Technology Innovation and the Future of Air Force Intelligence Analysis. Volume 1, Findings and Recommendations

Abstract

There is growing demand for the Air Force Distributed Common Ground System (AF DCGS) to analyze sensor data. Getting the right intelligence to the right people at the right time is increasingly difficult as the amount of data grows and timelines shrink. The need to exploit all collections limits the ability of analysts to address higher-level intelligence problems. Current tools and databases do not facilitate access to needed information. Approach. Air Force/A2 asked RAND Project AIR FORCE (PAF) to analyze how new tools and technologies can help meet these demands, including how artificial intelligence (AI) and machine learning (ML) can be integrated into the analysis process. PAF assessed AF DCGS tools and processes, surveyed the state of the art in AI/ML methods, and examined best practices to encourage innovation and to incorporate new tools. Conclusions. Many analytic tasks can be fully or partially automated, although human involvement will continue to be necessary in more-complex tasks. AI/ML can free analysts to focus on solving intelligence problems and developing supporting technologies to make analysis more efficient. Analysts will require new skills both to facilitate use of AI/ML and to take advantage of opportunities to conduct more-advanced analysis. Recommendations. AF DCGS should leverage existing technologies to automate some analysis and reporting tasks and to make archival intelligence more accessible. Take advantage of AI/ML technologies, when available, for early-phase analysis tasks (e.g., identifying and tagging imagery, issuing threat warnings, re-tasking collectors). Organize to balance human effort across three competencies: supporting missions, supporting analysis, and solving intelligence problems. Recruit and train analysts with data science, programming, and other skills. Follow best practices for developing, implementing, and sustaining new tools.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2021
Accession Number
AD1121435

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

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Engineered Resilient Systems
  • Human Systems
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Vision
  • Computers
  • Detection
  • Employment
  • Geographic Information Systems
  • Information Systems
  • Intelligence Cycle
  • Machine Learning
  • Machine Translation
  • National Security
  • Neural Networks
  • Organizational Structure
  • Pattern Recognition
  • Surveillance
  • Warfare

Readers

  • Aerospace logistics and air mobility.
  • Defense Technology Research and Development.
  • Geospatial Intelligence and Artificial Intelligence Analytics

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy