AI News, Artificial Intelligence Outperforms Human Data Scientists

Artificial Intelligence Outperforms Human Data Scientists

Artificial intelligence may be poised to ease theshortage of data scientists who build models that explain and predict patterns in the ocean of“Big Data” representingtoday’s world.

“If you look at the growth indemand for people with data science abilities, it’sfar outpacing the number of people who havethose skills.” The Data Science Machine can automatically createaccurate predictive models based on raw datasets within two to 12 hours;

The reason for such high demand for data scientists comes fromBig Data’s revolutionary promiseof tapping into vast collections of data—whether it’s the onlinebehavior of social media users, the movements offinancial markets worth trillions of dollars, or the billions of celestial objects spotted bytelescopes—toexplain and predict patterns in the huge datasets.

But how do you transforma sea of raw data into information that can help businesses or researchers identify and predict patterns?Human data scientists usuallyhave to spend weeks or months working on their predictive computer algorithms.

Such a time-consuming part of the data scientists’ job description inspired Kanter, an MIT grad student at the time, andKalyan Veeramachaneni, a research scientist atMIT’sComputer Science and AI Lab who acted as Kanter’s master’s thesis advisor, to try creating acomputer program that could automate the biggest bottlenecks indata science.

theautomation of the “featureengineering” process of identifying and extracting relevant variables from the sea of raw data.A second part of the new MIT software focuseson auto-tuning:figuring out the best set of parameters to generate the best predictions from the data.

”Typically the Data Science Machine doeswell andbeats many humans, but some humans beat it,” Kanter explained.“So it would be naive to say human data scientists don’t have any value.” Kanter andVeeramachaneni see the Data Science Machine as an automated tool that can empower data scientists and make them more efficientrather than putting them out of ajob.

Data Science Machine’s efficient results might be good enough for an astronomer working in a university lab, or the marketing arm oftraditional retail business that has yet to really build a data science team.As Kanter pointedout: In the future world we’re headed for, where every company makes data-driven decisions, you can’t just make existing data scientists more efficient;

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