A new tomato-picking robot is learning to think before it acts. Instead of simply identifying ripe fruit, it predicts how easy each tomato will be to harvest and adjusts its approach accordingly. This smarter strategy boosted success rates to 81%, with the robot even switching angles when needed. The breakthrough could pave the way for farms where robots and humans work side by side.
Choosing the right method for multimodal AI—systems that combine text, images, and more—has long been trial and error. Emory physicists created a unifying mathematical framework that shows many AI techniques rely on the same core idea: compress data while preserving what’s most predictive. Their “control knob” approach helps researchers design better algorithms, use less data, and avoid wasted computing power. The team believes it could pave the way for more accurate, efficient, and environmentally friendly AI.
Researchers tested whether generative AI could handle complex medical datasets as well as human experts. In some cases, the AI matched or outperformed teams that had spent months building prediction models. By generating usable analytical code from precise prompts, the systems dramatically reduced the time needed to process health data. The findings hint at a future where AI helps scientists move faster from data to discovery.
Dinosaur footprints have always been mysterious, but a new AI app is cracking their secrets. DinoTracker analyzes photos of fossil tracks and predicts which dinosaur made them, with accuracy rivaling human experts. Along the way, it uncovered footprints that look strikingly bird-like—dating back more than 200 million years. That discovery could push the origin of birds much deeper into prehistory.
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.
Spanish researchers have created a powerful new open-source tool that helps uncover the hidden genetic networks driving cancer. Called RNACOREX, the software can analyze thousands of molecular interactions at once, revealing how genes communicate inside tumors and how those signals relate to patient survival. Tested across 13 different cancer types using international data, the tool matches the predictive power of advanced AI systems—while offering something rare in modern analytics: clear, interpretable explanations that help scientists understand why tumors behave the way they do.
A newly developed AI can predict which diseases specific genetic mutations are likely to cause, not just whether they are harmful. The breakthrough could speed up diagnoses and open new paths for personalized treatment.
Researchers have created a prediction method that comes startlingly close to real-world results. It works by aiming for strong alignment with actual values rather than simply reducing mistakes. Tests on medical and health data showed it often outperforms classic approaches. The discovery could reshape how scientists make reliable forecasts.
Researchers at the University of Surrey developed an AI that predicts what a person’s knee X-ray will look like in a year, helping track osteoarthritis progression. The tool provides both a visual forecast and a risk score, offering doctors and patients a clearer understanding of the disease. Faster and more interpretable than earlier systems, it could soon expand to predict other conditions like lung or heart disease.
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.
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.
A new AI model from NYU Abu Dhabi predicts solar wind days in advance with far greater accuracy than existing methods. By analyzing ultraviolet solar images, it could help protect satellites, navigation systems, and power grids from disruptive space weather events.
Scientists at Mount Sinai have created an artificial intelligence system that can predict how likely rare genetic mutations are to actually cause disease. By combining machine learning with millions of electronic health records and routine lab tests like cholesterol or kidney function, the system produces "ML penetrance" scores that place genetic risk on a spectrum rather than a simple yes/no. Some variants once thought dangerous showed little real-world impact, while others previously labeled uncertain revealed strong disease links.