The OP has > Machine learning is the basis on which all large Internet companies are built, enabling them to rank responses to a search query, give suggestions

The OP has > Machine learning is the basis on which all large Internet companies are built, enabling them to rank responses to a search query, give suggestions and select the most relevant content for a given user. IMHO, that's claiming too much: My Internet search engine (running, in alpha test) essentially does > give suggestions and select the most relevant content for a given user but has nothing to do with anything like machine learning in computer science. Instead the core data manipulations are from some original derivations I did in applied math based on some advanced prerequisites in pure and applied math. Why do I have confidence in the power of the math? Theorems and proofs from some careful assumptions that hold likely plenty well enough in the real situation. More generally, my view is that for a specific problem to be solved with information technology, commonly by far the most powerful approach is via applied math, possibly original for that problem. An example is my work in anomaly detection as in, say, https://news.ycombinator.com/item?id=11880593 https://news.ycombinator.com/item?id=11880593 For a technique of great generality and economic power, there is integer linear programming: Where it works, which is often, it can be said to totally knock the socks off artificial intelligence or machine learning. Integer linear programming is serious stuff, e.g., was one of the main motivations for considering good algorithms and, then, the profound question of P versus NP. Gee, farther into the OP I see > We need to retrain truck drivers and office assistants to create data analysts, trip optimizers and other professionals we don’t yet know we need. > trip optimizers Really? And that's new? Not exactly! That topic has been a biggie in operations research, optimization, and integer programming for decades, really, back to the 1950s. Really, Dantzig, in the late 1940s, developed linear programming at Rand first to help with how best to deploy a military force a long way away quickly -- call it a case of trip optimization. The famous traveling salesman problem in optimization and integer programming and P versus NP? Could call it trip optimization. For FedEx, each night, which planes go where? At least early on, could call that trip optimization. Much of logistics is trip optimization, and an important part of that is handling uncertainty, and now we can be into stochastic dynamic programming. Now we are a long way from artificial intelligence or machine learning. Point: The world is awash in problems in manipulating information; how to do that is very old and in many fields of science, engineering, and operations of wide variety, often deep into mathematics, and long before computer science and machine learning. I would ask, was the work of James Simons as in the OP? My impression is no. IMHO, the OP is claiming too much.