It dawned on me that I think I’ve exhausted the topics of classification and prediction in A.I., and here’s why:

Either a dataset is locally consistent, in which case, my core algorithms will solve it, in polynomial time;

If it’s not locally consistent, then you use interpolation among the features, perhaps of a very high degree, but it doesn’t matter, because the bottom line is, even a neural network is a function that maps an input vector to a classification –

So, you can approximate that neural network function using a polynomial, since it’s a function from . Because you can use vectorized Monte Carlo solutions to find the appropriate polynomial, the problem is honestly, not interesting.

So, I’m turning entirely to my work in physics and A.I., which has applications to image processing, and if I have time, I’ll also do some work on NLP, because I have tons of unpublished research on the topic.