MIT algorithm emulates human intuition in big data analysis

Max Kanter, who created the algorithm as part of his master’s thesis at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), entered the algorithm into three major big data competitions. In a paper to be presented this week at IEEE International Conference on Data Science and Advanced Analytics, he announced that his “Data Science Machine” has beaten 615 of the 906 human teams it’s come up against.

The algorithm used raw datasets to make models, predicting things such as when a student would be most at risk of dropping an online course, or what indicated that a customer during a sale would turn into a repeat buyer.

Kanter’s algorithm seems to do a decent job of approximating human “intuition” with much less time and manpower, he hopes it can provide a good benchmark.

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