Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...

Discovering and characterizing new materials is important for unlocking advances in fields like clean energy, advanced ...

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of ...

For centuries, humans looked to seers and astrologers to determine fate. Today, we look to algorithms, and the loss of agency ...

Engineers at NIMS Develop a System That Captures All the Elements of Trial and Error in Material Design, Enabling Reliable ...

Scientists have found a way to make AI much better at predicting complex, chaotic systems by tapping into the unique power of ...

At HRS 2026, Dr. Song Zuo presented evidence that AI can detect atrial fibrillation with over 90% sensitivity, ...

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center ...

Researchers have shown that blending quantum computing with AI can dramatically improve predictions of complex, chaotic ...

Depending on who you listen to, AI will cause widespread disruption and unemployment, especially at starting and lower middle ...

Harnessing heat generated by a device itself, microscopic silicon structures could lead to more energy-efficient thermal ...

Neuroscientist Vivienne Ming argues in her new book that the biggest risk of artificial intelligence is people using it too ...