34 min

Using Large Language Models in the National Security Realm Software Engineering Institute (SEI) Podcast Series

    • Technology

At the request of the White House, the Office of the Director of National Intelligence (ODNI) began exploring use cases for large language models (LLMs) within the Intelligence Community (IC). As part of this effort, ODNI sponsored the Mayflower Project at Carnegie Mellon University’s Software Engineering Institute (SEI) from May 2023 through September 2023. The Mayflower Project attempted to answer the following questions:
How might the IC set up a baseline, stand-alone LLM? How might the IC customize LLMs for specific intelligence use cases? How might the IC evaluate the trustworthiness of LLMs across use cases? In this SEI Podcast, Shannon Gallagher, AI engineering team lead, and Rachel Dzombak, special advisor to the director of the SEI’s AI Division, discuss the findings and recommendations from the Mayflower Project and provides additional background information about LLMs and how they can be engineered for national security use cases.

At the request of the White House, the Office of the Director of National Intelligence (ODNI) began exploring use cases for large language models (LLMs) within the Intelligence Community (IC). As part of this effort, ODNI sponsored the Mayflower Project at Carnegie Mellon University’s Software Engineering Institute (SEI) from May 2023 through September 2023. The Mayflower Project attempted to answer the following questions:
How might the IC set up a baseline, stand-alone LLM? How might the IC customize LLMs for specific intelligence use cases? How might the IC evaluate the trustworthiness of LLMs across use cases? In this SEI Podcast, Shannon Gallagher, AI engineering team lead, and Rachel Dzombak, special advisor to the director of the SEI’s AI Division, discuss the findings and recommendations from the Mayflower Project and provides additional background information about LLMs and how they can be engineered for national security use cases.

34 min

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