These are good points, and I agree with pretty much all of them.
Instances in a bureaucracy can be very different and play different roles or pursue different purposes. They might be defined by different prompts and behave as differently as text continuations of different prompts in GPT-3
I think that this is an important idea. Though simulators analogous to GPT-3, it may be possible to develop strong, almost-provably-non-agentic intelligent resources, then prompt them to simulate diverse, transient agents on the fly. From the perspective of building multicomponent architectures this seems like a strange and potentially powerful tool.
Regarding interpretability, tasks that require communication among distinct AI components will tend to expose information, and manipulating “shared backgrounds” between information sources and consumers could potentially be exploited to make that information more interpretable. (How one might train against steganography is an interesting question.)
These are good points, and I agree with pretty much all of them.
I think that this is an important idea. Though simulators analogous to GPT-3, it may be possible to develop strong, almost-provably-non-agentic intelligent resources, then prompt them to simulate diverse, transient agents on the fly. From the perspective of building multicomponent architectures this seems like a strange and potentially powerful tool.
Regarding interpretability, tasks that require communication among distinct AI components will tend to expose information, and manipulating “shared backgrounds” between information sources and consumers could potentially be exploited to make that information more interpretable. (How one might train against steganography is an interesting question.)