Better Training Data, Better AI
Within AI research and deployment, longstanding emphasis on model architectures and computational scale has begun to bring diminishing returns. As AI models become more complex and require ever more resources to achieve marginal performance gains, the community has already started shifting toward a data-centric paradigm where
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Rethinking Training Data
Machine learning (ML) models rely heavily on accessing and providing high-quality training data. Due to the long deployment timeline and domain boundaries of such models, performance will inevitably decline due to a mismatch between training data and operational conditions. This paper highlights three situations where retraining
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