I agree with everything you’ve said. Obviously, AI (in most domains) would need to evaluate its plans in the real world to acquire training data. But my point is that we have the choice to not carry out some of the agent’s plans in the real-world. For some of the AI’s plans, we can say no—we have a veto button. It seems to me that the AI would be completely fine with that—is that correct? If so, it makes safety a much more tractable problem than it otherwise would be.
The problem is that at the beginning, its plans are generally going to be complete nonsense. It has to have a ton of interaction with (at least a reasonable model of) its environment, both with its reward signal and with its causal structure, before it approaches a sensible output.
There is no utility for the RL agent’s operators to have an oracle AI with no practical experience. The power of RL is that a simple feedback signal can teach it everything it needs to know to act rationally in its environment. But if you want it to make rational plans for the real world without actually letting it get direct feedback from the real world, you need to add on vast layers of additional computational complexity to its training manually, which would more or less be taken care of automatically for an RL agent interacting with the real world. The incentives aren’t in your favor here.
I agree with everything you’ve said. Obviously, AI (in most domains) would need to evaluate its plans in the real world to acquire training data. But my point is that we have the choice to not carry out some of the agent’s plans in the real-world. For some of the AI’s plans, we can say no—we have a veto button. It seems to me that the AI would be completely fine with that—is that correct? If so, it makes safety a much more tractable problem than it otherwise would be.
The problem is that at the beginning, its plans are generally going to be complete nonsense. It has to have a ton of interaction with (at least a reasonable model of) its environment, both with its reward signal and with its causal structure, before it approaches a sensible output.
There is no utility for the RL agent’s operators to have an oracle AI with no practical experience. The power of RL is that a simple feedback signal can teach it everything it needs to know to act rationally in its environment. But if you want it to make rational plans for the real world without actually letting it get direct feedback from the real world, you need to add on vast layers of additional computational complexity to its training manually, which would more or less be taken care of automatically for an RL agent interacting with the real world. The incentives aren’t in your favor here.
Thanks, I appreciate the explanation!