Yes, after reflection I think this is correct. I think I had in mind a situation where with deployment, the training of the AI system simply stops, but of course, this need not be the case. So if training continues, then one either needs to argue stronger reasons why the distribution shift leads to a catastrophe (e.g., along the lines you argue) or make the case that the training signal couldn’t keep up with the fast pace of the development. The latter would be an outer alignment failure, which I tried to avoid talking about in the text.
Yes, after reflection I think this is correct. I think I had in mind a situation where with deployment, the training of the AI system simply stops, but of course, this need not be the case. So if training continues, then one either needs to argue stronger reasons why the distribution shift leads to a catastrophe (e.g., along the lines you argue) or make the case that the training signal couldn’t keep up with the fast pace of the development. The latter would be an outer alignment failure, which I tried to avoid talking about in the text.