@rohanpaul_ai: New Google paper shows that wearable data becomes far more useful when AI learns the person behind the signals. It's is…

New Google paper shows that wearable data becomes far more useful when AI learns the person behind the signals. It's is not another heart-rate algorithm, but a general model trained on more than one trillion minutes of sensor data from five million people. The authors propose SensorFM, a foundation model trained on more than 1 trillion minutes of unlabeled wearable data from 5 million people, so it can learn general patterns of human physiology before seeing specific health tasks. That scale changes the problem from measuring isolated events to learning patterns of lived physiology: sleep, movement, temperature, oxygen, heart rhythms, and their ordinary daily messiness. Wearables are not weak because they lack data; they are weak because most systems compress that data into crude summaries before the meaningful structure has a chance to appear. SensorFM tries to learn that structure first, then reuse it across tasks, which is why the same representation can help with cardiovascular, metabolic, mental health, sleep, lifestyle, and demographic predictions. The evidence is strongest as a scaling story: larger models trained on more data performed better, and the learned embeddings beat engineered-feature baselines on 34 of 35 prediction tasks. ---- Paper Link – arxiv. org/abs/2511.15352v3 Paper Title: "People readily follow personal advice from AI but it does not improve their well-being"