ChatGPT's dangerous dietary recommendation led to a man replacing table salt with toxic sodium bromide for three months, causing severe poisoning and hospitalization.

YouTube is one of the most popular online platforms in the U.S. among all age groups. But not all content on the video-sharing site is appropriate for all ages. While the platform, like most, has restrictions on certain content, such as violence and nudity, for users under 18, these safeguards have in the past been easy for young users to circumvent by entering an older birthdate on their account. But now, the company is rolling out an artificial intelligence-powered tool to estimate a user's age based on their activity on the platform "and then use that signal, regardless of the birthday in the account, to deliver our age-appropriate product experiences and protection," said James Beser, director of product management at YouTube Youth, in blog post last month. The technology, according to Beser, has been used in other markets "for some time" and will begin being tested in the U.S. on Wednesday before a wider rollout.

AI risks entrenching racism and sexism in Australia, the human rights commissioner has warned, amid internal Labor debate about how to respond to the emerging technology. Lorraine Finlay says the pursuit of productivity gains from AI should not come at the expense of discrimination if the technology is not properly regulated. Finlay's comments follow Labor senator Michelle Ananda-Rajah breaking ranks to call for all Australian data to be "freed" to tech companies to prevent AI perpetuating overseas biases and reflect Australian life and culture. Ananda-Rajah is opposed to a dedicated AI act but believes content creators should be paid for their work. Media and arts groups have warned of "rampant theft" of intellectual property if big tech companies can take their content to train AI models.

It is the richest company in the world, with a market value of 4tn. But while you may know the names of other extraordinarily rich companies – such as Apple or Google – you may never have heard of Nvidia. Created by Jensen Huang, a Taiwanese American, Nvidia is a microchip company with just 40,000 employees. Its products can be found in the games we play and ChatGPT. So how did Huang do it – and will he be able to see off Donald Trump's tariff regime and navigate the difficulties of doing business during Trump's trade war with China? Journalist Tae Kim explains Jensen's rise through the tech ranks and tells Helen Pidd where he is likely to take the company next.

Big Brother Watch is bringing a legal challenge against the Met Police's use of the technology, alongside Shaun Thompson, who was wrongly identified by an LFR camera. Rebecca Vincent, interim director of Big Brother Watch, said: "Police have interpreted the absence of any legislative basis authorising the use of this intrusive technology as carte blanche to continue to roll it out unfettered, despite the fact that a crucial judicial review on the matter is pending. "The Home Office must scrap its plans to roll out further live facial recognition capacity until robust legislative safeguards are established." Charlie Whelton, policy and campaigns officer at Liberty, said: "It's welcome news that the government will finally develop a statutory framework on the use of facial recognition, but this should be in place before more facial recognition technology is rolled out. "There's no reasonable excuse to be putting even more cameras on our streets before the public have had their say and legislation is brought in to protect all of us." The government says officers using the LFR vans will need to follow the College of Policing's guidance on the technology and the Surveillance Camera Code of Practice.

An important challenge in scientific machine learning is to develop methods that can exploit and maximize the amount of learning possible from scarce data [1-4]. The need for such methods arises often in science and engineering, especially in the case of computational fluid dynamics (CFD), since expensive-to-evaluate high-fidelity (HF) models make many-query problems such as uncertainty quantification, risk analysis, optimization, and optimization under uncertainty computationally prohibitive [5]. Surrogate models that approximate the solutions to HF models can facilitate the design and analysis process; however, lack of sufficient HF data in tandem with high-dimensional quantities of interest adversely affect surrogate model accuracy. We propose multifidelity (MF) linear regression methods that leverage abundant low-cost, lower-fidelity (LF) data alongside limited HF data to construct linear regression models. These models operate within a reduced-dimensional subspace, obtained through the principal component analysis (PCA), to effectively handle both training data scarcity and the high dimensionality (on the order of tens of thousands of quantities of interest) inherent in our problem setting. Linear regression has been widely utilized as a surrogate modeling approach in aerospace applications due to its simplicity and interpretability. We note that linear regression encompasses a broad class of models that are linear in their parameters but can include features that are arbitrarily nonlinear functions of the input variables [6].

The rapid emergence of large language models (LLMs) has raised urgent questions across the modern workforce about this new technology's strengths, weaknesses, and capabilities. For privacy professionals, the question is whether these AI systems can provide reliable support on regulatory compliance, privacy program management, and AI governance. In this study, we evaluate ten leading open and closed LLMs, including models from OpenAI, Anthropic, Google DeepMind, Meta, and DeepSeek, by benchmarking their performance on industry-standard certification exams: CIPP/US, CIPM, CIPT, and AIGP from the International Association of Privacy Professionals (IAPP). Each model was tested using official sample exams in a closed-book setting and compared to IAPP's passing thresholds. Our findings show that several frontier models such as Gemini 2.5 Pro and OpenAI's GPT-5 consistently achieve scores exceeding the standards for professional human certification - demonstrating substantial expertise in privacy law, technical controls, and AI governance. The results highlight both the strengths and domain-specific gaps of current LLMs and offer practical insights for privacy officers, compliance leads, and technologists assessing the readiness of AI tools for high-stakes data governance roles. This paper provides an overview for professionals navigating the intersection of AI advancement and regulatory risk and establishes a machine benchmark based on human-centric evaluations.

Natural disasters such as hurricanes and wildfires increasingly introduce unusual disturbance on economic activities, which are especially likely to reshape commercial land use pattern given their sensitive to customer visitation. However, current modeling approaches are limited in capturing such complex interplay between human activities and commercial land use change under and following disturbances. Such interactions have been more effectively captured in current resilient urban planning theories. This study designs and calibrates a Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change (Urban-STA4CLC) to predict both the yearly decline and expansion of commercial land use at census block level under cumulative impact of disasters on human activities over two years. Guided by urban theories, Urban-STA4CLC integrates both spatial and temporal attention mechanisms with three theory-informed modules. Resilience theory guides a disaster-aware temporal attention module that captures visitation dynamics. Spatial economic theory informs a multi-relational spatial attention module for inter-block representation. Diffusion theory contributes a regularization term that constrains land use transitions. The model performs significantly better than non-theoretical baselines in predicting commercial land use change under the scenario of recurrent hurricanes, with around 19% improvement in F1 score (0.8763). The effectiveness of the theory-guided modules was further validated through ablation studies. The research demonstrates that embedding urban theory into commercial land use modeling models may substantially enhance the capacity to capture its gains and losses. These advances in commercial land use modeling contribute to land use research that accounts for cumulative impacts of recurrent disasters and shifts in economic activity patterns.

We introduce DiffPhysCam, a differentiable camera simulator designed to support robotics and embodied AI applications by enabling gradient-based optimization in visual perception pipelines. Generating synthetic images that closely mimic those from real cameras is essential for training visual models and enabling end-to-end visuomotor learning. Moreover, differentiable rendering allows inverse reconstruction of real-world scenes as digital twins, facilitating simulation-based robotics training. However, existing virtual cameras offer limited control over intrinsic settings, poorly capture optical artifacts, and lack tunable calibration parameters -- hindering sim-to-real transfer. DiffPhysCam addresses these limitations through a multi-stage pipeline that provides fine-grained control over camera settings, models key optical effects such as defocus blur, and supports calibration with real-world data. It enables both forward rendering for image synthesis and inverse rendering for 3D scene reconstruction, including mesh and material texture optimization. We show that DiffPhysCam enhances robotic perception performance in synthetic image tasks. As an illustrative example, we create a digital twin of a real-world scene using inverse rendering, simulate it in a multi-physics environment, and demonstrate navigation of an autonomous ground vehicle using images generated by DiffPhysCam.

We study a temporal voting model where voters have dynamic preferences over a set of public chores -- projects that benefit society, but impose individual costs on those affected by their implementation. We investigate the computational complexity of optimizing utilitarian and egalitarian welfare. Our results show that while optimizing the former is computationally straightforward, minimizing the latter is computationally intractable, even in very restricted cases. Nevertheless, we identify several settings where this problem can be solved efficiently, either exactly or by an approximation algorithm. We also examine the effects of enforcing temporal fairness and its impact on social welfare, and analyze the competitive ratio of online algorithms. We then explore the strategic behavior of agents, providing insights into potential malfeasance in such decision-making environments. Finally, we discuss a range of fairness measures and their suitability for our setting.