Quartz describes an MIT study with the surprising conclusion that at least in some circumstances, an algorithm can not only sift numbers faster than humans (after all, that's what computers are best at), but also discern relevant factors within a complex data set more accurately and more quickly than can teams of humans. In a competition involving 905 human teams, a system called the Data Science Machine, designed by MIT master's student Max Kanter and his advisor, Kalyan Veeramachaneni, beat most of the humans for accuracy and speed in three tests of predictive power, including one about "whether a student would drop out during the next ten days, based on student interactions with resources on an online course." Teams might have looked at how late students turned in their problem sets, or whether they spent any time looking at lecture notes. But instead, MIT News reports, the two most important indicators turned out to be how far ahead of a deadline the student began working on their problem set, and how much time the student spent on the course website. ... The Data Science Machine performed well in this competition. It was also successful in two other competitions, one in which participants had to predict whether a crowd-funded project would be considered “exciting” and another if a customer would become a repeat buyer.