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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.15802 |
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| _version_ | 1866916259630678016 |
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| author | Basdevant, Adrien François, Camille Storchan, Victor Bankston, Kevin Bdeir, Ayah Behlendorf, Brian Debbah, Merouane Kapoor, Sayash LeCun, Yann Surman, Mark King-Turvey, Helen Lambert, Nathan Maffulli, Stefano Marda, Nik Shivkumar, Govind Tunney, Justine |
| author_facet | Basdevant, Adrien François, Camille Storchan, Victor Bankston, Kevin Bdeir, Ayah Behlendorf, Brian Debbah, Merouane Kapoor, Sayash LeCun, Yann Surman, Mark King-Turvey, Helen Lambert, Nathan Maffulli, Stefano Marda, Nik Shivkumar, Govind Tunney, Justine |
| contents | Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level of generality or with a narrow focus on specific technical attributes. In part, this is because defining open source for foundation models has proven tricky, given its significant differences from traditional software development. In order to inform more practical and nuanced decisions about opening AI systems, including foundation models, this paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness, and outlines how openness varies in different parts of the AI stack, both at the model and at the system level. In doing so, its authors hope to provide a common descriptive framework to deepen a nuanced and rigorous understanding of openness in AI and enable further work around definitions of openness and safety in AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15802 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence Basdevant, Adrien François, Camille Storchan, Victor Bankston, Kevin Bdeir, Ayah Behlendorf, Brian Debbah, Merouane Kapoor, Sayash LeCun, Yann Surman, Mark King-Turvey, Helen Lambert, Nathan Maffulli, Stefano Marda, Nik Shivkumar, Govind Tunney, Justine Software Engineering Artificial Intelligence Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level of generality or with a narrow focus on specific technical attributes. In part, this is because defining open source for foundation models has proven tricky, given its significant differences from traditional software development. In order to inform more practical and nuanced decisions about opening AI systems, including foundation models, this paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness, and outlines how openness varies in different parts of the AI stack, both at the model and at the system level. In doing so, its authors hope to provide a common descriptive framework to deepen a nuanced and rigorous understanding of openness in AI and enable further work around definitions of openness and safety in AI. |
| title | Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2405.15802 |