Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

A new and unique computer system from MIT has outperformed human intuition using its algorithms, and it’s amazing, and perhaps a little frightening: the Data Science Machine beat out over 600 human teams in finding predictive analysis.

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

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.

Read more at Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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The Beer Game

The Beer Game

A rite of passage for new Sloan MBA students provides lessons in systems thinking.

Thursday, August 29, 1:00 p.m.
It is a miserably muggy afternoon in Cambridge as the incoming class of the MIT Sloan School of Management—roughly 400 students from 41 countries—files into a second-floor ballroom at the Kendall Square Marriott. They are here to play the Beer Game, a Sloan orientation tradition. Unfortunately given the weather, the Beer Game does not involve drinking cool beverages.

“There is no actual beer in the Beer Game,” says John Sterman, the Sloan professor who is overseeing the proceedings for the 25th consecutive year.

Rather, the Beer Game is a table game, developed in the late 1950s by digital computing pioneer and Sloan professor Jay Forrester, SM ’45. Played with pen, paper, printed plastic tablecloths, and poker chips, it simulates the supply chain of the beer industry. In so doing, it illuminates aspects of system dynamics, a signature mode of MIT thought: it illustrates the nonlinear complexities of supply chains and the way individuals are circumscribed by the systems in which they act.

All that will be explained in a class-wide debriefing Sterman will conduct after the game. For now, it’s game on, and as a writer for MIT News, I’ve been invited by Sterman to play this year. I go to one of the 47 tables where students are randomly seating themselves in teams of eight, introduce myself to my seven teammates (MBA candidates from India, Peru, and the United States), and listen to Sterman explain the rules.

Beer game simulates the reality of supply chain management. If you are interested in knowing more about supply chain management, feel free to contactus.