I've experimented a little bit with neural networks generating text and images. A lot of my (admittedly not that much) knowledge comes from this article, The Unreasonable Effectiveness of Neural Networks.

During the "training" process (what I called evolution), the NNs start at a "blank slate" with no neurons connecting to any others any stronger than any others. Over time the computer tries millions of random mutations and the ones that get closer to the training data "win" and get to be included in the pool for the next generation. It's a lot like evolution, which is why I used that analogy.

It tests the training data by running the entire neural network in its current (unfinished) state and seeing how close it gets to the output when fed in the input. Then it quantifies the difference, essentially giving that iteration of the network a score (called the neural network's "fitness"). The lowest ones are, obviously, discarded.

Most of the things I've talked about have some sort of numerical cutoff, and it's pretty hard to get all the numbers close enough to avoid weird machine learning problems like overfitting. This is part of why I've only dabbled in machine learning; training can take days at a time unless you have a good GPU (yes, it uses a GPU; the same things that make them great at rendering discrete pixels make them great at simulating discrete neurons).

It gets a little more complicated when doing many-to-one (merging datasets) NNs; the one above probably uses a LSTM, or Long Short-Term Memory, neural network. I admittedly don't know as much about these; I'm just extrapolating on the sampling thing from what I know, though I bet with five days and access to the source I could make it happen