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Design and implementation of the U.S. Army Artificial Intelligence Innovation Institute
Author(s): Greg Cirincione; Tien Pham; Andrew Ladas; Brian Stanton; Gregory Fischer
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Paper Abstract

It is readily evident that the U.S. Army must establish an artificial intelligence (AI) and machine learning (ML) science and technology (S and T) strategy to rapidly capture, develop, and field the steady stream of discoveries and innovations derived from the global proliferation of AI. In order to focus on Army-specific problem sets, the U.S. Army Combat Capabilities Development Command (CCDC) Army Research Laboratory (ARL) intends to stand up the Army AI Innovation Institute (A2I2) in 2019. This paper discusses how the A2I2 will coordinate, conduct, and accelerate basic research to address Army-specific challenges, with a focus on advancing AI capabilities for autonomous maneuver in multi-domain operations (MDO). CCDC ARL will leverage its existing distributed high-performance computing (HPC) and network infrastructure, along with its regional laboratory extensions, to enable basic AI research with top-tier universities, small and large commercial businesses, established Department of Defense (DOD) industrial partners, and other DOD and non-DOD government organizations. This paper also discusses how the A2I2 will establish an accessible database of heterogeneous data, a repository of AI and ML algorithms and software tools, and military-relevant challenge problems.

Paper Details

Date Published: 10 May 2019
PDF: 13 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060B (10 May 2019); doi: 10.1117/12.2524026
Show Author Affiliations
Greg Cirincione, U.S. Army Research Lab. (United States)
Tien Pham, U.S. Army Research Lab. (United States)
Andrew Ladas, U.S. Army Research Lab. (United States)
Brian Stanton, U.S. Army Research Lab. (United States)
Gregory Fischer, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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