On not reading books. Franco Moretti, author of the controversial Graphs, Maps, Trees: Abstract Models for a Literary History, proposes that literary study needs to abandon "close reading" for "distant reading": "understanding literature not by studying particular texts, but by aggregating and analyzing massive amounts of data." He is co-founder of the Stanford Literary Lab, where he and like-minded colleagues have published studies on programming computers to use statistical analysis to identify a novel's genre(PDF) and analyzing plots as networks(PDF). Similar projects are on the way.
It has applications in health care, pharmaceuticals, facial recognition, economics/related areas, and of course, much much more. Previously, MeFi discussed controversial homeland security applications, and the nexus between social networking and mobile devices that further contributes to the pool. With plenty to dig into, let's talk Data Mining in more detail. [more inside]
Kaggle hosts competitions to glean information from massive data sets, a la the Netflix Prize. Competitors can enter free, while companies with vast stores of impenetrable data pay Kaggle to outsource their difficulties to the world population of freelance data-miners. Kaggle contestants have already developed dozens of chess rating systems which outperform the Elo rating currently in use, and identified genetic markers in HIV associated with a rise in viral load. Right now, you can compete to forecast tourism statistics or predict unknown edges in a social network. Teachers who want to pit their students against each other can host a Kaggle contest free of charge.
How (not) to write an online-dating message, based on a sample of 500,000 "first contact" messages. [more inside]