The primary thing I am arguing, as Max as already said, is that the AI alignment paradigm obscures the most fundamental problem of AI safety: That of prediction. It does this by conflating various interpretations of what values or utility functions are.
One of the most fundamental insights entailed by a move from an alignment to a predictive paradigm is that is becomes far from clear whether the relevant problems are solvable.
Nothing in this shows that AGI is “guaranteed to be destructive to human preferences” of course. Rather, it shows that various paradigms that one may choose to try to make AI safe, like RLHF, should actually not make us more confident in our AGI systems at all because they address the wrong questions: We can never know in advance whether they will work and this is true for all paradigms that try to sidestep the prediction problem by appealing to values (bracketing hard-wired values of course).
I have problems getting the first point. If bugs are hard to find then shouldn’t this precisely entail that dangerous AI is hard to differentiate from benign AI?! Any literature you can suggest on the subject?
Regarding the second point. I don’t find Eliezer’s idea entirely convincing. But I don’t think the fire thesis hinges on his view. Rather, it is built on the much weaker and simpler view that if we don’t know the utility function of some AGI system then this system is dangerous—I find it very hard to see any convincing reasons for thinking this is false. Eliezer thinks doom is default. I just assume that ignorance makes it rational to air on the side of caution.