Taming Complexity: AI-Powered Control for High-Dimensional Systems

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

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.
A new framework systematically maps the unique safety challenges posed by artificial intelligence within the growing landscape of Digital Public Goods in India.

New research demonstrates that artificial intelligence can analyze carotid ultrasound videos to detect subtle vascular damage, offering a potentially more accurate and scalable approach to cardiovascular risk assessment.
A critical review unpacks the speculative narratives surrounding artificial intelligence and their often-overlooked ideological underpinnings.