Page 16: 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

AI model shortens the development time of new materials

Time-consuming testing and computer simulations are bottlenecks in the design of new materials. A thesis from the University of Gothenburg aims to develop an AI model that can efficiently determine the durability and strength ...

Hardware

Wafer-scale accelerators could redefine AI

The promise of a new type of computer chip that could reshape the future of artificial intelligence and be more environmentally friendly is explored in a technology review paper published by UC Riverside engineers in the ...

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