There's a lot of multiplication of numbers in parallel, so it makes sense to try to fit that to matrices.
Cryptography is built bottom-up, but likewise it makes sense to exploit data structures that already exist in silicon.
That sounds interesting. Where have you heard about that? Or is this your own research?
This is exactly the point. I was disappointed that I had to scroll so far down the page until I saw the word "entropy." There is a deep connection between machine learning and encryption and compression in information theory. As Shannon demonstrated, the one-time pad's encrypted output is maximum entropy, and so would data compressed to the Shannon limit. Such an optimal compressor learns the underlying probability distribution of the data to represent it with the fewest bits possible, which is exactly the goal of machine learning. A trained ML model can be seen as a lossy compression of the training data. Autoencoding models make the link between ML and compression (and thus encryption) explicit.