This new framework aims to finally set the standard for open-source AI models
ZDNETOpen-source and artificial intelligence (AI) developers and leaders agree that open-source AI is important. Despite the best efforts of the Open Source Initiative (OSI) to create an open-source AI definition (OSAID), there is still much disagreement on what should and shouldn’t be included in an OSAID. Springing from this disagreement, the newly formed Open Source Alliance (OSA) has released its take on OSAID: the Open Weight Definition (OWD).The OWD is a new framework that balances closed and open-source AI integrity. The framework is designed, its creators say, to address the complexities and challenges posed by the rapid development of AI technology. It aims to provide a clear standard for what constitutes “open source” in AI models, particularly large language models (LLMs).Also: DeepSeek’s new open-source AI model can outperform o1 for a fraction of the costWeights are fundamental components in AI. Based on the raw data, weights are the numerical values associated with the connections between nodes across different layers of an AI program. These values are determined during the machine learning training process. Specifically, the OWD includes:Model Weights Accessibility: The definition emphasizes making model weights available to developers and researchers.Dataset Information: While not requiring full access to training data, the definition stresses the need for detailed information about dataset contents and collection methods.Architecture Transparency: The framework encourages disclosure of model architecture information to facilitate improvements and modificationsAmanda Brock, OpenUK’s CEO, said she supports the OWD: “The Alliance is being driven to broaden the engagement across multiple organizations currently competing to ensure better global collaboration. This first step of sharing an approach to defining open weights is in line with the disaggregation of AI and defining the level of openness of the disaggregated but critical component, whether that be data, weight, or model. … It certainly seems to be more practical and workable than a small group creating a definition that isn’t fit for purpose.” More