My current attitude towards the Löbian obstacle is “just live with it.” This post outlines that view and some of the underlying intuitions.
To show I’m being a good sport, I’ll also propose a new reflection principle. But my best guess for the right answer remains “live with it.” Continue reading
My current preferred formalization of extrapolation of an agent’s preferences rests on imagining what would happen if that agent was provided with an idealized environment in which it could undergo an extensive process of reflection. It is clear that this is not a completely satisfactory account, though there is uncertainty about whether it is “good enough” for the intended use case.
One crisp difficulty is the following: this approach relies completely on the agent wanting you to know its extrapolated preferences.
Suppose that I have in hand a perfect model of my decision-making process, and I am interested in using this to define what I would believe, want, or do “upon reflection.” That is, in general I can use this model to define my current best guess as to the answer, but I might also be interested in talking about my “enlightened judgment,” if I knew all of the facts and considered all of the arguments and were more the person I wish I were and so on. Can we give a satisfactory formal definition of my enlightened judgment in terms of this literal model of my decision-making process?
Suppose I want to provide a completely precise specification of “me,” or rather of the input/output behavior that I implement. How can I do this? I might be interested in this problem, for example, because it appears to be a primary difficulty in providing a precise specification of “maximize the extent to which I would approve of your decision upon reflection.” (I have suggested that we would be happy with a powerful AI that made decisions according to this maxim.)
I have written about this issue in the past; in this post I’ll outline a slightly improved scheme (now with 100% fewer faraday cages). The technical changes are relatively modest, but I’m also taking a somewhat different approach to the issue, and overall I think it seems much more like the kind of thing that could actually be done. I also want to take the opportunity to try to clarify and expand the exposition some, since I think that the amount of discussion and thought that this idea has gotten now vastly surpasses the amount of care that went into crafting the original exposition.
I welcome additional objections to this scheme. As usual I think the literal proposal laid out here is extremely unlikely to ever be used. However, finding problems with this proposal can still be useful for shedding light on the problem, and in particular on how difficult it is and where the difficulties lie.
Will machine intelligences communicate with humans by directly exposing or reporting properties of their internal state, or will they tend to communicate by strategically choosing utterances that they think will have the intended effect on the listener? In this post I try to lay out the distinction more clearly and describe some differences.
I suggest that it would be desirable if powerful agents acted in the way we would most approve of, upon reflection. I argue that this definition can be made unusually precise, would lead to positive outcomes, and is robust to conceptual imprecision, philosophical errors, and unfavorable circumstances.
Suppose that I have hired a group of employees who are much smarter than I am. For some tasks it may be easy to get useful work out of them: for example, if I am interested in finding a good layout for the components on a chip and can easily evaluate the quality of a proposed layout, I can simply solicit proposals, test the proposals, and award the employees according to the quality of their proposals.
However, for some kinds of tasks there may be fundamental problems with adopting this kind of incentive-based policy. For example, suppose I am interested in working with these employees to build an AI which is not only much smarter than any of us, but which will act autonomously in support of my values even when I can’t monitor its behavior. In cases like this, I will have to try something different. Continue reading