Man versus Machine? The latter wins, this time.
Researchers from the Massachusetts Institute of Technology have cracked the code to solve the major dilemma in modern day big-data analysis. While computers have fulfilled the task of looking for patterns in the ocean of data, human input had always been deemed indispensable in discerning which "features" of data to analyze.
According to a release on Eureka Alert, MIT researcher Max Kanter, with the guidance of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) research scientist Kalyan Veeramachaneni (as thesis adviser), has designed a system that "outperforms 615 of 906 human teams" -- quite possibly replacing human intuition in the big-data analysis "equation."
In order to assess the capability of the "Data Science Machine," the researchers entered it into three data science competitions -- of which it contended with human teams in spotting predictive patterns in random data sets, as reported by UPI.
The first two of three competitions resulted in 94% and 96% accuracy marks for the Data Science Machine, as told by Eureka Alert. The third, though, produced a slight dip of 87%. However, while the Machine was able to complete the predictions in 12 hours, the human teams took months to work on their predictive algorithms (also by Eureka Alert).
"We view the Data Science Machine as a natural complement to human intelligence," says Kanter as per Eureka Alert. "There's so much data out there to be analyzed. And right now it's just sitting there not doing anything. So maybe we can come up with a solution that will at least get us started on it, at least get us moving," he adds.
Thesis adviser Veeramachaneni said the Machine could lead researchers in the search for data components to be analyzed in order to draw well-founded conclusions.
"What we observed from our experience solving a number of data science problems for industry is that one of the very critical steps is called feature engineering," Veeramachaneni notes as per Eureka Alert. "The first thing you have to do is identify what variables to extract from the database or compose, and for that, you have to come up with a lot of ideas," he adds.
Margo Seltzer, a Harvard University computer science professor, believes that the innovation is "one of those unbelievable projects" that employs a new approach to address a real-world problem, adding that the technology will "become the standard quickly -- very quickly," according to UPI.
Kanter, along with Veeramachaneni will present the paper on Data Science Machine at the IEEE International Conference on Data Science and Advanced Analytics, as reported by Eureka Alert.