No, no, you're looking at this all wrong. Apple stayed out of the Phablet market until they were "cool/hip/trendy". The vast sales Samsung had were merely to unimportant people. Apple, on the other hand, entered the market exactly when phablets became cool, because, by definition, phablets became cool only once Apple had entered the market.

Fuzzy logic is logic. So are linear logic, intuitionistic logic, temporal logic, modal logic, and categorical logic. Just because you only learned Boolean logic doesn't mean there aren't well developed consistent logics beyond that. In practice bivalent logics are the exceptions.

Yes; "if you're just willing to get your hands a little dirty and muck in and learn then you can bend the hugely complicated interface to your needs" they'll say; they'll complain that your just not willing to learn things, and thus it is your fault. Such people will inevitably state that they are "power users" who need ultimate configurability and are (unlike you) willing to learn what they need to to get that.

They will inevitably deride GNOME3 for it's complete lack of configurability. Of course they'll gloss over the fact that GNOME3 actually exposes pretty much everything via a javascript interface and makes adding/changing/extending functionality via javascript extensions trivial (GNOME3 even has a javascript console to let you do such things interactively). Apparently actually learning an API and coding completely custom interfacdes from myriad building blocks is "too much work". They are "power users" who require a pointy-clicky interface to actually configure anything. Even dconf is "too complicated".

For those of us who learned to "customize our desktop" back in the days of FVWM via scriptable config files calling perl scripts etc. it seems clear that "power users" are really just posers who want to play at being "super-customised". Almost all the modern DEs do have complete customisation available and accessible; some of them just use a richer (scripting) interface to get such things done.

It's not the features that you stare at with no idea what they do that cause a problem. As you say, a quick look at the manual can help to sort that out (though it does add to the overall cognitive load). It's all the potentially subtle things that you don't even realise are features and so never look up and don't realise that, contrary to first inspection, the code is actually doing something subtly different to what you expect.

Except math is almost never actually done that way in practice. Euclid was wonderful, but almost all modern math does not work that strictly (and Euclid really should have been more careful with the parallel postulate -- there's "more than one thing at a time" involved there). Yes, proofs are careful and detailed, but so is, say, technical writing in English. Except for a few cases (check out metamath.org, or Homotopy Type Theory) almost no-one actually pedantically lays out all the formal steps introducing "only one concept at a time".

I don't think you need math even to be a great programmer. I do think a lot of great programmers are people who think in mathematical terms and thus benefit from mathematics. But I also believe you can be a great programmer and not be the sort of person who thinks in those terms. I expect the latter is harder, but then I'm a mathematician so I'm more than read to accept that I have some bias in this topic.

Math is a language. Just because you can frame things in that language doesn't mean that that language is necessary. Recipes are often in English. English is sequencing (words are a serial stream after all). That doesn't mean English is necessary for programming (there seem to many competent non-english speaking programmers as far as I can tell).

Disclaimer: I am a professional research mathematician; I do understand math just fine.

Of course there was a time when college education was supposed to be education and not just vocational training.

The other part of the problem is that math is so diverse. There's calculus and engineering math with all kinds of techniques for solving this or that PDE; there's set theoretic foundations; there's graph theory and design theory and combinatorics and a slew of other discrete math topics; there's topology and metric spaces and various abstractions for continuity; there's linear algebra and all the finer points of matrices and matrix decompositions and tensors and on into Hilbert spaces and other infinite dimensional things; there's category theory and stacks and topos theory and other esoterica of abstraction. On and on, and all very different and I can't even pretend to have anything but cursory knowledge of most of them ... and I have a Ph.D. in math and work for a research institute trying to stay abreast of a decent range of topics. The people who actually study these topics in depth are all called "mathematicians", but if you're an algebraic geometer then sure, you're probably familiar with category theory and homological algebra; if you do design theory and graph theory then those seem like the most useful subject available.

Calculus is perhaps not the best measure however. Depending on where you go in the programming field calculus is likely less useful than some decent depth of knowledge in graph theory, abstract algebra, category theory, or combinatorics and optimization. I imagine a number of people would chime in with statistics, but to do statistics right you need calculus (which is an example of one of the directions where calculus can be useful for programming).

Of course the reality is that you don't need any of those subjects. Those subjects can, however, be very useful to you as a programmer. So yes you can certainly be a programmer, and even a very successful and productive one without any knowledge of calculus, or graph theory say. On the other hand, there may well be times when graph theory, or calculus, or statistics could prove very useful. what it comes down to is whether you are inclined to think that way -- and if so it can be a benefit; if not it won't be the way you think about the problem anyway.

You may want to look into pandas as a middle ground. It's great for sucking in tabular or csv data and then applying statistical analysis tools to it. It has a native "dataframe" object which is similar to database tables, and has efficient merge, join, and groupby semantics. If you have a ton of data then a database and SQL is the right answer, but for a decent range of use cases in between pandas is extremely powerful and effective.

Python has the default implementation CPython which compiles python to an interpreted bytecode; there's also Jython which compiles to JVM, and IronPython which compiles Microsoft's CLR. There's also Cython (which requires extra annotations) which compiles to C and thence to machine code, and numba which does compilation to LLVM. Finally there's Pypy which is a python JIT compiler/interpreter written in a restricted subset of Python.

They are memes in the sense that they are specifically finding words and phrases that are frequently inherited by papers (where "descendant" is determined by citation links), and rarely appear spontaneously (i.e. without appearing in any of the papers cites by a paper). An important feature is that their method used zero linguistic information, didn't bother with pruning out stopwords, or indeed, do any preprocessing other than simple tokenisation by whitespace and punctuation. Managing to come out with nouns and complex phrases under such conditions is actually very impressive. You should try actually reading the paper.

Actually no, they are not. By using citations to create a directed graph of papers they are specifically looking for words or phrases that are highly likely to be inherited by descendant documents and also much less frequently spontaneously appear in documents (i.e. not used in any of the cited documents). They really are interested in the heritability of words and phrases.