Part of the problem with a reduced impact AI is that it will, by definition, only have a reduced impact.
Some of the designs try and get around the problem by allowing a special “output channel” on which impact can be large. But that feels like cheating. Here is a design that accomplishes the same without using that kind of hack.
Imagine there is an asteroid that will hit the Earth, and we have a laser that could destroy it. But we need to aim the laser properly, so need coordinates. There is a reduced impact AI that is motivated to give the coordinates correctly, but also motivated to have reduced impact—and saving the planet from an asteroid with certainty is not reduced impact.
Now imagine that instead there are two AIs, X and Y. By abuse of notation, let ¬X refer to the event that the output signal from X is scrambled away from the the original output.
Then we ask X to give us the x-coordinates for the laser, under the assumption of ¬Y (that AI Y’s signal will be scrambled). Similarly, we Y to give us the y-coordinates of the laser, under the assumption ¬X.
Then X will reason “since ¬Y, the laser will certainly miss its target, as the y-coordinates will be wrong. Therefore it is reduced impact to output the correct x-coordinates, so I shall.” Similarly, Y will output the right y-coordinates, the laser will fire and destroy the asteroid, having a huge impact, hooray!
The approach is not fully general yet, because we can have “subagent problems”. X could create an agent that behave nicely given ¬Y (the assumption it was given), but completely crazily given Y (the reality). But it shows how we could get high impact from slight tweaks to reduced impact.
High impact from low impact
A putative new idea for AI control; index here.
Part of the problem with a reduced impact AI is that it will, by definition, only have a reduced impact.
Some of the designs try and get around the problem by allowing a special “output channel” on which impact can be large. But that feels like cheating. Here is a design that accomplishes the same without using that kind of hack.
Imagine there is an asteroid that will hit the Earth, and we have a laser that could destroy it. But we need to aim the laser properly, so need coordinates. There is a reduced impact AI that is motivated to give the coordinates correctly, but also motivated to have reduced impact—and saving the planet from an asteroid with certainty is not reduced impact.
Now imagine that instead there are two AIs, X and Y. By abuse of notation, let ¬X refer to the event that the output signal from X is scrambled away from the the original output.
Then we ask X to give us the x-coordinates for the laser, under the assumption of ¬Y (that AI Y’s signal will be scrambled). Similarly, we Y to give us the y-coordinates of the laser, under the assumption ¬X.
Then X will reason “since ¬Y, the laser will certainly miss its target, as the y-coordinates will be wrong. Therefore it is reduced impact to output the correct x-coordinates, so I shall.” Similarly, Y will output the right y-coordinates, the laser will fire and destroy the asteroid, having a huge impact, hooray!
The approach is not fully general yet, because we can have “subagent problems”. X could create an agent that behave nicely given ¬Y (the assumption it was given), but completely crazily given Y (the reality). But it shows how we could get high impact from slight tweaks to reduced impact.
EDIT: For those worried about lying to the AIs, do recall http://lesswrong.com/r/discussion/lw/lyh/utility_vs_probability_idea_synthesis/ and http://lesswrong.com/lw/ltf/false_thermodynamic_miracles/