Autonomous Decision-Making, Machine Learning, and Artificial Intelligence:  What Does It All Mean and Why Should You Care?

We have entered a new era of analytics with machine learning and artificial intelligence algorithms beginning to deliver on the long promised advancement into self-learning systems.  These approaches allow us to solve previously intractable problems with completely new attack plans.  The appetite of deep learning algorithms for vast amounts of data and the ability to derive intelligence from diverse sets of noisy data allows us go far beyond previously capabilities in what we used to call advanced analytics.  However, to be successful we need to understand the capabilities and limitations of the new technologies.  We also need to develop new skill sets in order to harness the power of deep learning to create business value in an enterprise.

Learn about the future of artificial intelligence and how you can take advantage of emerging capabilities today.
Learn about the differences between deep learning and shallow learning and when to apply which technique.
Learn about the enabling technology for AI and ML – what works and what does not..

Stephen Brobst
Chief Technology Officer, Teradata Corporation

Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management. Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996. During Barack Obama’s first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD). He was recently ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.