Some machine learning proponents claim you only have to provide data to get value. However, reality is a bit more complex. On the way to active analytics for business, we have to answer two big questions: What must happen to data before running machine learning algorithms, and how should machine learning output be used to generate actual business value?
Jean-François Puget demonstrates the vital role of human context in answering those questions. You’ll discover why human context should be embraced as a guide to building better, smarter systems that people will use, trust, and love.
This keynote is sponsored by IBM.
Jean-François Puget is the technical lead for IBM machine learning and optimization offerings. Jean-François has spent his entire career turning scientific ideas into innovative software. He joined IBM as part of the ILOG acquisition and since then has held various technical executive positions, including CTO for IBM Analytics Solutions. Jean-François has published over 80 scientific papers in refereed journals and top AI conferences. He holds a PhD in machine learning from Paris IX University.
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