The idea of splitting a high impact task between two low-impact AIs has on critical flaw. AI X is aiming for low impact, conditional on ¬Y (the other AI not being turned on, or not outputting a message, or something similar). “Outputting the right coordinates” is one way that X can accomplish its goal. However, there is another way it can do it: “create a robot that will output the right coordinates if ¬Y, and [do something else] if Y.”
That’s a dangerous situation to be in, especially if we have a more general situation that the “laser aiming at the asteroid”. But note that if X does create such a robot, and if ¬Y is actually true, then that robot must be low impact and not dangerous, since that’s X’s programming. Since X cannot predict all the situations the robot would encounter, the robot is probably generically “safe” and low impact.
Therefore, if the robot behaves the same way under Y and ¬Y, we’re good.
How could we achieve that? Well, we could adapt my idea from “restrictions that are hard to hack”. If a hypothetical superintelligent AI C observed the output stream from X, could it deduce that Y vs ¬Y was something important in it? If C knew that X was conditioning on ¬Z, but didn’t know Z=Y, could it deduce that? That seems like a restriction that we could program into X, as a third component of its utility (the first being the “do what we want” component, the second being the “have a reduced impact conditional on ¬Z” one).
And if we have a “safe” robot, given ¬Y, and the programming of that robot does not (explicitly or implicitly) mention Y or its features, we probably have a safe robot.
The idea still needs to be developed and some of the holes patched, but I feel it has potential.
High impact from low impact, continued
A putative new idea for AI control; index here.
The idea of splitting a high impact task between two low-impact AIs has on critical flaw. AI X is aiming for low impact, conditional on ¬Y (the other AI not being turned on, or not outputting a message, or something similar). “Outputting the right coordinates” is one way that X can accomplish its goal. However, there is another way it can do it: “create a robot that will output the right coordinates if ¬Y, and [do something else] if Y.”
That’s a dangerous situation to be in, especially if we have a more general situation that the “laser aiming at the asteroid”. But note that if X does create such a robot, and if ¬Y is actually true, then that robot must be low impact and not dangerous, since that’s X’s programming. Since X cannot predict all the situations the robot would encounter, the robot is probably generically “safe” and low impact.
Therefore, if the robot behaves the same way under Y and ¬Y, we’re good.
How could we achieve that? Well, we could adapt my idea from “restrictions that are hard to hack”. If a hypothetical superintelligent AI C observed the output stream from X, could it deduce that Y vs ¬Y was something important in it? If C knew that X was conditioning on ¬Z, but didn’t know Z=Y, could it deduce that? That seems like a restriction that we could program into X, as a third component of its utility (the first being the “do what we want” component, the second being the “have a reduced impact conditional on ¬Z” one).
And if we have a “safe” robot, given ¬Y, and the programming of that robot does not (explicitly or implicitly) mention Y or its features, we probably have a safe robot.
The idea still needs to be developed and some of the holes patched, but I feel it has potential.