Autonomous systems are designed for repetition. They are good in the situations where patterns can be memorized, charted, and anticipated with a high level of certainty. However, real-world driving is filled with edge cases, which do not scale well to datasets. Even a sophisticated system can be thrown off by a plastic bag floating along […]
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 warnings doctors have largely overlooked.
AI tools designed to diagnose cancer from tissue samples are quietly learning more than just disease patterns. New research shows these systems can infer patient demographics from pathology slides, leading to biased results for certain groups. The bias stems from how the models are trained and the data they see, not just from missing samples. Researchers also demonstrated a way to significantly reduce these disparities.
Researchers have built a fully implantable device that sends light-based messages directly to the brain. Mice learned to interpret these artificial patterns as meaningful signals, even without touch, sight, or sound. The system uses up to 64 micro-LEDs to create complex neural patterns that resemble natural sensory activity. It could pave the way for next-generation prosthetics and new therapies.
AI-powered analysis of routine blood tests can reveal hidden patterns that predict recovery and survival after spinal cord injuries. This breakthrough could make life-saving predictions affordable and accessible in hospitals worldwide.