Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty. Researchers have developed a lightweight machine learning framework that ...

Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The ...

Large language models (LLMs) can suggest hypotheses, write code and draft papers, and AI agents are automating parts of the research process. Although this can accelerate science, it also makes it ...

As AI models migrate from secure data centers to exposed edge devices, a new threat vector has emerged: model theft. Popat identified this vulnerability early, pioneering a novel defense mechanism ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

Some cybersecurity researchers say it’s too early to worry about AI-orchestrated cyberattacks. Others say it could already be ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

This integration addresses the fundamental barriers that have historically limited formal verification adoption: complexity ...

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...

GenAI and predictive AI battle for resources, but even as the overwhelming attention focuses on genAI, enterprises are still ...

Abstract: Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires ...

The future is being coded now line by line, doctrine by doctrine. If speed outruns ethics and automation eclipses ...