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LLMs without hardware feedback fail to deploy, but a new iterative optimization method achieves 250x compression with less than 3.3% accuracy loss in real-world applications.
Training a foundation model on a trillion minutes of wearable sensor data unlocks surprisingly accurate predictions across a wide range of health conditions, even with limited labeled data.
Training on semantically equivalent chart renderings in Python, R, and LaTeX unlocks surprisingly effective multi-lingual chart-to-code generation from a single model.
