I'm trying think through various approaches to AI alignment, and so far this is the one I came up with that I like best. I have not read much of the literature, so please do point me if this has been discussed before.
What if we train an AI agent (ie, reinforcement learning) to survive/thrive in an environment where there are a wide variety of agents with wildly different levels of intelligence? In particular, such that pretty much every agent can safely assume they'll eventually meet an agent much smarter than they are; structure the environment to reward tit-for-tat with a significant bias towards cooperation, eg require agents to "eat" resources that require cooperation to secure and are primarily non-competitive. The idea is to have them learn to respect even beings of lesser intelligence, because they want beings of higher intelligence to respect them; and because in this environment a bunch of lesser intelligences can gang up and defeat one higher-intelligence being. Also, we effectively train each AI to detect and defeat new AIs that seek to disturb this balance. I have not thought this through, curious what you all think
Since there are no humans in the training environment, how do you teach that? Or do you put human-substitutes there (or maybe some RLHF-type thing)? Also, how would such AIs will even reason about humans, since they can't read our thoughts? How are they supposed to know if we would like to "vote them out" or not? I do agree though that a swarm of cooperative AIs with different goals could be "safer" (if done right) than a single goal-directed agent.
This setup seems to get more and more complicated though. How are agents supposed to analyze "minds" of each other? I don't think modern neural nets can do that yet. And if we come up with a way that allows us to reliably analyze what an AI is thinking, why use this complicated scenario and not just train (RL or something) it directly to "do good things while thinking good thoughts", if we're relying on our ability to distinguish "good" and "bad" thoughts anyway?
(On an unrelated note, there already was a rather complicated paper (explained a bit simpler here, though not by much) showing that if agents reasoning in formal modal logic are able to read each other's source code and prove things about it, then at least in the case of a simple binary prisoner's dilemma you can make reasonable-looking agents that also don't do stupid things. Reading source code and proving theorems about it is a lot more extreme than analyzing thought logs, but at least that's something)
I'm trying think through various approaches to AI alignment, and so far this is the one I came up with that I like best. I have not read much of the literature, so please do point me if this has been discussed before.
What if we train an AI agent (ie, reinforcement learning) to survive/thrive in an environment where there are a wide variety of agents with wildly different levels of intelligence? In particular, such that pretty much every agent can safely assume they'll eventually meet an agent much smarter than they are; structure the environment to reward tit-for-tat with a significant bias towards cooperation, eg require agents to "eat" resources that require cooperation to secure and are primarily non-competitive. The idea is to have them learn to respect even beings of lesser intelligence, because they want beings of higher intelligence to respect them; and because in this environment a bunch of lesser intelligences can gang up and defeat one higher-intelligence being. Also, we effectively train each AI to detect and defeat new AIs that seek to disturb this balance. I have not thought this through, curious what you all think
(Cross posted from EA Forum)