The rapid growth of artificial intelligence and machine learning (AI/ML) in commercial domains contrasts sharply with its slow adoption in defense applications. This talk addresses the core disparities between commercial and defense environments, aiming to identify the key barriers delaying AI/ML integration into national security systems. By bridging these two domains, the presentation establishes a clearer understanding of what technical and operational shifts are required for signal processing and AI researchers to effectively adapt advanced algorithms for defense-critical use cases.
While AI/ML gains initial traction in specific defense problems such as sensor data processing and signal analysis, widespread adoption remains hindered by domain-specific challenges. Defense applications demand high robustness, explainability, deterministic performance under adverse conditions, and operation with limited or non-standardized training data—constraints rarely encountered in commercial settings. The evidence presented underscores that off-the-shelf commercial models cannot simply be transposed into military infrastructure without addressing fundamental issues in reliability, trust, and real-time execution bounds.
Ultimately, the defense sector's successful adoption of AI/ML relies on targeted collaboration with the research community to solve these operational limitations. Researchers must focus on developing trustworthy, robust algorithms tailored to unique defense constraints rather than purely pursuing commercial performance benchmarks. By directly addressing these gaps, the signal processing community can enable scalable, high-impact deployment of machine learning technologies across modern defense systems.
