>>12467993t. Normie who has no training or understanding of any stem subject whatsoever
There are literally new practical problems that are solved on a regular basis. Most of the math that is used in genetic engineering, population genetics, bioinformatics, dynamical models, modeling of signalling and communication, computational epidemiology, network analysis, systems biology, systems chemistry, etc. is less than 25 years old, and has only emerged after the development of the theory of, e.g. small world networks and boolean networks by people like Steven Strogatz and Stuart Kauffman.
The same could be said for machine learning, natural language processing, and other data driven areas of information science that require a lot of probabalistic and computational machinery.
