By the time you finish reading this sentence, another humanoid robot will have rolled off a production line somewhere in China. That is not hyperbole. On March 30, 2026, Shanghai-based Agibot announced it had produced its 10,000th humanoid robot – a milestone the company reached after scaling from 5,000 to 10,000 units in just three […]
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.
A wireless eye implant developed at Stanford Medicine has restored reading ability to people with advanced macular degeneration. The PRIMA chip works with smart glasses to replace lost photoreceptors using infrared light. Most trial participants regained functional vision, reading books and recognizing signs. Researchers are now developing higher-resolution versions that could eventually provide near-normal sight.
A powerful new AI tool called Diag2Diag is revolutionizing fusion research by filling in missing plasma data with synthetic yet highly detailed information. Developed by Princeton scientists and international collaborators, this system uses sensor input to predict readings other diagnostics can’t capture, especially in the crucial plasma edge region where stability determines performance. By reducing reliance on bulky hardware, it promises to make future fusion reactors more compact, affordable, and reliable.