Technology Innovation and the Future of Air Force Intelligence Analysis- Volume 2, Findings and Recommendations
Abstract
Intelligence collections and demand have grown over the past two decades, and intelligence analysts are often performing routine tasks, leaving them unable conduct larger strategic analyses that are needed to address future threats as outlined by the 2018 National Defense Strategy. The authors provide an in-depth analysis of technologies that could help the Air Force Distributed Common Ground System (AF DCGS) become more effective, efficient, adept at using human capital, and agile. A key point is that artificial intelligence (AI) and machine learning (ML) technologies alone do not solve these intelligence challenges; rather, if they are properly implemented and complemented byhuman analysts who have the right skills and training, the capabilities can allow the AF DCGS to evolve to better meet warfighter needs. This is the second volume in a series about how AI/ML technology can help the AF DCGS meet the challenges of a demanding intelligence environment and the complexity of future threats envisioned by the 2018 National Defense Strategy. The authors provide more in-depth discussion of project methodology; a primer on AI/ML technology; case studies of analytic challenges in previous operations; best practices for successfully deploying new technologies; and other topics of interest to specialists, stakeholders, and experts.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2021
- Accession Number
- AD1121423
Entities
People
- Abbie Tingstad
- Amado Cordova
- Amanda Wicker
- Anne Stickells
- Balys Gintautas
- Dahlia A. Goldfeld
- Donald Brunk
- Edward Geist
- Lance Menthe
- Sarah Soliman
- Sherrill Lingel
Organizations
- RAND Corporation