Why hasn’t functional programming taken over yet?

“I’ve read some texts about declarative/functional programming (languages), tried out Haskell as well as written one myself. From what I’ve seen, functional programming has several advantages over the classical imperative style:

  • Stateless programs; No side effects
  • Concurrency; Plays extremely nice with the rising multi-core technology
  • Programs are usually shorter and in some cases easier to read
  • Productivity goes up (example: Erlang)
  • Imperative programming is a very old paradigm (as far as I know) and possibly not suitable for the 21st century

Why are companies using or programs written in functional languages still so “rare”?

Why, when looking at the advantages of functional programming, are we still using imperative programming languages?

Maybe it was too early for it in 1990, but today?…”

http://stackoverflow.com/questions/2835801/why-hasnt-functional-programming-taken-over-yet

Practical machine learning tricks from the KDD 2011 best industry paper

“A machine learning research paper tends to present a newly proposed method or algorithm in relative isolation. Problem context, data preparation, and feature engineering are hopefully discussed to the extent required for reader understanding and scientific reproducibility, but are usually not the primary focus. Given the goals and constraints of the format, this can be seen as a reasonable trade-off: the authors opt to spend scarce “ink” on only the most essential (often abstract) ideas.

As a consequence, implementation details relevant to the use of the proposed technique in an actual production system are often not mentioned whatsoever. This aspect of machine learning is often left as “folk wisdom” to be picked up from colleagues, blog posts, discussion boards, snarky tweets, open-source libraries, or more often than not, first-hand experience.

Papers from conference “industry tracks” often deviate from this template, yielding valuable insights about what it takes to make machine learning effective in practice. This paper from Google on detecting “malicious” (ie, scam/spam) advertisements won best industry paper at KDD 2011 and is a particularly interesting example…”

http://blog.david-andrzejewski.com/machine-learning/practical-machine-learning-tricks-from-the-kdd-2011-best-industry-paper/

iSICP – The Elements of Programming

“A powerful programming language is more than just a means for instructing a computer to perform tasks. The language also serves as a framework within which we organize our ideas about processes. Thus, when we describe a language, we should pay particular attention to the means that the language provides for combining simple ideas to form more complex ideas. Every powerful language has three mechanisms for accomplishing this:

  • primitive expressions, which represent the simplest entities the language is concerned with, 
  • means of combination, by which compound elements are built from simpler ones, and 
  • means of abstraction, by which compound elements can be named and manipulated as units.

In programming, we deal with two kinds of elements: procedures and data. (Later we will discover that they are really not so distinct.) Informally, data is “stuff” that we want to manipulate, and procedures are descriptions of the rules for manipulating the data. Thus, any powerful programming language should be able to describe primitive data and primitive procedures and should have methods for combining and abstracting procedures and data…”

http://xuanji.appspot.com/isicp/1-1-elements.html