> we avoid having to make as many obviously not universally applicable assumptions

The point of the above koan is that there are always assumptions (priors) whether you like them or not. You can make them explicit or random and unknown. This is the crux of the bias-variance tradeoff [1]. Whether deep NNs manage to circumvent that in any meaningful way (especially outside the domain of spatial/temporal analog signals) is an open question.

[1]: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

Exactly, there is simply no way for a human to understand this stuff without (some layers of) abstraction, each abstraction introduces bias and/or error.