If big data is a haystack, then algorithms may be better than humans when it comes to finding a needle.

Computer algorithms root out patterns in immense data sets, produce correlations, and ultimately come up with predictions. Selecting what an algorithm analyzes has always been done by humans.

Until now.

A pair of researchers at the Massachusetts Institute of Technology (MIT) — Max Kanter, a master’s student in computer science, and his advisor, Kalyan Veeramachaneni, a research scientist at MIT’s computer science and artificial intelligence laboratory — have created the Data Science Machine to find patterns and select which data points are relevant, without the problem-solving help of humans.

The Data Science Machine is capable of making predictions from raw data, without the help of humans who are typically needed to choose the appropriate data points for a machine to analyze. The algorithm the machine uses to achieve this, called Deep Feature Synthesis, went up against human teams in three data science competitions. Out of 906 teams, the Machine beat 615. According to the research, “In 2 of the 3 competitions we beat a majority of competitors, and in the third, we achieved 94 percent of the best competitor’s score. In the best case, with an ongoing competition, we beat 85.6 percent of the teams and achieved 95.7 percent of the top submissions score.”

Read the article “Is an MIT Algorithm Better Than Human Intuition?” on csmonitor.com.