Dr. Steven ‘Cap’ Rogers
Air Force Research Laboratory, Senior Scientist for Autonomy
Talk Title: Autonomy Way Forward
Dr. Steve “Cap” Rogers is a Senior Scientist (ST) at the Air Force Research Laboratory (AFRL) where he serves as the principle scientific authority for Autonomy. As the Autonomy ST, Dr. Rogers leads the newly initiated AFRL Autonomy Capability Team (ACT3) in the rapid advancement of autonomy R&D. Dr. Rogers also leads multi-Directorate coordinated autonomy R&D including the Autonomy in Motion (AiM) and Autonomy at Rest (A@R) portfolios. His personal research has focused on QUalia Exploitation of Sensing Technology (QuEST), how to build autonomous systems by replicating the engineering characteristics of consciousness. After retiring from active duty in the Air Force, Dr. Rogers founded a company (iCAD) for developing practical applications of advanced information processing techniques for medical products. The company invented the world’s most accurate computer aided detection system for breast cancer. He has over 150 technical publications and more than 20 patents.
When you engage a new colleague on a topic, almost always there is a need for a ‘break-in’ period used to converge on the meaning of words. Dr. Steve ‘Cap’ Rogers presents a Frequently Asked Questions approach to lowering that barrier. If you have ever been asked or wondered ‘What is Intelligence’, ‘What is Autonomy?’, ‘What is artificial intelligence?’, ‘What is reasoning?’, ‘What is consciousness?’, ‘Do machines understand me?’, ‘What is understanding?’, ‘What is cognition?’, or ‘What is cognitive Electronic Warfare?’, you will resonate with the need to generate a consistent set of answers to these questions and many more. The consistency in the answers leads to a more precise description of mission capabilities and mission capability gaps.
This discussion starts with examples that illustrate where we are in AI/ML; commercial successes in the medical area targeting early detection of breast and lung cancer, as well as breakthroughs in gaming as demonstrated by the recent success of the Google DeepMind AlphaGo and AlphaZero systems. Then we will provide a brief look back on how we got here by looking at the waves of work in AI/ML and the neurophysiological insights culminating in the dogma surrounding current deep learning successes. That leads to a discussion on what is different this time versus prior waves of AI/ML. We will conclude with a look at the limitations of current approaches and how addressing the really important question of machine consciousness from an engineering characteristics perspective might bring value.