AI
Researchers have built a large, practical test for AI tools that help with data work. The results suggest these systems handle routine, well-structured tasks fairly well, but still stumble when the data are messy or when images and text must be understood together. This matters because many organisations are starting
AI
AI agents usually follow a slow cycle: generate an idea, execute it, then check the results. This study shows a faster path: teach agents to predict outcomes first, then verify only the most promising options. * They build “execution priors” (inspired by world models) so runtime checks can be replaced by
AI
Extreme weather is getting worse, but understanding the physical “why” behind each event still takes weeks of expert detective work. Forecasting AI has improved, yet diagnostic reasoning — explaining causes — has lagged. Enter EWE (Extreme Weather Expert), a new AI agent that acts like a meteorologist-in-the-loop. EWE plans analyses, reasons in
AI
Extreme weather is hitting harder and more often, but diagnosing the “why” behind each event is slow and expert-heavy. Meet EWE (Extreme Weather Expert): an AI agent that acts like a weather detective. * Thinks like experts: Plans analyses, reasons step-by-step, and uses a meteorology toolkit. * Works end-to-end: Turns raw atmospheric
RecommenderSystems
Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations. Dynamic-K flips the script. Instead of