Eh—disagree. Unlike the majority of our sciences, ML tech disperses extremely quickly. Software being software. Patents aside, there's nothing that machine lea

Eh—disagree. Unlike the majority of our sciences, ML tech disperses extremely quickly. Software being software. Patents aside, there's nothing that machine learning research is going to give as an advantage nationally other than a couple years of warning that something is coming. On the other hand, if more software devs understood the basics of statistics and ML they'd be able to apply the tech into specialized domains. The same thing is happening with ML that happened with web dev. Abstracting over the annoying stuff allows developers to attack the problem at the conceptual level. But you can't think conceptually without understanding thing from first principles. For example, I expect developers to understand the definition of accuracy and why maximizing it usually isn't useful.