R-DEX Systems (R-DEX) was formed in 2014 to develop and transition innovative technologies to Government and commercial clients. The R-DEX mission is to bring cutting edge Artificial Intelligence (AI), Machine Learning (ML), and robotics technologies to Government and commercial customers. R-DEX is actively engaged in multiple federally-funded efforts to develop a suite of technologies to achieve resilient AI systems that can be trusted, validated, and on par with human-like performance. These efforts focus in multiple modalities non-traditional sensor modalities, including Synthetic Aperture Radar (SAR), Inverse SAR, panchromatic electro-optical (EO), Electronic Warfare (EW), RF, and radar. Throughout the course of these efforts, R-DEX has developed and continues to develop a suite of Quality Assurance (QA) tools for AI/ML models with the goal of improving confidence in AI/ML results and to improve performance. In this talk, we will discuss R-DEX QA tools for AI/ML that focus in three general areas: 1) stress testing, 2) preventative tools, and 3) treatment tools. This discussion will include a summary of R-DEX’s proprietary Adversarial Input Detector (AID) models to detect and classify adversarial inputs as well as methods for prediction correction. These models hold for adversarial inputs that are caused by either bad actors or natural distortions. R-DEX’s tools also include techniques to combat epistemic uncertainty, namely, the uncertainty that AI/ML models face that arise from a lack of knowledge (e.g., lack of training examples, corrupted data, etc.). Common issues include data drift, unseen samples arising, and model not learning proper signals. R-DEX tools for combatting epistemic uncertainty include SDENets, data drift detection and retraining, and AI/ML forensics and meta-learning analysis. In addition, R-DEX tools to improve model robustness include DefenseGAN, R-DEX’s AID models, Jacobian Regularization, and Segmentation Masks.

Building AI Resiliency

Poster Session 2A

poster

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Robert Bock

1

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Building AI Resiliency

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