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Developing a Series of AI Challenges for the United States Department of the Air Force

14 July 2022
V. Gadepally
Greg Angelides
Andrei Barbu
Andrew Bowne
L. Brattain
Tamara Broderick
Armando Cabrera
G. Carl
Ronisha Carter
Miriam Cha
Emilie Cowen
Jesse Cummings
Bill Freeman
James R. Glass
Sam Goldberg
Mark Hamilton
T. Heldt
Kuan-Wei Huang
Phillip Isola
Boris Katz
Jamie Koerner
Yen-Chen Lin
David Mayo
Kyle McAlpin
T. Perron
J. Piou
H. Rao
Hayley Reynolds
Kaira Samuel
S. Samsi
Morgan S. Schmidt
Leslie Shing
Olga Simek
Brandon Swenson
Vivienne Sze
Jonathan E. Taylor
Paul Tylkin
Mark S. Veillette
Matthew L. Weiss
Allan B. Wollaber
S. Yuditskaya
J. Kepner
    AI4CE
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Abstract

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure 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.

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