Working Alongside Thinking Machines
Working Alongside Thinking Machines
Part 3 of the 2018 UA College of Science Lecture Series
Featuring Nirav Merchant, Director Data Science Institute, Data7, University of Arizona
Machine learning (ML) based systems are rapidly becoming pervasive, powering many applications from recommending music, movies and merchandise to driving our cars to assisting in medical diagnoses. Our daily interactions, behavior, and choices, whether we are aware of them or not, are the sources of data for training these systems. But how are these ML based platforms built and utilized ? While ML based platforms create amazing opportunities, especially when coupled with advances in cloud computing, reliance on these platforms comes with ethical, security, and technical concerns. How do we strike a balance for enabling pragmatic and productive use of these capabilities? ML powered platforms are gaining proficiency and becoming deeply integrated into existing and emerging automation across many domains of science and society, causing a shift in opportunities impacting many professions. What are the new learning and training opportunities that allow us to stay relevant and lead the way for future innovations.
