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Stanford researchers have developed an AI that can predict future disease risk using data from just one night of sleep. The system analyzes detailed physiological signals, looking for hidden patterns across the brain, heart, and breathing. It successfully forecast risks for conditions like cancer, dementia, and heart disease. The results suggest sleep contains early health …

Read more “Stanford’s AI spots hidden disease warnings that show up while you sleep”

Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI models. Unlike generative diffusion models, the team’s Discrete Spatial Diffusion approach honors scientific and physics principles. The team validated their model on two challenging scientific applications—subsurface rock microstructures and lithium-ion battery electrodes—with promising results.

The pivot doesn’t look out of place at CES, where Chinese electronics companies are increasingly applying their manufacturing prowess to new industries.

While the capabilities of robots have improved significantly over the past decades, they are not always able to reliably and safely move in unknown, dynamic and complex environments. To move in their surroundings, robots rely on algorithms that process data collected by sensors or cameras and plan future actions accordingly.