Moving mountains of data: Can humans learn from the failures of new technologies?

Moving mountains of data: Can humans learn from the failures of new technologies?

Moving mountains of data: Can humans learn from the failures of new technologies?

As machine learning advances, along with other sides of data science, some voices in the data industry are speaking out to draw attention to the potential negative effects application of these sciences will have, particularly in regard to forcing humans out of jobs given to machines.

But others, such as Janet George (pictured), fellow and chief data officer/scientist/big data/cognitive computing at WD, a Western Digital Co., are arguing that humans will remain an essential part of the work equation and that they should learn to work alongside the intelligent machines. She spoke with Lisa Martin (@Luccazara), co-host of theCUBE, SiliconANGLE’s mobile live streaming studio, at the Stanford Global Women in Data Science (WiDS) Conference in Stanford, CA. (*Disclosure below.)

Industrial data science led the discussion, with George sharing some insights on how Western Digital approaches using data nodes at the extremely large scales that are being established as standards for the future of data-centric tech companies. “Can we recognize packed-in information at very large scales?” was one question she identified as a driver of their development, along with detecting and extrapolating from patterns at that scale.

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Having machine learning recognize missing or malformed data, as well as understanding correlations, is another part of Western Digital’s targets for effective usage of its data, and George was confident in its ability to achieve those goals. “At Western Digital, we move mountains of data. That’s just part of our job. … Data’s inherently very familiar to us,” she said.

By bringing its data together with data science and machine learning, she continued, “We’re really tapping into our data to understand how we can make artificial intelligence and machine learning ingrained.” And while advancing that understanding has taken considerable investment, George felt that it was an essential step. “If we’re going to lead the world’s data, we need to understand our own data,” she stated.



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