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Book Cover
E-book
Author Culotta, Aron, author

Title Use open source for safer generative AI experiments / Aron Culotta, Nicholas Mattei
Edition [First edition]
Published [Cambridge, Massachusetts] : MIT Sloan Management Review, 2023

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Description 1 online resource (5 pages)
Summary The public availability of generative AI models, particularly large language models (LLMs), has led many employees to experiment with new use cases, but it also put some organizational data at risk in the process. The authors explain how the burgeoning open-source AI movement is providing alternatives for companies that want to pursue applications of LLMs but maintain control of their data assets. They also suggest resources for managers developing guardrails for safe and responsible AI development
Notes Reprint #65221
Subject Artificial intelligence -- Industrial applications
Open source software -- Industrial applications
Artificial intelligence -- Industrial applications.
Form Electronic book
Author Mattei, Nicholas, author.
Other Titles Use open source for safer generative artificial intelligence experiments