Commuters walk along a street on the eve of the India AI Impact Summit 2026 in New Delhi on Sunday. New Delhi - A global artificial intelligence summit kicks off in New Delhi on Monday with big issues on the agenda, from job disruption to child safety, but some attendees warn the broad focus could diminish the chance of concrete commitments from world leaders. While frenzied demand for generative AI has turbocharged profits for many tech companies, anxiety is growing over the risks that it poses to society and the environment. Prime Minister Narendra Modi will on Monday afternoon inaugurate the five-day AI Impact Summit, which aims to declare a shared roadmap for global AI governance and collaboration. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Learning continuous-time point processes is essential to many discrete event forecasting tasks.

We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior beliefs that are a convex combination of their prior and the Bayesian posterior, where the more biased an agent is, the closer their posterior is to the prior. Since we often cannot observe the agent's beliefs directly, we take an approach inspired by information design . Specifically, we measure an agent's bias by designing a signaling scheme and observing the actions the agent takes in response to different signals, assuming that the agent maximizes their own expected utility. Our goal is to detect bias with a minimum number of signals. Our main results include a characterization of scenarios where a single signal suffices and a computationally efficient algorithm to compute optimal signaling schemes.

Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success.