That’s a fair point particularly on the classes of answers that it solves. I’m not so much interested in the “how” because we’ll always find a way of there is a

That’s a fair point particularly on the classes of answers that it solves. I’m not so much interested in the “how” because we’ll always find a way of there is a “why”. There are many applications of computational geometry today (which is not in data science but more engineering). If we can we can work with topological objects I can see we might be able to find areas where we can derive value. NN is a little different because it has always had a very strong why (it’s a very flexible, highly parameterized nonlinear regression model — a fitting function for everything) but was hampered by the how for many years. Topology’s whys are a bit less universal but I can see some very useful future applications.