Labs really won’t volunteer the data that would justify constraining them, and they are even reticent to create data that could later be used against them.
Yeah, for some reason this wasn’t part of my model which shows my experience in this area I guess. That’s a good point.
I agree with the final point as well, I guess there’s some sort of directionality and overclaiming in terms of the exact risks that I worry about? Like, if we say AI 2027 is our danger scenario and AI 2027 turns out not to be true then you lose credibility but I assume this is not how you go about building up these things.
I guess my thought would be that some of the evals advocacy might be negative EV if it isn’t pointed at things that is likely to scale to a continual learning regime? Now that doesn’t mean that the point of focusing more on advocacy is a bad one, it just is more like a prioritisation question?
Yeah, for some reason this wasn’t part of my model which shows my experience in this area I guess. That’s a good point.
I agree with the final point as well, I guess there’s some sort of directionality and overclaiming in terms of the exact risks that I worry about? Like, if we say AI 2027 is our danger scenario and AI 2027 turns out not to be true then you lose credibility but I assume this is not how you go about building up these things.
I guess my thought would be that some of the evals advocacy might be negative EV if it isn’t pointed at things that is likely to scale to a continual learning regime? Now that doesn’t mean that the point of focusing more on advocacy is a bad one, it just is more like a prioritisation question?
Thanks for answering my question!