Acquisitions accelerate as tech giants seek to build AI smarts
Uh, guys with the big bucks:  AL&#x2F;ML are at most a tiny fraction of the material, tools, power, and value of the pure&#x2F;applied math on the shelves of the research libraries.<p>If you have a problem you want solved with AI&#x2F;ML, 99 44&#x2F;100% of the time you are better off going for what&#x27;s on the shelves of the libraries, as taught in various high end grad schools, than with anything currently specifically AL&#x2F;ML.<p>So, broadly, go for work in statistics, optimization, stochastic processes, and optimal control.  For a specific application, may want some work that stands on that existing material and is also at least somewhat original.<p>E.g., the crucial technical core of my startup is some original applied math I derived based on pure&#x2F;applied math that&#x27;s long been on the shelves of the research libraries.  For the valuable work of my startup, what&#x27;s in AI&#x2F;ML now is in comparison at best weak, nearly silly early grade school baby talk.<p>Really, guys, 99 44&#x2F;100% of the good stuff is still where it&#x27;s long been -- on the shelves of the best research libraries.  And for the education for that work, it&#x27;s definitely NOT in departments of computer science.  Instead look at selected programs in pure&#x2F;applied math in some of the best research universities.