Concretely, my best guess is that you need inner alignment, since failure of inner alignment probably produces random goals, which means that multiple inner-misaligned AIs are unlikely to share goals.
I disagree with this. I don’t expect a failure of inner alignment to produce random goals, but rather systematically produce goals which are simpler/faster proxies of what we actually want. That is to say, while I expect the goals to look random to us, I don’t actually expect them to differ that much between training runs, since it’s more about your training process’s inductive biases than inherent randomness in the training process in my opinion.
I disagree with this. I don’t expect a failure of inner alignment to produce random goals, but rather systematically produce goals which are simpler/faster proxies of what we actually want. That is to say, while I expect the goals to look random to us, I don’t actually expect them to differ that much between training runs, since it’s more about your training process’s inductive biases than inherent randomness in the training process in my opinion.