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AI for National Security

NSCAI Report: How the United States Can Maintain AI Leadership

The final report of the National Security Commission on Artificial Intelligence (NSCAI) realistically assesses the current state of AI and the investment required for the United States to maintain technology leadership, especially when safeguarding against shifting, increasingly malign threats. Based on deep analysis, the report’s most critical message is simply this: “America is not prepared to defend or compete in the AI era.” To address this challenge, the nation must channel its ingenuity across the public and private sectors into a coordinated, “all-in” push to responsibly develop and deploy AI at scale.

AI’s Game-Changing Potential to Transform Intelligence Analysis

Intelligence organizations can use AI to enhance process efficiency and productivity for improved insights that strengthen national security.

  • Automate mundane processes and increase intelligence throughput
    Organizations can use AI to automate a large portion of the time- and resource-intensive intelligence analysis process, achieving the scale needed for processing and enabling analysts to focus on the challenging problems AI is not yet able to solve.
  • Allow analysts to derive value from previously inaccessible information
    With AI, organizations can process the vastly increasing volume of multi-INT data in near-real time to detect patterns and anomalies, identify content that requires further processing, and discover insights never before possible.
  • Support fusion across data sources and types to allow for richer insights
    By applying AI technologies such as natural language processing (NLP), analysts can review a broad range of data sources, rapidly develop summaries and correlations, and deliver unprecedented insights.
  • Continue to advance AI to augment the intelligence analyst
    Sophisticated algorithms currently in development will expand the uses of AI and provide significant near-term value to the IC with minimal customization. Examples include NLP, language translation, object detection, and automated data tagging.

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