Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. Have a Meta business account? You're consenting to AI scraping now, legal experts say Nina Raemont is ZDNET's Health and Wearables Editor based in New York City. Meta Business Suite's new terms allow web-crawling on publication sites, legal experts say. The change goes into effect on Oct. 30.

As AI takes on more autonomous roles in industrial settings, organizations need new approaches to data, governance, and human oversight, says Arti Garg, chief technologist at AVEVA. Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure. That makes responsible deployment central to the next wave of industrial automation. "How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?" asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain. One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources.

Trump has touted a recent Census report. WIRED found grave flaws in its analysis and the process behind it, and confirmed the identity of several of its authors--among them a one-time DOGE affiliate. A report published by the US Census Bureau last month purported to uncover evidence backing up President Donald Trump's baseless claims that noncitizens voting cost him the 2020 election . It was the latest salvo in Trump's broader assault on the safety of US elections. "I WON THE ELECTION," Trump quickly declared on Truth Social after the publication of the report.

Water cyberattacks from Iran, China, and Russia target America's critical infrastructure. AI detection and anomaly detection tools could help defend aging water utility systems.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we find out about time-series anomaly detection, delve into music generation, honour award winners, and catch up on the action from the RoboCup humanoid soccer league. We caught up with Thi Kieu Khanh Ho to find out more about her work on time-series anomaly detection, what inspired her to study AI, and what she plans to work on next. This interview is part of our series featuring the AAAI Doctoral Consortium participants. In the latest in our series of IJCAI interviews, AIhub ambassador Liliane-Caroline Demers spoke to François Pachet to find out more about his work on music generation with AI.

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Thi Kieu Khanh Ho who is studying time-series anomaly detection. We found out more about her research, and what inspired her to study AI, and what she plans to work on next. Tell us a bit about your PhD -- where are you studying, and what is the topic of your research? I am doing my PhD at McGill University and Mila - Québec AI Institute, in the Department of Electrical and Computer Engineering, supervised by Professor Narges Armanfard. My research focuses on time-series anomaly detection, the problem of teaching AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples.

In such settings, the inverse scattering transform (IST) of Ablowitz, Kaup, Newell, and Segur [2] has enjoyed a rich and successful history, and is now the standard theoretical framework for deriving reduced-order evolution equations for soliton dynamics. Although these derivations are traditionally of an analytical - rather than data-driven - nature, recent work has employed the IST formalism as a tool for experimental data analysis, using the technique to analyze soliton content from empirical measurements [8, 15, 24]. Moreover, recent approaches using alternative parameterization techniques have demonstrated that the learning of reduced-order, interpretable equations of motion for solitons is tenable in a data-driven setting [6, 26, 27]. Despite the success of this recent work, however, little effort has been devoted to developing a data-driven modeling approach based on the IST itself, most likely due to the fact that the framework is fundamentally problem-specific. In this paper, we address the question of whether effective soliton dynamics can be inferred directly from observed scattering data (as opposed to being derived or approximated analytically).

The hosting platform wants sites to have more control over how AI companies use their content. Cloudflare has announced plans to automatically block mixed-use web crawlers that index websites for search engines and act as AI agents and trainers at the same time. The company previously offered its customers the optional ability to prevent crawlers from scraping their sites for AI chatbots, but now Cloudflare's stance is becoming more defensive by default. Now that the majority of traffic on the Internet is non-human, we must go further and act faster so that a sustainable ecosystem can emerge, Matthew Prince, Cloudflare's CEO and co-founder shared in a statement. Cloudflare's new tools and partnerships give website owners increased visibility and commercial opportunities and benefit AI companies that have bots with clear and transparent intent.

Multi-task regression aims at jointly solving multiple regression problems, called tasks. Compared to solving each task separately, better performances can be achieved as long as the tasks are sufficiently related. Full-conformal prediction is a framework that formulates a data-dependent prediction-region containing the unknown output-vector at any prescribed confidence level. However, explicit computation of this prediction-region is intractable in general since it requires training infinitely many predictors. The present work focuses on multi-task regression in a Reproducing Kernel Hilbert Space (RKHS) of vector-valued functions. This computational issue is addressed by designing an approximating predictionregion containing the full-conformal one. This construction is carried out in two scenarios: piq when the inter-task covariance-matrix is known, and piiq when this matrix is estimated. In terms of volume, the tightness of this approximation is assessed theoretically by means of an upper-bound in the first scenario. It is also empirically proved to improve upon the split-conformal prediction on synthetic data in both scenarios.

We study policy optimization for online episodic tabular Markov decision processes with unknown transition kernels, aiming for best-of-both-worlds guarantees together with data-dependent regret bounds. Recent work (Dann et al., 2023; Li et al., 2026) has shown that policy optimization can adapt to both adversarial and stochastic losses with first-order, second-order, and path-length bounds, but only under known transitions, leaving open whether such data-dependent guarantees are achievable by policy optimization when the transition kernel is unknown. We resolve this by developing a new algorithm based on optimistic follow-the-regularized-leader that attains these guarantees under unknown transitions. The key ingredient is a new design of optimistic $Q$-function estimators together with a data-dependent transition bonus that controls estimator bias through the loss-prediction error. Our analysis further identifies an unavoidable transition-dependent complexity term that captures the intrinsic cost of estimating the transition kernel. As a result, we obtain first-order, second-order, and path-length bounds with the transition-dependent complexity term while simultaneously achieving gap-dependent $\mathrm{polylog}(T)$ regret in the stochastic regime.