Search traffic to news sites has already plunged by a third in one year, according to the Reuters Institute for the Study of Journalism. Search traffic to news sites has already plunged by a third in one year, according to the Reuters Institute for the Study of Journalism. Publishers fear AI search summaries and chatbots mean'end of traffic era' Media companies expect web traffic to their sites from online searches to plummet over the next three years, as AI summaries and chatbots change the way consumers use the internet. An overwhelming majority are also planning to encourage their journalists to behave more like YouTube and TikTok content creators this year, as short-form video and audio content continues to boom. The findings are drawn from a new report from the Reuters Institute for the Study of Journalism, which included the views of 280 media leaders from 51 countries.

Malaysia and Indonesia block Musk's Grok over sexually explicit deepfakes Malaysia and Indonesia have blocked access to Elon Musk's artificial intelligence (AI) chatbot Grok over its ability to produce sexually explicit deepfakes. Grok, a tool on Musk's X platform, allows users to generate images. In recent weeks however, it has been used to edit images of real people to show them in revealing outfits. The South East Asian countries said Grok could be used to produce pornographic and non-consensual images involving women and children. They are the first in the world to ban the AI tool.

Host Nikki Glaser returned to host the Golden Globes on Sunday, delivering a scorching opening monologue that roasted many of the celebrities in the room. Just like Wicked, I'm back for a sequel, she told the A-list crowd. Just like Frankenstein, I've been pieced together by an unlicensed European surgeon. And just like the podcasters nominated tonight, I should not be allowed to be this close to Julia Roberts. The stars took her cutting comments in good humour as Glaser reflected on the last year in film and TV.

Parents of under-fives in England are to be offered official advice on how long their children should spend watching TV or looking at computer screens. The government says it will publish its first guidance on screen time for the age group in April. It comes as government research was published showing that about 98% of children under two were watching screens on a daily basis - with parents, teachers and nursery staff saying youngsters were finding it harder to hold conversations or concentrate on learning. Children with the highest screen time - around five hours a day - reportedly could say significantly fewer words than those at the other end of the scale who watched for around 44 minutes. A national working group led by Children's Commissioner for England Dame Rachel de Souza and Department for Education scientific adviser Professor Russell Viner will formulate the guidance after speaking to parents, children and early years practitioners.

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Jensen Huang, chief executive officer of Nvidia, talks about partnering with Mercedes Benz during the Nvidia Live event at CES 2026 in Las Vegas, Nevada, on Monday. Jensen Huang took the stage at the CES trade show in Las Vegas this week to make the clearest pitch yet for Nvidia's autonomous driving technology. In doing so, the chief executive officer's vision for vehicles that can drive themselves edged into the terrain of major customers like Tesla and its boss, Elon Musk. Huang's remarks sparked a widely watched -- if notably polite -- indirect multiday exchange between two of the most influential figures in technology. It also sharpened a central question about autonomous driving: Who controls the technology that will first power consumer cars that drive themselves -- and later, driverless cars known as robotaxis that are designed for ride-hailing?

Eggie, Neo, Isaac and Memo are domestic robots. But would you let them load your dishwasher? The idea of having a friendly robot butler that can do all the dull duties of running a home has existed for decades. But now, thanks to AI, it's genuinely happening and this year the first truly multi-purpose domestic bots will start to enter homes. In Silicon Valley, they're being trained at speed to fold laundry, load the dishwasher, and clean up after us.

We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our approach is based on classical excursion and crossing theorems for continuous semimartingales, which correlates number $N_\varepsilon$ of excursions of magnitude at least $\varepsilon$ with the quadratic variation $[X]_T$ of the process. The scaling law holds universally for all continuous semimartingales with finite quadratic variation, including general Ito diffusions with nonlinear or state-dependent volatility, but fails sharply for deterministic systems -- thereby providing a theoretically-certfied method of distinguishing between these dynamics, as opposed to the subjective entropy or recurrence based state of the art methods. We construct a robust data-driven diffusion test. The method compares the empirical excursion counts against the theoretical expectation. The resulting ratio $K(\varepsilon)=N_{\varepsilon}^{\mathrm{emp}}/N_{\varepsilon}^{\mathrm{theory}}$ is then summarized by a log-log slope deviation measuring the $\varepsilon^{-2}$ law that provides a classification into diffusion-like or not. We demonstrate the method on canonical stochastic systems, some periodic and chaotic maps and systems with additive white noise, as well as the stochastic Duffing system. The approach is nonparametric, model-free, and relies only on the universal small-scale structure of continuous semimartingales.

Modern engineering and scientific workflows frequently require simultaneous prediction across related tasks and fidelity levels [1-6]. In such contexts, some outputs are scarce and expensive to obtain, while others are cheaper and more abundant. Multi-task Gaussian processes (MTGPs), also known as multi-output Gaussian processes, offer a principled Bayesian framework to exploit inter-task correlations, enabling knowledge sharing that improves predictive accuracy and reduces the demand for large high-fidelity datasets [7-9]. Over decades of development, MTGPs have been applied across diverse domains, including time series forecasting, multitask optimization, and multifidelity classification, demonstrating their broad utility wherever data cost asymmetries and cross-task dependencies are present [10-16]. The central motivation for MTGPs is to leverage dependencies among related tasks to enhance predictive quality when high-fidelity information is limited [17]. For example, predicting an airfoil's lift coefficient from limited, expensive high-fidelity computational fluid dynamics (CFD) simulations can benefit from correlating with sufficient low-fidelity simulations [3]. Recent work in joint multi-objective and multifidelity optimization has also utilized MT - GPs to balance exploration and exploitation across tasks, improving predictive performance and decision-making by explicitly modeling relationships among outputs and fidelities [12].

As machine learning models become increasingly embedded in societal infrastructure, auditing them for bias is of growing importance. However, in real-world deployments, auditing is complicated by the fact that model owners may adaptively update their models in response to changing environments, such as financial markets. These updates can alter the underlying model class while preserving certain properties of interest, raising fundamental questions about what can be reliably audited under such shifts. In this work, we study group fairness auditing under arbitrary updates. We consider general shifts that modify the pre-audit model class while maintaining invariance of the audited property. Our goals are two-fold: (i) to characterize the information complexity of allowable updates, by identifying which strategic changes preserve the property under audit; and (ii) to efficiently estimate auditing properties, such as group fairness, using a minimal number of labeled samples. We propose a generic framework for PAC auditing based on an Empirical Property Optimization (EPO) oracle. For statistical parity, we establish distribution-free auditing bounds characterized by the SP dimension, a novel combinatorial measure that captures the complexity of admissible strategic updates. Finally, we demonstrate that our framework naturally extends to other auditing objectives, including prediction error and robust risk.