Putting aside the weird preface about observation and mathematics, which misses the point that science builds a theory, which is a formal system that recapitula

Putting aside the weird preface about observation and mathematics, which misses the point that science builds a theory, which is a formal system that recapitulates the relations among observations, the neural network stuff shows very deep lack of understanding. For example: > an autoencoder (AE) is basically one of the easiest ones me to explain, implement and understand Then a list describing it. But that list does not constitute an explanation unless you have a pretty good intuitive understanding of linear algebra and function composition. > So essentially, neural networks become a sort of mathematical abstraction that isn’t very leaky. I think what the author really means by "leaky" is "not opaque." Mathematical structures that you can analyze in a well defined way (not opaque/"leaky") like integrals vs mathematical structures that the tools to dig into in a deep way don't exist yet (opaque/"not leaky"). Except that such understanding of neural nets is a major current research program. So to all the youngsters who might be looking at this, this article is the ravings of a crank who doesn't know what he's talking about.