At the recent London meet-up someone (I'm afraid I can't remember who) suggested that one might be able to solve the Friendly AI problem by building an AI whose concerns are limited to some small geographical area, and which doesn't give two hoots about what happens outside that area. Cipergoth pointed out that this would probably result in the AI converting the rest of the universe into a factory to make its small area more awesome. In the process, he mentioned that you can make a "fun game" out of figuring out ways in which proposed utility functions for Friendly AIs can go horribly wrong. I propose that we play.
Here's the game: reply to this post with proposed utility functions, stated as formally or, at least, as accurately as you can manage; follow-up comments explain why a super-human intelligence built with that particular utility function would do things that turn out to be hideously undesirable.
There are three reasons I suggest playing this game. In descending order of importance, they are:
- It sounds like fun
- It might help to convince people that the Friendly AI problem is hard(*).
- We might actually come up with something that's better than anything anyone's thought of before, or something where the proof of Friendliness is within grasp - the solutions to difficult mathematical problems often look obvious in hindsight, and it surely can't hurt to try
Define "Interim Friendliness" as a set of constraints on the AI's behavior which is only meant to last until it figures out true Friendliness, and a "Proxy Judge" as a computational process used to judge the adequacy of a proposed definition of true Friendliness.
Then there's a large class of Friendliness-finding strategies where the AI is instructed as follows: With your actions constrained by Interim Friendliness, find a definition of true Friendliness which meets the approval of the Proxy Judge with very high probability, and adopt that as your long-term goal / value system / decision theory.
In effect, Interim Friendliness is there as a substitute for a functioning ethical system, and the Proxy Judge is there as a substitute for an exact specification of criteria for true Friendliness.
The simplest conception of a Proxy Judge is a simulated human. It might be a simulation of the SIAI board, or a simulation of someone with a very high IQ, or a simulation of a random healthy adult. A more abstracted version of a Proxy Judge might work without simulation, which is a brute-force way of reproducing human judgment, but I think it would still be all about counterfactual human judgments. (A non-brute-force way of reproducing human judgment is to understand the neural and cognitive processes which produce the judgments in question, so that they may be simulated at a very high level of abstraction, or even so that the final judgment may be inferred in some non-dynamical way.)
The combination of Interim Friendliness and a Proxy Judge is a workaround for incompletely specified strategies for the implementation of Friendliness. For example: Suppose your blueprint for Friendliness is to apply reflective decision theory to the human decision architecture. But it turns out that you don't yet have a precise definition of reflective decision theory, nor can you say exactly what sort of architecture underlies human decision making. (It's probably not maximization of expected utility.) Never fear, here are amended instructions for your developing AI:
With your actions constrained by Interim Friendliness, come up with exact definitions of "reflective decision theory" and "human decision architecture" which meet the approval of the Proxy Judge. Then, apply reflective decision theory (as thus defined) to the human decision architecture (as thus defined), and adopt the resulting decision architecture as your own.