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gwern comments on [Link]"Neural Turing Machines" - Less Wrong Discussion
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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.
Recurrent Neural Networks feed their outputs back into their inputs, and run continuously. Instead of visualizing a river flowing directly downstream, visualize a river with a little cove off to one side, where vortices swirl around.
The same way you add memory to many-to-one boolean functions: feedback loops.
I think another way to look at neural networks is they are nested non-linear regression models.
I am probably in the minority here, but I don't think the stuff in the OP is that interesting.