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Machines versus humans at Google

　　A curious revelation from Googler Peter Norvig appears in a recent post by Anand Rajaraman:
　　 [To execute a web search] a subset of documents is identified based on the presence of the user's keywords. Then, these documents are ranked by a very fast algorithm that combines ... 200 [pre-computed] signals in-memory using a proprietary formula.
　　
　　[This] appears to be made-to-order for machine learning algorithms. Tons of training data (both from usage and from the armies of "raters" employed by Google), and a manageable number of signals (200) -- these fit the supervised learning paradigm well, bringing into play an array of ML algorithms from simple regression methods to Support Vector Machines.
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