Science

New research demonstrates that fine-tuned artificial intelligence models can accurately forecast corrective actions for warranty claims, paving the way for truly automated processing.

A new approach to predictive maintenance leverages self-evolving multi-agent systems to enhance reliability and minimize downtime in industrial IoT environments.

Researchers are harnessing the power of distributed learning and advanced neural networks to improve the accuracy of lung disease diagnosis while safeguarding patient data.

A new approach combines graph neural networks and model predictive control to enable real-time command of complex systems like soft robots.

New research reveals that common methods for evaluating AI safety are easily bypassed, exposing a hidden vulnerability in large language models.

A new framework leverages expert knowledge to help artificial intelligence reliably detect anomalies in complex industrial processes without requiring extensive model training.

A new approach leverages neural networks to rapidly and accurately determine the internal motions of galaxies, offering a significant speedup over conventional methods.

A new model accurately forecasts the likelihood of secondary crashes using live traffic data, offering a path toward more responsive and preventative traffic management.

A new framework leverages the power of ensemble forecasting and uncertainty quantification to identify subtle precursors to critical failures, offering a path towards proactive system maintenance.

A new approach leverages large language models to forecast extreme price fluctuations in electricity markets, offering a competitive edge when data is scarce.