I only skimmed through this post. But I think this is a crux:

> The more we read [the neural network] the way its own readers do, the more interpretable I think it will look. We should be wary of reading it too much like a human, and concluding it’s messy just because it doesn’t fit our preconceived concepts.

The anthropomorphization of artificial neural networks has been a problem for as long as they've been called "neural" networks. Not that I can complain about it, though. I got my first job as a machine learning engineer coming from academic neuroscience by putting "biological neural networks" on my resume.