Quantitative Searching

Last March, Stephen Wolfram (of Mathematica fame) announced the eponymous Wolfram|Alpha, a computational data engine based on natural language processing, a large library of algorithms and what he coined as a new kind of science approach to answering queries. According to Wolfram, Wolfram|Alpha is "a first step in an ambitious, long-term project to make all systematic knowledge immediately computable by anyone." I happen to be quite fond of first steps in ambitious long-term projects.

You can get an idea of how Wolfram|Alpha works by exploring a set of provided examples. You can also track the project's progress through a blog. The project's goal is to make it possible for people to access essentially any kind of systematic factual knowledge. You can watch an informative screen cast of Wolfram|Alpha here.

The Wolfram|Alpha engine differs from traditional web search engines in that it doesn't just return a list of results based on a query, but instead it attempts to compute a set of quantitative answers by accessing its own internal knowledge base. This is pretty interesting stuff. However, I think it would be even more interesting, and incredibly more powerful, if Wolfram|Alpha could return informed answers based on a combination of its library of algorithms operating on the distributed resources of the web rather than only on an internal knowledge base. I guess we'll have to wait a bit longer for that.