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

arXiv: https://arxiv.org/abs/2207.07033 · HTML: https://mlsanity.com/paper/2207.07033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis](https://mlsanity.com/paper/1807.07814/) (2018)
- [Object Recognition with and without Objects](https://mlsanity.com/paper/1611.06596/) (2016)
- [Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations](https://mlsanity.com/paper/2004.07906/) (2020)
- [The MIT Supercloud Dataset](https://mlsanity.com/paper/2108.02037/) (2021)
- [Signal Enhancement for Magnetic Navigation Challenge Problem](https://mlsanity.com/paper/2007.12158/) (2020)
- [The MIT Supercloud Workload Classification Challenge](https://mlsanity.com/paper/2204.05839/) (2022)
- [Using a Power Law Distribution to describe Big Data](https://mlsanity.com/paper/1509.00504/) (2015)
- [Technical Report on Data Integration and Preparation](https://mlsanity.com/paper/2103.01986/) (2021)
- [Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset](https://mlsanity.com/paper/2110.07575/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data](https://mlsanity.com/paper/2401.16437/) (2024)
- [Advancing AI Challenges for the United States Department of the Air Force](https://mlsanity.com/paper/2511.00267/) (2025)

## Abstract (arXiv)

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.