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TL;DR I try to distinguish between what I call ‘anthropomorphic’ vs ‘biological’ reasoning and look at different ways in which they are used.
Note: This post is a small part of a larger set of ideas that makes more specific/falsifiable predictions which I hope to soon compile into a bigger post. I don't claim the ideas here are super original, but I haven't seen this distinction discussed much at depth re: current model behavior.
A large debate when it comes to talking about AI behavior seems to be that of using or not using ‘anthropomorphic’ ideas to reason about models. Roughly speaking, the argument for seems to be some combination of ‘models are trained on human data’ and ‘models act human in a lot of nontrivial ways,’ and the argument against is more ‘on principle, assuming human priors is a strong assumption’ and ‘models also have a lot of more alien behavioral qualities.’ However, I think that this idea of ‘anthropomorphic vs not’ is a false dichotomy, and, as I’ll argue in this post, we should be thinking about two separate sources of human behavioral analogies.
Specifically, I’d like to distinguish between ‘anthropomorphic’ reasoning and what I will call here ‘biological’ reasoning. Biological reasoning means here that instead of using human analogies due to pretraining data, we use human analogies because humans and models are both intelligent creatures trained with some notion of reward or evolution, which can provide them with similar ‘basic objects.’ Things coming from biological reasoning should also apply to organisms at other levels, like animals or cells. In addition, most behavioral qualities that we use biological reasoning to think about are best thought of as coming from RL (although I don’t think RL is fully necessary to install them, but this is a topic for another post). In contrast, anthropomorphic reasoning is the more typical definition, coming from the fact that models are trained on human priors, and thus using human-level objects to rea