Advancing AI Challenges for the United States Department of the Air Force*

Christian Prothmann,Vijay Gadepally,Jeremy Kepner, Koley Borchard,Luca Carlone, Zachary Folcik, J. Daniel Grith, Michael Houle,Jonathan P. How, Nathan Hughes,Ifueko Igbinedion,Hayden Jananthan,Tejas Jayashankar,Michael Jones,Sertac Karaman, Binoy G. Kurien,Alejandro Lancho, Giovanni Lavezzi, Gary C. F. Lee,Charles E. Leiserson,Richard Linares, Lindsey McEvoy,Peter Michaleas, Chasen Milner,Alex Pentland,Yury Polyanskiy, Jovan Popovich, Jeffrey Price, Tim W. Reid, Stephanie Riley,Siddharth Samsi, Peter Saunders,Olga Simek, Mark S. Veillette,Amir Weiss,Gregory W. Wornell,Daniela Rus, Scott T. Ruppel

2025 IEEE High Performance Extreme Computing Conference (HPEC)（2025）

Massachusetts Institute of Technology

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摘要

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.

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关键词

Artificial Intelligence Applications,Massachusetts Institute Of Technology,Artificial Intelligence Research,Mean Square Error,Convolutional Neural Network,Machine Learning Models,Natural Language,Public Datasets,Long Short-term Memory,Hybrid Model,Language Model,Radar Images,Radar Data,Signal Separation,Challenging Dataset,Radio Frequency Signal,Scaling Constant,Amazon Web Services,Propulsion System,Proximal Policy Optimization,High-level Synthesis,Synthetic Benchmark,Quadrature Phase Shift Keying,Machine Learning Techniques,Learning Algorithms,Power-law