Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russia strikes children's hospital in Ukraine as Kyiv hits energy sites A Russian strike on a children's hospital in southern Ukraine has wounded at least nine people, authorities have said, shortly after Kyiv targeted Russian energy sites with drones. Four children were injured in Russia's strike on the medical facility in Kherson on Wednesday, which Ukrainian President Volodymyr Zelenskyy described as a "deliberate" attack that shows Moscow does not want peace.

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 attend AIES and ECAI, learn about policy design for two-sided platforms, discover how to balance speed and physical laws in atomic-scale simulations, and find out more about machine learning for chip design. October has been a busy month on the conference front. Over in Madrid, researchers gathered for the conference on Artificial Intelligence, Ethics, and Society (AIES) . The event featured two keynote talks, panel discussions and poster sessions.

Character.ai to ban teens from talking to its AI chatbots The platform, founded in 2021, is used by millions to talk to chatbots powered by artificial intelligence (AI). But it is facing several lawsuits in the US from parents, including one over the death of a teenager, with some branding it a clear and present danger to young people. Online safety campaigners have welcomed the move but said the feature should never have been available to children in the first place. Character.ai said it was making the changes after reports and feedback from regulators, safety experts, and parents, which have highlighted concerns about its chatbots' interactions with teens. Experts have previously warned the potential for AI chatbots to make things up, be overly-encouraging, and feign empathy can pose risks to young and vulnerable people.

Thousands of tourists warned they'll be'stranded for weeks' in the Caribbean as monster Hurricane Melissa carves path of destruction German activist dubbed'anti-Greta' seeks asylum in US with support of Elon Musk It's an extraordinary power grab that will leave Harry and Meghan quaking... but Diana predicted it all along: MAUREEN CALLAHAN Zohran Mamdani's deep family ties to George Soros revealed: TOM LEONARD unravels years-long web of finances and scheming that leads (wouldn't you guess it!) to Obama Netanyahu orders'powerful strikes in Gaza' and'kills nine' after accusing Hamas of violating ceasefire terms following'faked' return of hostage remains Taylor Swift is HIDING: Insiders spill on secretive behavior at NFL games... and why she's adamant about new life in the shadows Baseball fans go wild for the'most beautiful woman on the planet' singing national anthem at the World Series Sydney Sweeney sparks liberal meltdown with shock appearance on Fox's World Series coverage ...

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Russia launched 396 attacks on 15 settlements in Ukraine's southern Zaporizhia region, killing one person and injuring three others, Governor Ivan Fedorov said on Tuesday. Russian forces also launched drone attacks, air strikes and artillery shelling across Ukraine's Kherson region, killing one person and wounding six, the head of the Kherson Regional Military Administration, Oleksandr Prokudin, said on Tuesday.

The AI job cuts are here - or are they? Amazon's move this week to slash thousands of corporate jobs fed into a longstanding anxiety: that Artificial Intelligence is starting to replace workers. The tech giant joined a growing list of companies in the US that have pointed to AI technology as a reason behind layoffs. But some question whether AI is fully to blame - and have voiced scepticism that recent high-profile layoffs are a telling sign of the technology's effect on employment. Chegg, the online education firm, cited the new realities of AI as it announced a 45% reduction in workforce on Monday.

We study the robust geometric median problem in Euclidean space $\mathbb{R}^d$, with a focus on coreset construction.A coreset is a compact summary of a dataset $P$ of size $n$ that approximates the robust cost for all centers $c$ within a multiplicative error $\varepsilon$. Given an outlier count $m$, we construct a coreset of size $\tilde{O}(\varepsilon^{-2} \cdot \min\{\varepsilon^{-2}, d\})$ when $n \geq 4m$, eliminating the $O(m)$ dependency present in prior work [Huang et al., 2022 & 2023]. For the special case of $d = 1$, we achieve an optimal coreset size of $\tildeΘ(\varepsilon^{-1/2} + \frac{m}{n} \varepsilon^{-1})$, revealing a clear separation from the vanilla case studied in [Huang et al., 2023; Afshani and Chris, 2024]. Our results further extend to robust $(k,z)$-clustering in various metric spaces, eliminating the $m$-dependence under mild data assumptions. The key technical contribution is a novel non-component-wise error analysis, enabling substantial reduction of outlier influence, unlike prior methods that retain them.Empirically, our algorithms consistently outperform existing baselines in terms of size-accuracy tradeoffs and runtime, even when data assumptions are violated across a wide range of datasets.

To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by 335 researchers from 65 countries around the world. The 116 language varieties in Global PIQA cover five continents, 14 language families, and 23 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (up to a 37% accuracy gap, despite random chance at 50%). Open models generally perform worse than proprietary models. Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.

Understanding people's preferences is crucial for urban planning, yet current approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. We conducted a largescale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics -- including gender, age, income, education, race and ethnicity, and personality traits -- shape perceptions among 1,000 participants with balanced demographics from five countries and 45 nationalities. This dataset, Street Perception Evaluation Considering Socioeconomics (SPECS), reveals demographic- and personality-based differences across six traditional indicators -- safe, lively, wealthy, beautiful, boring, depressing -- and four new ones -- live nearby, walk, cycle, green. Location-based sentiments further shape these preferences. Machine learning models trained on existing global datasets tend to overestimate positive indicators and underestimate negative ones compared to human responses, underscoring the need for local context. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.

Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on large-scale real-world data such as electronic health records (EHRs) is critical, as clinical decisions are directly informed by these sources, yet current evaluations remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world clinical data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. It covers eight major task types spanning the entire continuum of patient care across six clinical stages and 20 representative applications, including triage and referral, consultation, information extraction, diagnosis, prognosis, and billing coding, and involves 14 clinical specialties. We systematically evaluated 95 LLMs (including DeepSeek-R1, GPT-4o, Gemini series, and Qwen3 series) under various inference strategies. Our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. The BRIDGE leaderboard: https://huggingface.co/spaces/YLab-Open/BRIDGE-Medical-Leaderboard