Page 14: 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.

Energy & Green Tech

Supercomputer ushers in new era of nuclear AI

Nuclear power is rising to meet the demand for American energy. But building new reactors or even renewing licenses of existing ones requires a tremendous amount of paperwork. Fortunately, AI is also on the rise, and paperwork ...

Computer Sciences

Making simulations more accurate than ever with deep learning

Future events such as the weather or satellite trajectories are computed in tiny time steps, so the computation must be both efficient and as accurate as possible at each step lest errors pile up. A Kobe University team has ...

Hi Tech & Innovation

Biological intelligence as the basis for new AI systems

In a new research project led by the Central Institute of Mental Health (CIMH) in Mannheim, scientists are investigating how insights into learning processes in animal brains can be used to make artificial intelligence (AI) ...

Machine learning & AI

Taming chaos in neural networks: A biologically plausible way

A new framework that causes artificial neural networks to mimic how real neural networks operate in the brain has been developed by a RIKEN neuroscientist and his collaborator. In addition to shedding light on how the brain ...

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