Personally I think Prolog is great* and I'm learning it for funsies but neural nets are a specific approach that people have been writing in Python, because they already know Python. Prolog is a whole thing. Kind of an apples and oranges problem.
* I mean seriously it's fantastically expressive: parsing, constraint logic, expert systems, database interaction-- all look and feel completely native, it's beautiful.
Absolutely, I fully agree!
In fact, I think precisely due to this elementary difference, it is reasonable to expect it to take longer for Prolog implementations to reach the practical applicability we are now seeing in neural networks after a few decades of research and improvements.
We may see analogous developments with Prolog when our current hardware gets better or changes its characteristics, and when Prolog implementations get better and use better approaches that are now becoming available.
I think one difficulty we are facing when judging developments in this area is that complex software projects such as implementing a Prolog system happen on time scales we are not yet used to.