The Mathematical Hacker

> [2] Scala and Clojure being proof that it is still possible (though only to a lesser extent - tail call elimination and corecursion being an example where it fails completely).

In clojure, loop..recur and trampoline does the job. Tail recursion is not automatic, but still, it's there and quite straight forward to use with the added benefit of recur cribbing if your call isn't a tail call.

>There aren't enough humans that can do math

On what basis do you make that assertion? "Lots of people program who didn't bother to learn maths" doesn't mean they aren't able to learn maths.

>This stuff we call code ain't that hard.

Then why do we so consistently fuck it up horribly?

The first real library I had to learn to use was GSL. The first framework I ever learned to use was ROOT. I also had to learn how to use FFTW and all sorts of other math libraries. I've had to hack fortran a bit, but mostly just know how to read it and compile it with g77 and stuff because that's what my professors would program in. In my only real course in programming at a university I learned how to link C to fortran libraries. I used Maple way more than matlab, and all of this was the majority of my programming training before I graduated from college.

If there is a divide between fortran and LISP hackers, I'd have to say that Python is going to be the adopted parent/mentor, and it's the only programming language truly situated to bring them together. This is why projects like PyPy get me excited. Other projects like Julia are really interesting as well.

I have to agree with the premise of the article though. There is a divide between mathematical hackers and non-mathematical hackers. I don't think it's anti-intellectualism per se, it's possibly more of a result of the increasing stratification of backgrounds in an industry which has increasingly focused on products, services, and most importantly consumers and "social".

I went to a talk yesterday by an veteran applied mathematician who does research at HP labs. He mentioned something that really resonated with this notion: The days of academic style research, of Bell Labs and the like, has disappeared. This isn't to say that research and math has disappeared from the industries, but as a researcher you have to justify the research by explaining how it can contribute to a bottom line for the company. There's a few companies with looser restrictions on this (Microsoft, Google), but typically this kind of research is best done at companies that are monopolies with high profit margins. In that case, a company like Apple would be best situated to conduct research on the level of the Bell Labs of the past, but it's evident that they don't. The mathematical hacker started falling out of vogue sometime in the 70s. If the mathematical hacker ever becomes vogue again, it won't be because of Apple. It will be because of big data, autonomous cars, medicine and bioinformatics.

I found the thesis of this essay to be a foregone conclusion.

We know that math is a universal language that, as far as we know, is capable of describing every phenomenon in the universe. And while Computer Science (if you can even call it a science) has had a storied history with mathematics it's not unusual to me to see maths used as a tool rather than the foundation of study. It's the same in many other fields such as physics, engineering, chemistry and so on -- mathematics is a useful tool and nothing more. In Computer Science mathematics is a useful language for discovering and discussing the properties and consequences of algorithms and their computation.

It stands to reason then that if one increases ones knowledge of mathematics then one will have a larger vocabulary to work with in their area of study. If you're studying physics then the more math you know the more complex phenomenon you will be able to model and discuss. In Computer Science you will be able to find efficient applications and optimizations of various algorithms and model physical and ephemeral phenomenon with more rigour and precision.

However there is a maximum lower bound on the amount of mathematical knowledge required in order to effectively practice programming. That is where I think the meme, "you don't need to know math," comes from in our field. More often than not the primary concern of a software developer is to balance two primary concerns: "does it work as it is expected to?" and "can another human being understand and maintain this?" Having a broader understanding of mathematics will certainly open your horizons but there's only so much you need to know to get by. Most of your "work-a-day" programming won't involve much math at all and is primarily concerned with APIs and implementation issues.

I personally believe that Computer Science certainly needs more rigour and mathematics should be stressed a little harder in our curriculum. However in a "hacker" culture rigour is the least of your concerns. The hacker is a pragmatic creature and heavily leans towards asking the question, "Does it work?" more often than, "is this the right way to do it?" See the "New Jersey" school of development (or "Worse is Better" philosophy). As a field I think we'd benefit from a stronger approach to rigour. We are currently mired in a populist culture that constantly re-invents the wheel every decade or so. It'd be nice if we had a common literature and history from which to draw upon as part of our standard curriculum.

Interesting article... but I don't think the treatment of lispers is quite fair. :p

Yes, and a "tradeoff" and a "design flaw" are just two sides of the same coin!