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Charting the Future Course of Behavior: Machine Learning and Artificial Intelligence |
Sunday, May 28, 2017 |
6:00 PM–6:50 PM |
Convention Center Mile High Ballroom 2C |
Area: PRA/TBA; Domain: Translational |
CE Instructor: Ryan Lee O'Donnell, M.S. |
Chair: Abigail Lewis (Bx+) |
RICHARD M. KUBINA (Penn State) |
PAUL THOMAS THOMAS ANDRONIS (Northern Michigan University) |
T. V. JOE LAYNG (Generategy, LLC) |
Abstract: According to Gartner research, 5.5 million new devices will connect every day this year and contribute to the Internet of Things (IoT). An estimated 6.4 billion IoT devices were expected to be used globally in 2016 alone. With forecast estimates of up to 20.8 billion IoT devices by 2020, analytics and data science professionals will need new and improved tools to explore and make sense of these massive datasets. Two areas that will be of importance to the world, and arguably the future of behavior analysis, are artificial intelligence and machine learning. Behavioral Science can contribute immensely towards these areas given our reliance on a coherent and systematic approach to philosophy, theory, experimental analysis, and practical applications to real-world issues. This panel seeks to address the potential avenues to pursue this future. The presenters will discuss their personal views and promising avenues for pursuit. Following will be an open discussion with attendees on inviting other perspectives and future directions and implications for the scientist-practitioner. |
Instruction Level: Basic |
Keyword(s): Artificial Intelligence, Internetof Things, Machine Learning |
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