As machine learning advances, some voices in the data industry are drawing attention to the potential negative effects underlying the application of these sciences, 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 Conference at Stanford University. (*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.
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.
Keeping humans involved
On the issue of machine learning being likely to cause disruption and potential job loss as it comes into widespread usage, George acknowledged the implications but also asserted that humans would continue to have a vital role in any system incorporating machine learning.
“Humans play a huge role, because these are domain experts. … What I see is the augmentation between machine learning and humans, and the domain experts,” she said, adding that using machine learning for building intelligence models that can inform the domain experts is “where Western Digital wants to be.”
George also expressed an interest in deep neural networks, noting, “Cognitive computing space has just started to open up” and that the failures accompanying experimentation with any emerging technology offered their own important value. “Failing fast and learning fast is part of data science, and I think that we have to get to that point, where we’re comfortable with failing and with what we learn from that failure,” she said.
And for the data scientists who will be gathering value from those failures, George shared what she sees as the most important qualities, in an intersection of the “three circles” of data, implementation and business acumen. With these three fields meeting, she felt that data scientists would be able to not just understand and assemble the data-tapping programs, but also to “ask the right questions and understand why that matters.”
“There’s a lot of fear in the industry,” George recognized, “And machine learning will make a lot of destructions in the industry, for sure. But I believe that that will cause a lot of shifts and changes.”
And with those challenges, she sees further opportunities. “I think humans have a natural tendency to step up [to challenges]. … This is a natural progression of the human race… I don’t think we can stop that, I think we should embrace it,” she concluded.
Watch the complete video interview, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the Stanford Global Women in Data Science (WiDS) Conference. (*Disclosure: TheCUBE is a media partner at the conference. Neither Stanford nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)