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Some fairly well-known consumer-facing things that use machine learning are spam filters, recommendation engines, speech recognition systems (speech-to-text or customer service stuff), internet advertising, news clustering (Google News), related stories, handwriting recognition, questionable content identification, automatic closed captioning, and machine translation. These are not all equally successful or sophisticated, but are ML-based and mostly good enough to use. There are a number of examples that are not consumer-facing, like credit card fraud detection, snail mail routing, quantitative trading, market segmentation analysis, demand prediction for inventory control, and other things. It is also used for scientific data analysis in several areas, with bioinformatics being the really big one. There are other examples. There are also applications that are not considered machine learning, but use the same ideas for different purposes. An example would be modern codes, which are used for things like compression and satellite communications, and are based on the same `graphical models' pervasive in machine learning. There is hype, and some applications need only a little bit, but it is at least used in some real stuff. |