Performance Breakthroughs Through Machine Learning and Deep Networks

News and excitement have been building as machine learning and, specifically, deep networks have led to performance breakthroughs in several areas. Perhaps tempering this enthusiasm are the concerns that arise from observed issues with the technology and its application. Depending on the application, questions arise about unintended bias (e.g. loan processing), unexpected failures (self-driving cars), strange competency failures (image classifications), and others.  It is easy to forget that AI/ML is at an early stage of technical maturity. My interest is understanding root issues, vulnerabilities, and then opportunities for advancing the art – as we find better ways to evaluate and improve systems that operate on “knowledge is learned.”