MIT may soon be replacing human intuition with algorithms. Scientists have created a new big-data analysis system that outperforms 615 of 906 human teams.
In the three competitions, the Data Science Machine made predictions that were 94, 96, and 87 percent as accurate as the winning submissions, performing better than 615 of the 906 participating teams. ‘We view the Data Science Machine as a natural complement to human intelligence,’ Max Kanter, whose master’s thesis provides the basis for the Data Science Machine, told MIT News.
The system exploits structural relationships inherent in database design; databases typically store different types of data in different tables, indicating the correlations between them using numerical identifiers. The Data Science Machine tracks these correlations, using them as a cue to feature construction.
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‘There’s so much data out there to be analyzed. ‘And right now it’s just sitting there not doing anything,’ he said.
‘So maybe we can come up with a solution that will at least get us started on it, at least get us moving.’ The Data Science Machine completed its prediction algorithms at ‘inhuman’ speed, taking between two and 12 hours for each submission.
Human teams worked on their algorithms for months.
More information: "Deep Feature Synthesis: Towards Automating Data Science Endeavors." groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf