Methodologies for Continuous Life-long Machine Learning for AI Systems
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University
Current machine learning architectures, strategies, and methods are typically static and non-interactive, making them incapable of adapting to changing and/or heterogeneous data environments, either in real-time, or in near-real-time. Typically, in real-time applications, large amounts of disparate data must be processed, learned from, and actionable
intelligence provided in terms of recognition of evolving activities….
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