학술논문
Developing a Series of AI Challenges for the United States Department of the Air Force
Document Type
Conference
Author
Gadepally, Vijay; Angelides, Gregory; Barbu, Andrei; Bowne, Andrew; Brattain, Laura J.; Broderick, Tamara; Cabrera, Armando; Carl, Glenn; Carter, Ronisha; Cha, Miriam; Cowen, Emilie; Cummings, Jesse; Freeman, Bill; Glass, James; Goldberg, Sam; Hamilton, Mark; Heldt, Thomas; Huang, Kuan Wei; Isola, Phillip; Katz, Boris; Koerner, Jamie; Lin, Yen-Chen; Mayo, David; McAlpin, Kyle; Perron, Taylor; Piou, Jean; Rao, Hrishikesh M.; Reynolds, Hayley; Samuel, Kaira; Samsi, Siddharth; Schmidt, Morgan; Shing, Leslie; Simek, Olga; Swenson, Brandon; Sze, Vivienne; Taylor, Jonathan; Tylkin, Paul; Veillette, Mark; Weiss, Matthew L; Wollaber, Allan; Yuditskaya, Sophia; Kepner, Jeremy
Source
2022 IEEE High Performance Extreme Computing Conference (HPEC) High Performance Extreme Computing Conference (HPEC), 2022 IEEE. :1-7 Sep, 2022
Subject
Language
ISSN
2643-1971
Abstract
American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances.