Automated data-gathering techniques, together with inexpensive mass memory storage apparatus, have allowed the acquisition and retention of prodigious amounts of data. Point-of-sale customer purchases, temperature and pressure readings (along with other weather data), news feeds, financial transactions of all sorts, Web pages, and Web interaction records are just a few of numerous examples. But the great volume of raw data calls for efficient “data-mining” techniques for classifying, quantifying, and extracting useful information. Machine learning methods are playing an increasingly important role in data analysis because they can deal with massive amounts of data. In fact, the more data the better.

Most machine learning methods construct hypotheses from data. So (to use a classic example), if a large set of data contains several instances of swans being white and no instances of swans being of other colors, then a machine learning algorithm might make the inference that “all swans are white.” Such an inference is “inductive” rather than “deductive.” Deductive inferences follow necessarily and logically from their premisses, whereas inductive ones are hypotheses, which are always subject to falsification by additional data. (There may still be an undiscovered island of black swans.) Still, inductive inferences, based on large amounts of data, are extremely useful. Indeed, science itself is based on inductive inferences.