Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper publishing April 22 in the Cell Press ...

Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Engineers develop a system that captures all the elements of trial and error in material design, enabling reliable ...

Machine learning components are enabling advances in self-driving cars, the power grid, and robotic medicine, but what are the implications for safety? Decades of research and practice in safety ...

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 ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Why it’s important not to over-engineer. Equipped with suitable hardware, IDEs, development tools and kits, frameworks, datasets, and open-source models, engineers can develop ML/AI-enabled, ...

How are artificial intelligence and machine learning models impacting drug development and delivery? There are a few different areas where machine learning is starting to have an impact on the whole ...

High-entropy alloys (HEAs) are rewriting the rules of materials science, and machine learning is accelerating their design. By predicting phase stability and performance from large datasets, ...