Page 12: Research news on Machine learning methodologies

Machine learning methodologies encompass algorithmic frameworks and architectures for training, optimizing, and deploying models such as neural networks, transformers, diffusion models, and reinforcement learning agents. Work in this area develops new training objectives, curriculum schemes, speculative and efficient decoding, pruning and communication-reduction strategies, and biologically inspired or physics-informed architectures. The domain also includes safety preservation, unlearning, scaling laws, and specialized methods for vision, language, control, and scientific computing, aiming to improve performance, efficiency, robustness, and controllability of complex AI systems.

Engineering

AI model could speed up and improve infrastructure crack detection

Every year, the spring freeze-thaw cycle leads to significant structural damage to critical infrastructure. Visible cracks in roads, bridges and buildings are the first signs and, if left undetected, can present serious dangers ...

Robotics

Closing the gap between animal movement and robotic control

Animals move with a level of precision and adaptability that robots struggle to match. In Carnegie Mellon University's Department of Mechanical Engineering, researchers are developing a new AI-driven approach to uncover how ...

Machine learning & AI

AI galaxy hunters could be adding to the global GPU crunch

NASA announced that it will launch the Nancy Grace Roman space telescope into orbit in September 2026, eight months ahead of schedule. The new space telescope is expected to deliver 20,000 terabytes of data to astronomers ...

Computer Sciences

A simple physics-inspired model sheds light on how AI learns

Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a "black box." To better understand how these systems ...

page 12 from 40