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Source channel @githubtrending · Post #14834 · Jun 17

#cuda DeepEP is a special communication library for Mixture-of-Experts (MoE) models. It helps these models work faster and more efficiently by improving how data is shared between different parts of the system. DeepEP supports low-precision operations and can handle data transfer between different types of connections, like NVLink and RDMA. This makes it very useful for both training and using AI models, especially when speed is important. Users benefit from faster processing times and better performance overall. https://github.com/deepseek-ai/DeepEP

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AI & Law

@ai_and_law · Post #341 · 06/28/2024, 07:04 AM

Implementing Transparency in AI: A Step Forward Zuzanna Warso and Paul Keller from Open Future, alongside Maximilian Gahntz from Mozilla, have published a proposal to implement the EU AI Act’s training data transparency requirement for general-purpose AI (GPAI). Article 53 1(d) of the Act mandates GPAI model providers to publish detailed summaries of their training content, covering data sources and sets with narrative explanations. The proposed template emphasizes a comprehensive scope and sufficient technical detail to benefit both experts and laypeople. These summaries should list primary data collections, provide narrative explanations of other data sources, and clearly distinguish between 'data sources' (origins) and 'datasets' (processed data points). This transparency requirement aims to enhance accountability, enable research and scrutiny, and strengthen individuals' and organizations' ability to exercise their rights in the AI development process. #AI#Transparency#AIAct#DataGovernance#OpenFuture#Mozilla