Session 1 - 10:00 - 12:00

Cognitive EW: Assuring In-Mission Learning for EW

This presentation will discuss the challenges for assuring the performance of a system that can learn from novel experiences in the field. EW systems operate at a timescale that means they cannot afford to learn post-mission, or with human supervision. EW systems must learn from a single observation, using self-supervised reinforcement feedback. The validation infrastructure must therefore support automated closed-loop, multi-resolution testing, and ways to test the effectiveness of actions. We must validate the learning process, rather than validating the learned model.

Lunch Break: 12:00 - 12:30

Session 2 - 1:00 - 3:00

Cognitive EW: Data Requirements for AI

A common myth is that we need a lot of data to train an AI-ML system. This myth not only causes developers to fear data collection, but also causes developers to create solutions that are not fieldable. EW systems can leverage first-principle models (such as physics), feature engineering, and progression of the engagement to dramatically reduce the data collection requirements, and create systems that can learn on single observations of novel environments.

Presented by Dr. Karen Haigh

Dr. Karen Haigh is an expert and consultant in Cognitive EW and embedded AI. Her focus is on physical systems with limited communications and limited computation resources that must perform under fast hard-real-time requirements. She recently wrote the book "Cognitive EW: An AI Approach" with Julia Andrusenko.  She was a pioneer in three fields now common across the globe: (1) closed-loop planning and machine learning for autonomous robots, (2) smart homes for elder care, and (3) cognitive RF systems. Dr. Haigh is a regular presenter to both academic and military communities around the world, and has created a variety of online content discussing embedded AI for mission-critical systems, supporting rapid real-time in-mission learning, and assuring AI in the field. Her course on Cognitive EW is available through the Association of Old Crows. Dr. Haigh has over 30 years of experience in the aerospace industry, working for Honeywell, BBN Technologies, L3, and Mercury Systems.

Dr. Haigh has chaired over a dozen AI conferences, symposia, and workshops, and is a regular program committee member and technical paper reviewer. She has over 100 publications, and 21 patents, including 3 books.

She received her PhD in from Carnegie Mellon University in Computer Science with a focus on AI and Robotics, and her undergraduate from the University of Ottawa in Honours Computer Science (summa cum laude).

https://sites.google.com/site/kzhaigh

https://www.linkedin.com/in/karenhaigh/

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