original u.
Do people feel this idea is implementable/improvable?