Field-Friendly Artificial Intelligence and Machine Learning Systems

STTR topic DON27TZ02-NV002 (U.S. Department of Defense – Navy), Phase I and II. Open to U.S. small businesses; STTR projects partner with a U.S. research institution. Opens: December 2, 2026. Closes: January 6, 2027. Current DNN architectures suffer from inherent "Black Box" vulnerabilities, demanding massive computational scaling and offering no clear link between parameter count and performance. The proposed model must address these shortfalls: - Optimized Resource Utilization: Unlike DNNs that heuristically overparameterize to boost accuracy, the proposed model must quantitatively map parameter count to expected performance and enable users to identify the minimum configuration required for mission success, significantly reducing computational overhead, complexity, and deployment costs. - Transparency: The model must replace opaque, distributed neural layers with novel methods for interpretability and trust that go beyond the current post-hoc techniques. By making input-output logic similar to standard programming code, the model should ensure decisions are easily explainable, verifiable, and compliant with auditing standards. - Reliability: The model must recognize whether it can provide a reliable output for a given problem instance, which will address another well-known criticism of DNNs wherein performance is typically measured as an average, e.g., average accuracy, over many instances of a problem and will help build user trust and acceptability of the model vs DNNs, particularly in sensitive areas like person identification in a crowd video and identifying the source of a cyber-security violation. The model must deliver DNN-level performance with superior speed and a reduced memory footprint, optimized for lean, secure processing on low-cost CPU and embedded edge hardware. By eliminating cloud-dependence, the model will maximize data privacy and operational security while substantially reducing power consumption compared to GPU-reliant models. It must…