A new study rigorously evaluates the ability of an AI-powered weather model to anticipate high-impact events, revealing both impressive short-term accuracy and fundamental limits to long-range prediction.
A novel approach combines the strengths of traditional weather models with machine learning to deliver more accurate and reliable predictions.
A new approach leverages the power of graph neural networks and strategically perturbed input data to generate more reliable probabilistic forecasts of sea surface temperature.
Understanding why AI coding agents fail is crucial for reliable software development, and this research introduces a method for turning complex execution data into actionable insights.
A new benchmark assesses how well large language models can automate the critical task of analyzing cybersecurity incidents.
A new framework details how to reliably orchestrate complex AI tasks across distributed systems, moving beyond simple requests to dynamic, interconnected agent interactions.
A new survey reveals that concerns among AI researchers center on near-term societal impacts, challenging common narratives about existential threats.
Current AI systems prioritize avoiding liability during mental health crises, but a new approach focuses on empowering users and providing constructive assistance.
A new framework leverages artificial intelligence to help urban transportation systems adapt to the growing risks of climate change and extreme weather events.
The growing use of artificial intelligence in weather and climate prediction presents both opportunities and risks, potentially exacerbating existing global inequalities.