Advancing AI Challenges for the United States Department of the Air Force
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2025
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| author | Prothmann, Christian Gadepally, Vijay Kepner, Jeremy Borchard, Koley Carlone, Luca Folcik, Zachary Grith, J. Daniel Houle, Michael How, Jonathan P. Hughes, Nathan Igbinedion, Ifueko Jananthan, Hayden Jayashankar, Tejas Jones, Michael Karaman, Sertac Kurien, Binoy G. Lancho, Alejandro Lavezzi, Giovanni Lee, Gary C. F. Leiserson, Charles E. Linares, Richard McEvoy, Lindsey Michaleas, Peter Milner, Chasen Pentland, Alex Polyanskiy, Yury Popovich, Jovan Price, Jeffrey Reid, Tim W. Riley, Stephanie Samsi, Siddharth Saunders, Peter Simek, Olga Veillette, Mark S. Weiss, Amir Wornell, Gregory W. Rus, Daniela Ruppel, Scott T. |
| author_facet | Prothmann, Christian Gadepally, Vijay Kepner, Jeremy Borchard, Koley Carlone, Luca Folcik, Zachary Grith, J. Daniel Houle, Michael How, Jonathan P. Hughes, Nathan Igbinedion, Ifueko Jananthan, Hayden Jayashankar, Tejas Jones, Michael Karaman, Sertac Kurien, Binoy G. Lancho, Alejandro Lavezzi, Giovanni Lee, Gary C. F. Leiserson, Charles E. Linares, Richard McEvoy, Lindsey Michaleas, Peter Milner, Chasen Pentland, Alex Polyanskiy, Yury Popovich, Jovan Price, Jeffrey Reid, Tim W. Riley, Stephanie Samsi, Siddharth Saunders, Peter Simek, Olga Veillette, Mark S. Weiss, Amir Wornell, Gregory W. Rus, Daniela Ruppel, Scott T. |
| contents | The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing AI Challenges for the United States Department of the Air Force Prothmann, Christian Gadepally, Vijay Kepner, Jeremy Borchard, Koley Carlone, Luca Folcik, Zachary Grith, J. Daniel Houle, Michael How, Jonathan P. Hughes, Nathan Igbinedion, Ifueko Jananthan, Hayden Jayashankar, Tejas Jones, Michael Karaman, Sertac Kurien, Binoy G. Lancho, Alejandro Lavezzi, Giovanni Lee, Gary C. F. Leiserson, Charles E. Linares, Richard McEvoy, Lindsey Michaleas, Peter Milner, Chasen Pentland, Alex Polyanskiy, Yury Popovich, Jovan Price, Jeffrey Reid, Tim W. Riley, Stephanie Samsi, Siddharth Saunders, Peter Simek, Olga Veillette, Mark S. Weiss, Amir Wornell, Gregory W. Rus, Daniela Ruppel, Scott T. Artificial Intelligence Computers and Society General Literature Machine Learning The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies. |
| title | Advancing AI Challenges for the United States Department of the Air Force |
| topic | Artificial Intelligence Computers and Society General Literature Machine Learning |
| url | https://arxiv.org/abs/2511.00267 |