History shows that, in total, research, at least the best of it, is powerful stuff, powerful enough to change civilizations, perhaps the most powerful stuff. T

History shows that, in total, research, at least the best of it, is powerful stuff, powerful enough to change civilizations, perhaps the most powerful stuff. The OP shows some of the history of some research and, thus, is good for better understanding of how research works. Can observe that for all or nearly all the topics in the OP, the best present treatments are much better sources than the historical documents. So, (a) even for those now famous research documents, the time to the present brought a lot of improvements, often even in the foundations, and advancements. (b) Curiously, for someone wishing to study and learn for applications or additional research, bluntly, it's nearly always best just to ignore the historical documents. Another point: It seems clear that now integrated digital circuits with, say, feature sizes under 10 nanometers, communications via solid state lasers lighting long haul optical fibers, operating systems and other general purpose software, means of developing new software, and the Internet represent good opportunities, likely new versions of copper, bronze, iron, concrete, open ocean sailing, steam, oil, electricity, electronics, etc. Then, bluntly, to take such opportunities mostly means (a) find a need and (b) use these new tools to meet the need, but in that work the foundations of computer science are only occasionally directly relevant. In simple terms, we can all learn to develop the software: Get a laptop computer, download a lot of software, learn some programming languages and some software libraries, and start meeting the needs. Of the topics of those historical documents, the ones I encountered in some detail include discrete optimization, information theory, RSA public key cryptosystems, algebraic coding theory, quite a lot of numerical analysis (e.g., Gauss elimination and alternatives), a lot in probability (and a lot in statistics), and a lot of the usual algorithms from heap sort, discrete dynamic programming, etc. Also covered a lot of topics not on that list, in total more topics than on that list. Lesson to others: I spent too much time on those topics, went much deeper than really useful for developing solutions to real problems. Net lesson: Get on with something current and useful to meet a need and (a) be careful not to spend too much time on those foundational topics and (b) in simple terms, for the historical documents, just f'get about them.