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 has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

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 ...

Machine Learning (ML) and Artificial Intelligence (AI) have become essential technologies across industries, automating tasks at a speed and scale far beyond human capabilities. However, building ...

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

As machine learning adoption continues to expand across industries, the demand for professionals who can build, deploy, and scale production-grade ML systems is rising rapidly. In response to this ...

As part of "shift left" to incorporate security discussions earlier in the software development life cycle, organizations are beginning to look at threat modeling to identify security flaws in ...

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 ...