Newsrooms and journalists across the world are adopting Generative AI (GenAI). Drawing on in-depth interviews with 23 journalists, this study identifies Bangladeshi journalists' awareness, acceptance, usage patterns, and their media organizations' stance toward GenAI. This study finds Bangladeshi journalists' high reliance on GenAI like their Western colleagues despite limited institutional support and the near absence of AI policy. Despite this contrast, concerns over GenAI's implications in journalism between the West and non-West were mostly identical. Moreover, this study contributes to the Unified Theory of Acceptance and Use of Technology (UTAUT) by proposing two changes regarding GenAI adoption among journalists in non-Western settings. First, this study identifies the non-contribution of facilitating conditions in shaping behavioral intent in GenAI adoption in non-Western contexts. Second, social influence works in a horizontal order through informal peer pressure or professional motivation in the absence of formal institutional hierarchical pressure. Voluntariness in the context of Bangladeshi journalists is underpinned by their professional compulsion. Therefore, this study contributes to understanding how contextual factors shape technology adoption trajectories in non-Western journalism.

US shutdown ends: What happens next? New Epstein emails: What do they say about Trump? Last month, tech billionaire Elon Musk launched Grokipedia, an AI-powered platform, to rival online encyclopedia Wikipedia. "Grokipedia will exceed Wikipedia by several orders of magnitude in breadth, depth and accuracy," Musk posted on X the day after his site went live on October 27. Grokipedia will exceed Wikipedia by several orders of magnitude in breadth, depth and accuracy https://t.co/Nt4M6vqEZu

Hollywood's SAG Awards announces it will change its name The Screen Actors Guild Awards, the marquee awards ceremony honouring actors, is getting a new name. Known colloquially as the SAG Awards, the awards show will now be dubbed the Actor Awards presented by Sag-Aftra, the labour union representing US film, television and radio actors. Since the beginning, our statue has been called'The Actor' and we're a show that's entirely about actors, so this new name is a perfect next step in the show's evolution, the show's executive producer said on Friday. The rebrand comes ahead of the 32nd edition of the star-studded ceremony, which is set for 1 March 2026. The award show's executive producer Jon Brockett told the BBC that the name change - which was announced at a board meeting on Friday - gives viewers in more than 190 countries an immediate understanding of who we are and what we're about - a show about actors honouring actors.

Bitcoin ransom wallet shows first'activity' after doorbell footage revealed in Nancy Guthrie case Nancy Guthrie's family say they do NOT recognize masked figure filmed night of abduction as investigators are seen searching bushes near daughter and son-in-law's home Nancy Guthrie's son-in-law disappears from public view as quirk in the law could mean his home is being searched without consent Look at Nancy Guthrie masked suspect's escape routes: Crime analyst MORGAN WRIGHT reveals terrifying new details about where she was taken Terrifying masked figure in latex gloves with gun seen on Nancy Guthrie's doorstep on morning of her kidnap Coastal paradise is now ground zero for housing bloodbath... and America faces a reckoning'Despicable' heiress, 58, whose mogul dad named VINEYARD after her broke his heart with outrageous claim... as new details of mortifying family feud are laid bare Britney Spears sells off her iconic music catalog in'landmark deal'... after fresh attack on her family Alabama woman learns fate after pushing woman to her death off CLIFF, with cops saying pair'knew each other through boyfriend' Canada PM Carney ridicules Trump with brutal taunt after president's border bridge threat Bill Gates' awful behavior towards Melinda at dinner party revealed by socialite friend, as she spills secrets of their marriage Fraser Bohm wins big in Pepperdine murder trial as judge orders prosecutors to hand over phone he refused to unlock... despite fears he could tamper with it Tiger Woods' son Charlie, 17, makes college commitment in huge next step... but snubs his dad's old team! Winter Olympics star who admitted to cheating on his girlfriend says he has not heard from her since his on-air confession and shares hope for'happy ending' Kurt Cobain's death was'homicide': Shocking new forensic investigation questions suicide ruling Bombshell secret that could DESTROY Turning Point USA: As Erika Kirk misses halftime show... whistleblowers tell all to KENNEDY Your soda habit may raise the risk of stroke... after study finds even'healthy' sugar substitute causes brain damage'Hot girls have started using AI': Influencers are turning to chatbots to add cute animals to their photos - but fans can't decide if it's cool or scary From the 90s yearbook trend to the action figure trend, several social media crazes have made use of articifical intelligence ( AI). Now, 'hot girl' influencers are jumping on the AI bandwagon. Several social media stars have started using chatbots to add cute animals to their photos, ranging from fluffy bunnies to huge horses. X user @jameygannon pointed out the trend in a now-viral post, which has been viewed over 14 million times.

Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Y et traditional surveys face persistent challenges, including fixed-question formats, high costs, limited adaptability, and difficulties ensuring cross-cultural equivalence. While recent studies explore large language models (LLMs) to simulate survey responses, most are limited to structured questions, overlook the entire survey process, and risks under-representing marginalized groups due to training data biases. We introduce AlignSurvey, the first benchmark that systematically replicates and evaluates the full social survey pipeline using LLMs. It defines four tasks aligned with key survey stages: social role modeling, semi-structured interview modeling, attitude stance modeling and survey response modeling. It also provides task-specific evaluation metrics to assess alignment fidelity, consistency, and fairness at both individual and group levels, with a focus on demographic diversity. To support AlignSurvey, we construct a multi-tiered dataset architecture: (i) the Social Foundation Corpus, a cross-national resource with 44K+ interview dialogues and 400K+ structured survey records; and (ii) a suite of Entire-Pipeline Survey Datasets, including the expert-annotated AlignSurvey-Expert (ASE) and two nationally representative surveys for cross-cultural evaluation. We release the SurveyLM family, obtained through two-stage fine-tuning of open-source LLMs, and offer reference models for evaluating domain-specific alignment. All datasets, models, and tools are available at github and huggingface to support transparent and socially responsible research.

Common knowledge is crucial for safe group coordination. In its absence, humans must rely on shared knowledge, which is inherently limited in depth and therefore prone to coordination failures, because any finite-order knowledge attribution allows for an even higher order attribution that may change what is known by whom. In three separate experiments involving 802 participants, we investigate the extent to which humans can differentiate between common knowledge and nth-order shared knowledge. We designed a two-person coordination game with imperfect information to simplify the recursive game structure and higher-order uncertainties into a relatable everyday scenario. In this game, coordination for the highest payoff requires a specific fact to be common knowledge between players. However, this fact cannot become common knowledge in the game. The fact can at most be nth-order shared knowledge for some n. Our findings reveal that even at quite shallow depths of shared knowledge (low values of n), players behave as though they possess common knowledge, and claim similar levels of certainty in their actions, despite incurring significant penalties when falsely assuming guaranteed coordination. We term this phenomenon 'the curse of shared knowledge'. It arises either from the players' inability to distinguish between higher-order shared knowledge and common knowledge, or from their implicit assumption that their co-player cannot make this distinction.

Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA). We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures. Existing methods usually employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap. To address this challenge, we propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries. EoG enables efficient evidence extraction from KGs for precise and robust reasoning, while ensuring low computational costs, scalability, and adaptability across different methods. Furthermore, we propose three graph quality evaluation metrics to analyze query-graph alignment in KGQA task, supported by theoretical validation of our optimization objectives. Extensive experiments on two KGQA benchmark datasets indicate that EoG can effectively generate high-quality KGs and achieve the state-of-the-art performance. Our code and data are available at https://github.com/zjukg/Enrich-on-Graph.

Steven Adler used to lead product safety at OpenAI. On this week's episode of, he talks about what AI users should know about their bots. When the history of AI is written, Steven Adler may just end up being its Paul Revere--or at least, one of them--when it comes to safety. Last month Adler, who spent four years in various safety roles at OpenAI, wrote a piece for The New York Times with a rather alarming title: "I Led Product Safety at OpenAI. In it, he laid out the problems OpenAI faced when it came to allowing users to have erotic conversations with chatbots while also protecting them from any impacts those interactions could have on their mental health. "Nobody wanted to be the morality police, but we lacked ways to measure and manage erotic usage carefully," he wrote. "We decided AI-powered erotica would have to wait." Adler wrote his op-ed because OpenAI CEO Sam Altman had recently announced that the company would soon allow " erotica for verified adults ."

Computational notebooks have become popular for Exploratory Data Analysis (EDA), augmented by LLM-based code generation and result interpretation. Effective LLM assistance hinges on selecting informative context -- the minimal set of cells whose code, data, or outputs suffice to answer a prompt. As notebooks grow long and messy, users can lose track of the mental model of their analysis. They thus fail to curate appropriate contexts for LLM tasks, causing frustration and tedious prompt engineering. We conducted a formative study (n=6) that surfaced challenges in LLM context selection and mental model maintenance. Therefore, we introduce NoteEx, a JupyterLab extension that provides a semantic visualization of the EDA workflow, allowing analysts to externalize their mental model, specify analysis dependencies, and enable interactive selection of task-relevant contexts for LLMs. A user study (n=12) against a baseline shows that NoteEx improved mental model retention and context selection, leading to more accurate and relevant LLM responses.

Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed socio-technical grounded theory (STGT) for data analysis to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain software development flow, improve developers' mental model, and foster entrepreneurship) and disadvantages (e.g., negative impact on developers' personality and damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as best practices on how to adopt LLMs. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.