In one way it's more of a more systematic view of architectures than a simple renaming.with a framework with a mathematical theory behind it investigation into architectures and also it'relationship with data.simplest case is two morphisms in the supervised case m,d:x->y where m is the model and d is the 'hidden' relationship between input and desired output.

In another view,it could be seen as a look into NNs themselves,size the well known (in the CT crowd) adage that colimits are a way to put many things in various relationships with each other into a bigger thing is basically what perceptron does only with concrete numbers and objective functions instead of existence and uniqueness.I see a melding of fuzzy PAC learning and GOFAI structuralism as a way forward in AI,and this is a haphazard proposal of a way to do it.I choose categories because they seem way more flexible than any logic (which by the curry-howard-lambek correspondence you can model in a category with sufficient structure).I mean,you can model categories in categories and it's used in foundations of mathematics and it's kind of intuitive if your mind is the right type of broken :)