It’s a jewel one should have in its tool-shop.

 Let me relate a few war stories...

 Once I had a very very complex problem to solve.

 I had not the foggiest notion in which order to compute what.

 So I took the cowards way and hooked in the Observer all over the place
 so I didn't have to think in what order to do it.

 It was very slow and buggy and I was no closer to understanding in the
 problem than before. It did work occasionally though.

 I put in enough logging to see what order it did things in (when it
 worked). After glaring at that for an hour I saw the pattern, recoded
 it as a couple of tight while loops.

 Result...

 Very fast, very understandable, easily maintained, no bugs and no observers.

 Story two...

 Once I took over the maintenance of some code that had several
 observer pattern instances scattered around it.

 It was fragile, buggy, and erratic.

 After much loss of hair and many hours of poring over log traces I
 figured it out.

 There were complex loop paths through several observers. No mere
 mortal could really understand what would happen if object X updated,
 since the possible impacts and possible variants of paths were almost
 limitless and depended crucially on the order of registration of
 observers.

 After a brief killing spree amongst the instances of the observer
 pattern the code was still buggy, but at least no longer fragile and
 erratic...

groups.google.com/group/comp.lang.smalltalk.dolphin/msg/fae4a931c64f5311

Luis von Ahn is an assistant professor in the Computer Science Department at Carnegie Mellon University, where he also received his Ph.D. in 2005. Previously, Luis obtained a B.S. in mathematics from Duke University in 2000. He is the recipient of a Microsoft Research Fellowship.

ABSTRACT

Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs. This talk introduces a paradigm for utilizing human processing power to solve problems that computers cannot yet solve. Traditional approaches to solving such problems focus on improving software. I advocate a novel approach: constructively channel human brainpower using computer games. For example, the ESP Game, described in this talk, is an enjoyable online game — many people play over 40 hours a week — and when people play, they help label images on the Web with descriptive keywords. These keywords can be used to significantly improve the accuracy of image search. People play the game not because they want to help, but because they enjoy it.

I describe other examples of "games with a purpose": Peekaboom, which helps determine the location of objects in images, and Verbosity, which collects common-sense knowledge. I also explain a general approach for constructing games with a purpose.