If you want to put it that way, nothing conceptually interesting is going on in any neural network paper - we already know they're universal.

My problem is the details: I visualize neural networks as like pachinko machines or Galton's quincunx - you drop a bunch of bits (many balls) into the top (bottom) layer of neurons (pins) and they cascade down to the bottom based on the activation functions (spacing of pins & how many balls hit a pin simultaneously), and at the bottom (top) is emitted a final smaller output (1 ball somewhere). I don't get the details of what it means to add a memory to this many-to-one function.