I don’t follow. You seem to be responding to a statement that “I think the ML communities perceptions are important, because the ML community’s attitude seems of critical importance for getting good Xrisk reduction policies in place”, which I see as having little bearing on the question the post raises of “how should we assess info-hazard type risks of AI research we conduct?”
Based on my reading of the post it seemed to me that you were concerned primarily with info-hazard risks in ML research, not AI research in general; maybe it’s the way you framed it that I took it to be contingent on ML mattering.
I don’t follow. You seem to be responding to a statement that “I think the ML communities perceptions are important, because the ML community’s attitude seems of critical importance for getting good Xrisk reduction policies in place”, which I see as having little bearing on the question the post raises of “how should we assess info-hazard type risks of AI research we conduct?”
Based on my reading of the post it seemed to me that you were concerned primarily with info-hazard risks in ML research, not AI research in general; maybe it’s the way you framed it that I took it to be contingent on ML mattering.
I meant it to be about all AI research. I don’t usually make too much effort to distinguish ML and AI, TBH.