For the past two years, the Open Source Initiative (OSI) has been diligently working on crafting a definition of Open Source AI. This initiative aims to provide a clear-cut framework for the industry to identify truly open models. The OSI is nearing its goal with the release of a new draft definition expected by October.

The need for such a definition, as highlighted by the OSI in May, arises from the prevalent mislabeling of AI models as open source by various companies. A robust definition will empower developers to assess the openness of AI models accurately.

In its latest draft version 0.0.9, the OSI has improved the explanation of what constitutes Open Source models and weights, asserting that all components of a system must adhere to the open source standard to qualify as open source entirely.

Furthermore, the OSI has reached a consensus that training data should not determine the classification of an AI system. In a recent discussion, they articulated that while training data is crucial for studying AI systems and understanding inherent biases, it does not constitute the optimal form for modifying AI systems. The insights gleaned from such data prioritize learning over importance in modification processes.

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