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 ...

Abstract: Accurate motion control in the face of disturbances within complex environments remains a major challenge in robotics. Classical model-based approaches often struggle with nonlinearities and ...

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

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

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

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

Companies ranging from OpenAI, Meta, Microsoft, and Google to smaller firms and startups are looking for high-quality AI ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

In 1930, a young physicist named Carl D. Anderson was tasked by his mentor with measuring the energies of cosmic rays—particles arriving at high speed from outer space.

That trend is evidenced by rapidly growing interest in hiring AI consultants and strategists. That role, along with four ...

Abstract: Despite the wide variety of applications and use cases that can be solved with the help of machine learning algorithms, researchers have yet to develop a general artificial intelligence ...