The reason why [Computer Science] research produces so little that can be called creative programming these days is that the modern process of grant-funded research is fundamentally incompatible with the task of writing interesting, cool and relevant software. Rather, its goal is to produce publications and careers, and it’s very good at that.
This is quite a naive point of view. To paraphrase Dijkstra:
Computer science is no more about programming than astronomy is about telescopes.
Math is about sets and relations and functions and logic, ..., and, yes, numbers. But many branches of math aren't particularly or specifically concerned with numbers (e.g. linear algebra, group theory, topology), and even those which seem to be (e.g. number theory or real analysis) can be thought of as about sets with particular properties (the mathematician's ideas of the integers and the real numbers are* just ideas of sets with particular properties).
[* are == need only be. The mathematician is allowed to be a realist about numbers and think that there are the real numbers etc., and that these are what she is reasoning about.]
But in my view, math is about getting the right numbers, and you don't fully understand a problem until you can get the right numbers out. Here are two examples:
1) Theorem: det A != 0 implies Ax=b has a solution for a square matrix A.
If you actually try this, you'll discover all sorts of matrices A for which the textbook method fails. Thinking carefully about how to find x in the presence of rounding errors leads you to discover condition numbers, numerical range and all that other interesting stuff.
2) Theorem (circa 1870): Fourier series work. (In particular, they converge pointwise.)
Gibbs: I tried, it didn't work for discontinuous functions. I made some graphs, they are terrible. WTF!
Eventually, people paid attention to Gibbs and discovered Gibbs ringing. Trying to figure this out led us to learn about uniform convergence, Hilbert spaces and all that.
Of course, I'm a numerical analyst, so I might be a little biased.
parenthesis hit the nail on the head. If I was asked to give the most concise definition of mathematics I could, I would say it's the study of structure and relations.
This is a sensitive topic for me, since I'm in love with studying.
I have to disagree with a few points, namely that universities have developed into bureaucracies primarily seeking to further careers. I worked as an undergraduate researcher in a neuroscience lab, and although publication was always a pressure, I don't know anyone who wasn't there because they didn't want to be. Studying the same subject on such an exclusive basis for 5 years or more can cause burnout, but everyone in the lab came in excited at the possibilities of solving new problems.
However, universities can do more to ensure that their work helps the community directly - big research needs to continue unabated, or perhaps even on an accelerated schedule, but students and many professors can shift their focus from class-centric papers and projects to community-based projects.
Actually, this is the startup I'm working on right now, and it came about because students "do not want to churn out meaningless solutions to irrelevant problems", they want to do meaningful things.
It was never the University's role to innovate in the manner that the article says they do. But just look at google summer of code: All of these projects—really, really cool things—are university research projects. Besides, it was never the university's role to innovate in the manner that the article insinuates: i.e. tangible code. Most of the "innovation", or creative code, you see in today's world are simply the application of already existing theories.
the university stopped being about pure research and started focusing on applied research because of WW2. It was at this time that the military industrial complex sunk some hooks into the university system.
The reason why [Computer Science] research produces so little that can be called creative programming these days...
I'd like to see some examples of "meaningless solutions to irrelevant problems". CS research was never really about producing "creative programming", but rather about furthering what we know about computers, which involves a lot of math and theoretical research.
If I were to guess, he's talking about problems which don't affect his life or aren't "interesting"; That, however, is where the money lies. Just think about the founders of google: their research wasn't exactly thrilling, but it's led them to be one of, if not the, most successful tech companies of all time.
I have a problem with the assumption that "publish or perish" is killing the universities. The assumption is that one has to publish a lot of material. That is not true. Gauss had an excellent quote that applies here, "few but ripe." This point behind the quote is publish a little, but let what you publish be truly brilliant. Do you honestly think Harvard's math department would not want the person who manages to prove the Riemann hypothesis if that person also only had two other published papers? The answer is resounding, "hell no." Academia is about quality, not quantity.
> Do you honestly think Harvard's math department would not want the person who manages to prove the Riemann hypothesis if that person also only had two other published papers?
Doesn't "want" depend somewhat on what they expect him to do in the future? Suppose, for example, that he'd decided to devote the rest of his life to beating video games while blindfolded. Would they still want him?
You have suddenly taken my argument to the absurd. Under my assumption, this mathematician is likely to continue to pursue mathematics. Why would he have worked on the Riemann hypothesis if he had no true interest in math? In addition to that, he has shown that he is willing to tackle some of the greatest problems in mathematics. So, therefore, I assume that he will continue to publish and that the ideas published are likely to be very deep. Therefore, he is likely to continue publishing few papers, but very deep papers.
This is quite a naive point of view. To paraphrase Dijkstra:
Computer science is no more about programming than astronomy is about telescopes.
(s/programming/computers/ for the original)
Look at this list http://en.wikipedia.org/wiki/Computer_science#Fields_of_comp... and tell me what percentage of that consists of programming. It's like saying math is about numbers.