Presentation
12 April 2021 The JHU/APL AI technology roadmap: research and exploratory development on the path to intelligent systems for every mission
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Abstract
Recent breakthroughs and rapid progress in AI will impact, if not transform, every mission. JHU/APL developed an AI Technology Roadmap to guide the Laboratory’s contributions to the critical challenges the nation will face developing and implementing intelligent systems for these missions over the coming decades. We began this exercise by describing a series of envisioned futures for intelligent systems across sea, land, air, space and information, and examined them to identify the common AI technology vectors needed to achieve each vision: (1) Autonomous Perception, describing the path to intelligent systems that perceive in the context of the extreme uncertainty and complexity of the real world; (2) Superhuman Decision-Making and Autonomous Action, to realize the potential for intelligent systems to reason over more information than any team of analysts or operators and act in ways systems under manned control cannot; (3) Human-Machine Teaming at the Speed of Thought, to ensure humans can stay involved at speed and scale; and of particular importance for national security applications, (4) Safe and Assured Operation, so these systems can be trusted to stay true to commander’s intent, in adversarial and sensitive contexts. Each technology vector is aligned with a targeted goal, and with each goal we provide a roadmap in the form of near-, mid-, and long-term AI advances critical to reaching the goal. This paper describes the JHU/APL AI Technology Roadmap and presents key examples of recent progress and forward-looking research and exploratory development along each vector.
Conference Presentation
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Michael Wolmetz, Kapil Katyal, Christopher Ratto, Bart Paulhamus, I-Jeng Wang, and Ashley Llorens "The JHU/APL AI technology roadmap: research and exploratory development on the path to intelligent systems for every mission", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117460A (12 April 2021); https://doi.org/10.1117/12.2593329
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