A new system developed by researchers at Massachusetts Institute of Technology (MIT) and Artificial Intelligence Laboratory (CSAIL) can surpass the smartest people in the world. It aims to exclude the human element out of the data analysis, said its makers.

The new system has been named ‘Data Science Machine’, which could be considered as a breakthrough in the field of artificial intelligence. The new system of AI aims to take humans out of data analysis.

The research team tested the first prototype of the new data-crunching system by enrolling it in three data science competitions, the statement says. It competed against teams of humans to find predictive patterns in unfamiliar data sets. The Data Science Machine finished ahead of 615 out of 906 human teams. And, instead of taking months to come up with prediction algorithms like the human teams, the machine took less than one day.

Researchers at the Massachusetts Institute of Technology (MIT) have developed a system they call the Data Science Machine, which recently beat out over 600 human teams in finding predictive patterns buried in unfamiliar data sets.

As per experts, big-data analysis requires human perception for searching patterns and choosing which features of data need to be analyzed. On the other hand, the new system allows doing both these things without any human involvement. Instead its makers claim that it can give better results than humans.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions. In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

Veeramachaneni said the machine could be a crucial asset in finding what components of a data set should be analyzed in order to draw conclusions.

For example, although MIT records student performance on online courses, it does not record statistics that could predict a student's likelihood to drop out. The Machine could identify variables such as how long it takes a student to get started on an assignment as well as how much time the student is active in the course and thereby infer the likelihood of course dropout.

MIT researchers in order to test the ability of their new system participated in three different science data competitions. During the competitions, researchers noted that their system made 94%, 96% and 87% accurate predictions.

The researchers revealed that their system completed its tasks ahead of 615 of 906 human teams. Where on one hand teams with human participants took several months to generate prediction algorithms, the Data Science Machine took just 2 to 12 hours to complete its assigned tasks.

Max Kanter, who is doing his MIT master's thesis in computer science on Data Science Machine, said in a statement that Data Science machine is a natural complement to human intelligence.

It also looks for so-called categorical data, which appear to be restricted to a limited range of values, such as days of the week or brand names. It then generates further feature candidates by dividing up existing features across categories.

Once it’s produced an array of candidates, it reduces their number by identifying those whose values seem to be correlated. Then it starts testing its reduced set of features on sample data, recombining them in different ways to optimize the accuracy of the predictions they yield.

“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 said.

To conduct analyses, the Machine looks at correlations between data tables using numerical identifiers. It then continually updates these identifiers as it continues to import data. As the identifiers add up, the Machine carries out various mathematical operations such as averages and sums and attempts to find trends in the data.

Harvard University computer science professor Margo Seltzer said the project is "one of those unbelievable projects" seeking to solve real-world problems through a new approach.

The Computer Science and Artificial Intelligence Laboratory – known as CSAIL ­– is the largest research laboratory at MIT and one of the world’s most important centers of information technology research.

CSAIL and its members have played a key role in the computer revolution. The Lab’s researchers have been key movers in developments like time-sharing, massively parallel computers, public key encryption, the mass commercialization of robots, and much of the technology underlying the ARPANet, Internet and the World Wide Web.