Regarding 1: I don’t think it would be good to simulate superintelligences with our predictive models. Rather, we want to simulate humans to elicit safe capabilities. We talk more about competitiveness of the approach in Section III.
Regarding 3: I agree it might have been good to discuss cyborgism specifically. I think cyborgism is to some degree compatible with careful conditioning. One possible issue when interacting with the model arises when the model is trained on / prompted with its own outputs, or data that has been influenced by its outputs. We write about this in the context of imitative amplification and above when considering factorization:
There are at least two major issues: it increases the probability that the model will predict AIs rather than humans, and it specifically increases the probability the model will predict itself, leading to multiple fixed points and the possibility of self-fulfilling prophecies.
I personally think there might be ways to make such approaches work and get around the issues, e.g., by making sure that the model is myopic and that there is a unique fixed point. But we would lose some of the safety properties of just doing conditioning.
Regarding 2: I agree that it would be good if we can avoid fooling ourselves. One hope would be that in a sufficiently capable model, conditioning would help with generating work that isn’t worse than that produced by real humans.
Thanks for your comment!
Regarding 1: I don’t think it would be good to simulate superintelligences with our predictive models. Rather, we want to simulate humans to elicit safe capabilities. We talk more about competitiveness of the approach in Section III.
Regarding 3: I agree it might have been good to discuss cyborgism specifically. I think cyborgism is to some degree compatible with careful conditioning. One possible issue when interacting with the model arises when the model is trained on / prompted with its own outputs, or data that has been influenced by its outputs. We write about this in the context of imitative amplification and above when considering factorization:
I personally think there might be ways to make such approaches work and get around the issues, e.g., by making sure that the model is myopic and that there is a unique fixed point. But we would lose some of the safety properties of just doing conditioning.
Regarding 2: I agree that it would be good if we can avoid fooling ourselves. One hope would be that in a sufficiently capable model, conditioning would help with generating work that isn’t worse than that produced by real humans.