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.

Computer Sciences

Forgetting may be the secret to better AI language learning

Giving AI a human-like memory limitation may actually help it learn language better. In their new proof-of-principle study, Abishek Thamma (University of Amsterdam) and Micha Heilbron (Max Planck Institute for Psycholinguistics) ...

Security

AI model predicts robberies across US cities with 86.3% accuracy

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing approaches by combining information about where crimes occur, when they happen and wider social patterns. ...

Computer Sciences

Combining lessons from ants and birds to improve AI

Combining ideas inspired by ant colonies and flocks of birds may hold the key to unlocking more effective artificial intelligence, according to a researcher at Missouri S&T. "With the way AI algorithms are currently structured, ...

Robotics

Could AI tell you where you left your keys?

An auto factory worker can remember the storage bin where she left a partly assembled component the night before and quickly return to that spot to pick it up. But robots that may work side by side with her would struggle ...

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