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来源类型 | Report |
规范类型 | 报告 |
DOI | https://doi.org/10.7249/RRA341-1 |
来源ID | RR-A341-1 |
Technology Innovation and the Future of Air Force Intelligence Analysis: Volume 1, Findings and Recommendations | |
Lance Menthe; Dahlia Anne Goldfeld; Abbie Tingstad; Sherrill Lingel; Edward Geist; Donald Brunk; Amanda Wicker; Sarah Lovell; Balys Gintautas; Anne Stickells; et al. | |
发表日期 | 2021-01-27 |
出版年 | 2021 |
语种 | 英语 |
结论 |
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摘要 | 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. ,Air Force/A2 asked researchers at RAND Project AIR FORCE 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. |
目录 |
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主题 | Intelligence Analysis ; Intelligence Collection ; Machine Learning ; Military Logistics ; United States Air Force |
URL | https://www.rand.org/pubs/research_reports/RRA341-1.html |
来源智库 | RAND Corporation (United States) |
引用统计 | |
资源类型 | 智库出版物 |
条目标识符 | http://119.78.100.153/handle/2XGU8XDN/524349 |
推荐引用方式 GB/T 7714 | Lance Menthe,Dahlia Anne Goldfeld,Abbie Tingstad,et al. Technology Innovation and the Future of Air Force Intelligence Analysis: Volume 1, Findings and Recommendations. 2021. |
条目包含的文件 | ||||||
文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
RAND_RRA341-1.pdf(2283KB) | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
x1611757305920.jpg.p(3KB) | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 |
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