A team of researchers from Massachusetts Institute of Technology (MIT) and Computer Science Artificial Intelligence Laboratory (CSAIL) claimed to have developed a unique computer system based on artificial intelligence that can outperforms even the smartest humans on Earth.

Data Science Machine (DSM) searches for patterns in data sets like a database of promotional sale dates or weekly profits. Computers can do perform many things faster than humans, but machines still requires human input to choose what to find in a huge data set.

In other words, such machines require human inputs to find meaning in patterns. The new computer system developed by MIT and CSAIL researchers now aims to automate that too.

Announcing the new system, the researchers noted, “While recent developments … enabled significant automation in feature engineering for those data types, feature engineering for relational and human behavioral data remains iterative, human-intuition driven, and challenging, and hence, time consuming.”

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.

DSM, which is being considered as a breakthrough in the field of artificial intelligence, apparently aims to take humans out of process of data analysis. In three contests, DSM competed against 906 human teams and outperformed 615 of them.

While participating humans worked on their predictive algorithms for several months, DSM was able make predictions in just 2-12 hours. Researchers claimed that their unique system made 94 per cent, 96 per cent and 87 per cent accurate predictions in the three contests respectively.