From battlefield communications to counter-drone defense, security must be measurable.” SEOCHO-GU, SEOUL, SOUTH KOREA, January 23, 2026 /EINPresswire.com/ — Solvit System (CEO Yeong-Goo Kim), a ...

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 ...

Robot perception and cognition often rely on the integration of information from multiple sensory modalities, such as vision, ...

MIT researchers unveil a new fine-tuning method that lets enterprises consolidate their "model zoos" into a single, continuously learning agent.

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

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

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, training on databases, and special computer chips.

Abstract: In unknown environments, achieving autonomous navigation for uncrewed aerial vehicles (UAVs) is a complex task. Ensuring that UAVs reach their destinations safely and efficiently remains a ...

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Abstract: Q-learning and double Q-learning are well-known sample-based, off-policy reinforcement learning algorithms. However, Q-learning suffers from overestimation bias, while double Q-learning ...