Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

Researchers have developed a new machine-learning-assisted approach to optimize micro-electro-discharge machining (µ-EDM) of ...

In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for clean-energy reactions are screened, identified, and validated across ...

The growth and impact of artificial intelligence are limited by the power and energy that it takes to train machine learning ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

What if cattle were selected not only for their productivity, but also for their resistance to disease? A study conducted by ...

Google and Microsoft's new WebMCP standard lets websites expose callable tools to AI agents through the browser — replacing ...

You don’t have to go it alone when searching for and maintaining the perfect batch. Through machine learning and AI-based batch control, the arduous search becomes easier to realize.

Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized ...

By transforming movement into data, Timothy Dunn is reshaping how scientists can study behavior and the brain.

AI 2.0 is different and challenges that belief as larger models are proving far less valuable in practice. Rather than ...