Page 15: Research news on Artificial intelligence

Artificial intelligence, as a technique, refers to computational methods that enable machines to perform tasks that typically require human cognitive capabilities, such as perception, reasoning, learning, and decision-making. Core AI techniques include search and optimization algorithms, symbolic reasoning and knowledge representation, probabilistic inference, and machine learning approaches such as supervised, unsupervised, and reinforcement learning. These techniques often rely on statistical modeling, function approximation, and gradient-based optimization to construct models that generalize from data. AI techniques are implemented in software frameworks, integrated into pipelines for training, validation, and deployment, and are evaluated using task-specific performance metrics and robustness assessments.

A step toward AI modeling of the whole Earth system

Modelers have demonstrated that artificial intelligence (AI) models can produce climate simulations with more efficiency than physics-based models. However, many AI models are trained on past climate data, making it difficult ...

Accelerating climate modeling at a lower cost

Scientists are increasingly turning to AI to model future changes in the climate. However, existing approaches often face a trade-off between accuracy, speed, and computational cost.

Zoo animals go wild for AI

From using moon rovers that encourage predators to hunt and forage in packs, to applying state-of-the-art algorithms to try and understand the facial expressions of Sumatran orangutans, artificial intelligence and robotics ...

Scalable AI tracks motion from single molecules to wildebeests

University of Michigan researchers have developed a tool powered by artificial intelligence that can help them examine the behavior of a single molecule out of a sea of information in the blink of an eye—or at least overnight.

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