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 (the prompt is “identity” of the agent instance, distinguishes the model as a simulator from agent instances as simulacra). Decision transformers with more free-form task/identity prompts illustrate this point, except a bureaucracy should have multiple agents with different task prompts in a single episode. MCTS and GAN are adversarial and could be reframed like this. One of the tentative premises of ELK is that a model trained for some purpose might additionally allow instantiating an agent that reports what the model knows, even if that activity is not clearly related to the model’s main purpose. Colluding instances inside a bureaucracy make it less effective in achieving its goals of producing a better dataset (accurately evaluating outcomes of episodes).
So I think useful arrangements of diverse/competing/flawed systems is a hope in many contexts. It often doesn’t work, so looks neglected, but not for want of trying. The concern with collusion in AI risk seems to be more about deceptive alignment, observed behavior of a system becoming so well-optimized that it ceases to be informative about how it would behave in different situations. Very capable AIs can lack any tells even from the perspective of extremely capable observers, their behavior can change in a completely unexpected way with changing circumstances. Hence the focus on interpretability, it’s not just useful for human operators, a diverse system of many AIs also needs it to notice misalignment or collusion in its parts. It might even be enough for corrigibility.
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.)
So I think useful arrangements of diverse/competing/flawed systems is a hope in many contexts. It often doesn’t work, so looks neglected, but not for want of trying.
What does and doesn’t work will depend greatly on capabilities, and the problem-context here assumes potentially superintelligent-level AI.
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 (the prompt is “identity” of the agent instance, distinguishes the model as a simulator from agent instances as simulacra). Decision transformers with more free-form task/identity prompts illustrate this point, except a bureaucracy should have multiple agents with different task prompts in a single episode. MCTS and GAN are adversarial and could be reframed like this. One of the tentative premises of ELK is that a model trained for some purpose might additionally allow instantiating an agent that reports what the model knows, even if that activity is not clearly related to the model’s main purpose. Colluding instances inside a bureaucracy make it less effective in achieving its goals of producing a better dataset (accurately evaluating outcomes of episodes).
So I think useful arrangements of diverse/competing/flawed systems is a hope in many contexts. It often doesn’t work, so looks neglected, but not for want of trying. The concern with collusion in AI risk seems to be more about deceptive alignment, observed behavior of a system becoming so well-optimized that it ceases to be informative about how it would behave in different situations. Very capable AIs can lack any tells even from the perspective of extremely capable observers, their behavior can change in a completely unexpected way with changing circumstances. Hence the focus on interpretability, it’s not just useful for human operators, a diverse system of many AIs also needs it to notice misalignment or collusion in its parts. It might even be enough for corrigibility.
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.)
What does and doesn’t work will depend greatly on capabilities, and the problem-context here assumes potentially superintelligent-level AI.