Speakers:

Kay Aikin is the CEO of Introspective Systems, a software company based in Portland Maine. She is a graduate of Pennsylvania State University and holds a degree in energy/sustainability engineering. She has spent her career as an energy engineer, architectural designer, and business development executive, currently the CEO of an AI-based complex systems software company

Dr. Betina Tagle is an Assistant Professor of Cybersecurity at the University of Maine at Augusta. She is a veteran of the US Navy as an Information Systems Technician (IT).  She was a 9/11 watchstander in CT. She has 20-years industry experience in both the public- and private-sector of Cybersecurity and IT. She holds a doctorate in Computer Science with a concentration in Information Assurance.

Dr. W.D. Rawle serves as Chair of the IEEE Maine Section and as a member of IEEE USA Congressional Policy Committees for Artificial Intelligence & Autonomous Systems, Research & Development, and Transportation & Aerospace. He sits on the Board of Directors for the New Hampshire Aerospace & Defense Export Consortium and chairs NHADEC’s Unmanned Systems Committee. He also serves as the Chair for the Avionics and Mission Systems Technical Committee of the American Helicopter Society. A thirty-year veteran of the aerospace industry, Rawle’s career has spanned General Electric, United Technologies, Ultra Electronics, and General Dynamics; and he has recently launched Oxford Analytics LLC, an advanced technology research consultancy focused on deep learning and autonomous systems.

Presentation Abstracts:

“Artificial Intelligence in the Electrical Grid”, Kay Aikin, CEO Introspective Systems

The electrical grid in both the US and throughout the world is rapidly changing. This transformation is progressing on both a physical sense with the introduction of Distributed Energy Resources (DER) such as solar, wind and energy storage but in the cyber realm with advanced analytics tools being deployed. Artificial Intelligence technologies has great promise in making this transformation happen.

The technologies used in the electrical grid range from rule-based systems, machine learning for pattern matching and Artificial Intelligence systems for real-time control that learn and evolve. This presentation describes various uses within the industry, challenges and opportunities and particularly the AI algorithms that Introspective Systems has been developing for the real-time resource allocation of energy.

“Safety Critical Design Considerations for Autonomous Systems”, W.D. Rawle PhD, Chair, IEEE Maine Section

Safety critical design methodologies have long been of significant interest in the aviation industry. Well established procedures and design artifacts, based almost entirely upon the “linearization” of stochastic processes, have been used to demonstrate design assurance at levels commensurate with the demands of public safety. However, as the world moves towards autonomous operation, well established procedures may not prove sufficient.

This presentation, beginning with a review of established practice directed by various standards body publications, explores the evolution and development of more advanced safety analysis techniques for the domain of autonomous operations. The presentation will review the extension of redundancy, fault tolerance and artificial intuition, or deep learning, into the realm of safety critical design. And finally, the presentation will review design and test methodologies, developing artifacts that may be used to demonstrate design assurance at specific levels that support public safety.

“Machine Learning: Algorithm Design and Implementation”, Dr. Betina Tagle, Assistant Professor, Cybersecurity, University of Maine at August"

Today in our society there is an ever growing number of applications (uses) in which we employ Artificial Intelligence (AI). Particularly a subset of AI, machine learning. Resume screening, visual and speech recognition, and self-driving cars to name a few. In such cases, inferred predictions are required because explicitly instructed decisions would not suffice. 

This presentation we will cover an overview of what machine learning is and the aspects of inference and knowledge representations for associations through designing and implementing algorithms. It is to gain a better understanding of how machine learning is used today and possibly tomorrow.

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