This article by Broder and Mitzenmacher gives a good description of how bloom filters work and what they can do for you. The bloom filter basically replaces a dataset with a filter that can tell you if an item is a member of that set or not. It will not give false negatives, but it might give false positives. In practise, this is a negative property that can be outweighted by the space savings a bloom filter introduces; after all, you do not need to query the dataset to determine membership. The most important and summarizing quote you should remember from the article:

The Bloom filter principle: Wherever a list or set is used, and space is at a premium, consider using a Bloom filter if the effect of false positives can be mitigated.

The article also gives a number of examples in which bloom filters are used. E.g. to aid resource location in P2P and cache systems.

Someone had to do it: a Map-Reduce system build around the browser. Just point your browser to a URL and you are instantly helping someone to solve large problems by taking part in the process and running a number of jobs. If you think about this, it can even be used to replace advertisements. Instead of looking at flashy ads, a site can load a few tasks in the background (a frame would be best) and use some of your CPU power That would probably even be cheaper for the visitor than running the CPU power drain called “Abobe Flash” to show the usual “OMG you just won an iPod!!!” ads.