(Written quickly and not very carefully.)

I think it's worth stating publicly that I have a significant disagreement with a number of recent presentations of AI risk, in particular Ajeya's "Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover", and Cohen et al.'s "Advanced artificial agents intervene in the provision of reward". They focus on policies learning the goal of getting high reward. But I have two problems with this:

1. I expect "reward" to be a hard goal to learn, because it's a pretty abstract concept and not closely related to the direct observations that policies are going to receive. If you keep training policies, maybe they'd converge to it eventually, but my guess is that this would take long enough that we'd already have superhuman AIs which would either have killed us or solved alignment for us (or at least started using gradient hacking strategies which undermine the "convergence" argument). Analogously, humans don't care very much at all about the specific connections between our reward centers and the rest of our brains - insofar as we do want to influence them it's because we care about much more directly-observable phenomena like pain and pleasure.
2. Even once you learn a goal like that, it's far from clear that it'd generalize in ways which lead to power-seeking. "Reward" is not a very natural concept, it doesn't apply outside training, and even within training it's dependent on the specific training algorithm you use. Trying to imagine what a generalized goal of "reward" would cash out to gets pretty weird. As one example: it means that every time you deploy the policy without the intention of rewarding it, then its key priority would be convincing you to inserting that trajectory into the training data. (It might be instructive to think about what the rewards would need to be for that not to happen. Below 0? But the 0 point is arbitrary...) That seems pretty noticeable! But wouldn't it be deceptive? Well, only within the scope of its current episode, because trying to get higher reward in other episodes is never positively reinforced. Wouldn't it learn the high-level concept of "reward" in general, in a way that's abstracted from any specific episode? That feels analogous to a human learning to care about "genetic fitness" but not distinguishing between their own genetic fitness and the genetic fitness of other species. And remember point 1: the question is not whether the policy learns it eventually, but rather whether it learns it before it learns all the other things that make our current approaches to alignment obsolete.

At a high level, this comment is related to Alex Turner's Reward is not the optimization target. I think he's making an important underlying point there, but I'm also not going as far as he is. He says "I don't see a strong reason to focus on the “reward optimizer” hypothesis." I think there's a pretty good reason to focus on it - namely that we're reinforcing policies for getting high reward. I just think that other people have focused on it too much, and not carefully enough - e.g. the "without specific countermeasures" claim that Ajeya makes seems too strong, if the effects she's talking about might only arise significantly above human level. Overall I'm concerned that reasoning about "the goal of getting high reward" is too anthropomorphic and is a bad way to present the argument to ML researchers in particular.