Unpacking Open Source Artificial Intelligence: Toward a Framework for Openness in Foundation Models
An article from Communications of the ACM discusses the need for a framework to define openness in open source AI, particularly for foundation models. It highlights the challenges in applying traditional open source definitions to AI due to data, model, and system complexities.
The article 'Unpacking Open Source Artificial Intelligence: Toward a Framework for Openness in Foundation Models' published in Communications of the ACM explores the concept of openness in open source artificial intelligence, focusing on foundation models. It argues that the traditional definition of open source, which works well for software, is insufficient for AI because of the involvement of data, model architectures, training processes, and deployment systems. The authors propose a multi-dimensional framework that includes transparency of data, code, model parameters, and evaluation methods. They also discuss the trade-offs between openness and other considerations like security and misuse. The article aims to facilitate clearer discussions and policies around open source AI.
Source: GNews EN — AI —
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