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.

A deadly conflict between bears and humans is playing out across Japan, where authorities have deployed the military to protect locals who are using drone-based alert and surveillance systems to track the bears. Since April this year, at least 13 people have been killed and more than 100 have been injured in bear attacks in the country, according to an October report by the Ministry of Environment. The ministry added that the death toll is the highest since Japan began keeping records of bear attacks in 2006. It is also home to Asiatic black bears - also known as Moon bears - which are smaller in size, weighing between 80-200kg (176-440 pounds), and are found on the mainland, which is more densely populated. Both types of bear have been involved in incidents this year, and both are dangerous to humans to varying degrees.

At least six people have been killed in a wave of Russia strikes on Kyiv, which the Ukrainian President Volodymyr Zelensky has condemned as a heinous attack. The BBC's James Landale visited the scene of one attack in eastern Kyiv where a drone rammed through a block of flats and left six people dead. Several other regions were also targeted. A drone attack on a market at Chornomorsk in the south of the country killed two people. Catherine Connolly has'never believed more' in the spirit of Ireland New Irish President Catherine Connolly says she has been given a powerful mandate to articulate a vision for a new republic.

King Charles'never understood' Meghan Markle but Queen Camilla saw through her'performance' - as royal expert reveals what really happened at Castle of Mey in 2018 Epstein lawyer ALAN DERSHOWITZ: I've seen the secret files and their damning contents. Here's the inconvenient truth they don't want you to know Gavin Newsom forced to revoke thousands of driver's licenses for illegal migrants after being'caught red-handed' Ariana Grande in crisis: Fan attack triggers'PTSD spiral' as sick new details of targeted plot are revealed... and insiders warn of'worst case scenario' years after concert bombing Michael Jackson's daughter Paris looks downcast after losing legal battle with his estate amid ongoing fight Chinese labs' race to discover the secret of immortality: After Xi and Putin were caught discussing how to cheat death, the communist nation is driving to stop ageing - with'living to 150 realistic' Amy Schumer's marriage on the BRINK as star sheds pounds and sells off homes amid'difficult time' Friends' haunting text messages to fashion designer moments before she was found dead on Hamptons yacht as owner explains why he was naked Sydney Sweeney wows in a low-cut black velvet gown as she joins glamorous Hailey Bieber, Olivia Rodrigo and Becky G on the red carpet at GQ's Men Of The Year 2025 awards Mutant meat enters Canada's food supply... and shocked Americans get a nasty surprise Why you've stopped losing weight on Mounjaro - and how to fix it: These are the sleep, alcohol and diet issues standing in your way... and the harsh truth about'microdosing' Two-time Super Bowl champion L'Jarius Sneed caught driving Lamborghini at center of alleged shooting I shed 14.5 stone after ditching my junk food habit - my secret weapon was grapes that you can get from any supermarket Grim truth about'catastrophic' diarrhea incident at Gwyneth Paltrow's house: One year later, insiders dare to tell full REAL story that will'forever haunt' her Bizarre VERY different stories I'm told about the deleted Harry and Meghan photos. The Sussex insiders are spinning one way... the Kardashians' another. Read both... and judge who you believe: ALISON BOSHOFF Vogue accused of Facetuning Amal Clooney: 'I thought it was someone else' Nutritionist influencer Diana Areas, 39, dies after'falling from top of building' The app that lets you speak with your deceased loved ones: Creepy AI creates interactive avatars of the dead - but sceptics call it'demonic, dishonest, and dehumanizing' Former Disney star Calum Worthy has been blasted for his app that uses artificial intelligence ( AI) to create avatars of dead loved ones. In a post on X, Mr Worthy, 34, shared a disturbing advert for the app, writing: 'What if the loved ones we've lost could be part of our future?'

At one point, Luis assumes that he and Esteban have been abandoned, only to realize, with a start, that their newfound friends are actually circling back to help. In such moments, we grasp the source of the story's mysterious power: a tough-minded understanding that kindness is rare yet persistent, and quite possibly an affront to the laws of nature. "Sirāt" is a chain of defiantly compassionate acts--noble human improbabilities that take on, in retrospect, an air of fatalistic inevitability. Laxe, a restless wanderer himself, knows Morocco well. He shot his first feature, "You All Are Captains" (2011), in Tangier, where he'd spent several years working at a shelter for disadvantaged children. Several of these children appeared in the movie--a formally playful collision of fiction and documentary in which Laxe, also making an appearance, slyly interrogated his European outsider-artist role. Next came "Mimosas" (2016), an elusive, arrestingly gorgeous drama about a caravan bearing a dying sheikh across Morocco's Atlas Mountains to his homeland. The film had the beauty of a travelogue and the opacity of a parable. Its most dynamic character was a fiery Muslim preacher who warned his fellow-travellers not to stray, geographically or morally.

It is well known that modern deep neural networks are powerful enough to memorize datasets even when the labels have been randomized.

We study the computational task of detecting and estimating correlated signals in a pair of spiked Wigner matrices. Our model consists of observations $$ X = \tfracλ{\sqrt{n}} xx^{\top} + W \,, \quad Y = \tfracμ{\sqrt{n}} yy^{\top} + Z \,. $$ where $x,y \in \mathbb R^n$ are signal vectors with norm $\|x\|,\|y\| \approx\sqrt{n}$ and correlation $\langle x,y \rangle \approx ρ\|x\|\|y\|$, while $W,Z$ are independent Gaussian Wigner matrices. We propose an efficient algorithm that succeeds whenever $F(λ,μ,ρ)>1$, where $$ F(λ,μ,ρ)=\max\Big\{ λ,μ, \frac{ λ^2 ρ^2 }{ 1-λ^2+λ^2 ρ^2 } + \frac{ μ^2 ρ^2 }{ 1-μ^2+μ^2 ρ^2 } \Big\} \,. $$ Our result shows that an algorithm can leverage the correlation between the spikes to detect and estimate the signals even in regimes where efficiently recovering either $x$ from $X$ alone or $y$ from $Y$ alone is believed to be computationally infeasible. We complement our algorithmic result with evidence for a matching computational lower bound. In particular, we prove that when $F(λ,μ,ρ)<1$, all algorithms based on {\em low-degree polynomials} fails to distinguish $(X,Y)$ with two independent Wigner matrices. This low-degree analysis strongly suggests that $F(λ,μ,ρ)=1$ is the precise computation threshold for this problem.

Frontier LLMs are optimised around high-resource assumptions about language, knowledge, devices, and connectivity. Whilst widely accessible, they often misfit conditions in the Global South. As a result, users must often perform additional work to make these systems usable. We term this alignment debt: the user-side burden that arises when AI systems fail to align with cultural, linguistic, infrastructural, or epistemic contexts. We develop and validate a four-part taxonomy of alignment debt through a survey of 411 AI users in Kenya and Nigeria. Among respondents measurable on this taxonomy (n = 385), prevalence is: Cultural and Linguistic (51.9%), Infrastructural (43.1%), Epistemic (33.8%), and Interaction (14.0%). Country comparisons show a divergence in Infrastructural and Interaction debt, challenging one-size-fits-Africa assumptions. Alignment debt is associated with compensatory labour, but responses vary by debt type: users facing Epistemic challenges verify outputs at significantly higher rates (91.5% vs. 80.8%; p = 0.037), and verification intensity correlates with cumulative debt burden (Spearmans rho = 0.147, p = 0.004). In contrast, Infrastructural and Interaction debts show weak or null associations with verification, indicating that some forms of misalignment cannot be resolved through verification alone. These findings show that fairness must be judged not only by model metrics but also by the burden imposed on users at the margins, compelling context-aware safeguards that alleviate alignment debt in Global South settings. The alignment debt framework provides an empirically grounded way to measure user burden, informing both design practice and emerging African AI governance efforts.

Although large language models (LLMs) are increasingly implicated in interpersonal and societal decision-making, their ability to navigate explicit conflicts between legitimately different cultural value systems remains largely unexamined. Existing benchmarks predominantly target cultural knowledge (CulturalBench), value prediction (WorldValuesBench), or single-axis bias diagnostics (CDEval); none evaluate how LLMs adjudicate when multiple culturally grounded values directly clash. We address this gap with CCD-Bench, a benchmark that assesses LLM decision-making under cross-cultural value conflict. CCD-Bench comprises 2,182 open-ended dilemmas spanning seven domains, each paired with ten anonymized response options corresponding to the ten GLOBE cultural clusters. These dilemmas are presented using a stratified Latin square to mitigate ordering effects. We evaluate 17 non-reasoning LLMs. Models disproportionately prefer Nordic Europe (mean 20.2 percent) and Germanic Europe (12.4 percent), while options for Eastern Europe and the Middle East and North Africa are underrepresented (5.6 to 5.8 percent). Although 87.9 percent of rationales reference multiple GLOBE dimensions, this pluralism is superficial: models recombine Future Orientation and Performance Orientation, and rarely ground choices in Assertiveness or Gender Egalitarianism (both under 3 percent). Ordering effects are negligible (Cramer's V less than 0.10), and symmetrized KL divergence shows clustering by developer lineage rather than geography. These patterns suggest that current alignment pipelines promote a consensus-oriented worldview that underserves scenarios demanding power negotiation, rights-based reasoning, or gender-aware analysis. CCD-Bench shifts evaluation beyond isolated bias detection toward pluralistic decision making and highlights the need for alignment strategies that substantively engage diverse worldviews.

Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. Our contributions are two-fold. First, we introduce the term Language Specific Knowledge (LSK) to denote queries that are best answered in an "expert language" for a given LLM, thereby enhancing its question-answering ability. We introduce the problem of language selection -- for some queries, language models can perform better when queried in languages other than English, sometimes even better in low-resource languages -- and the goal is to select the optimal language for the query. Second, we introduce simple to strong baselines to test this problem. Additionally, as a first-pass solution to this novel problem, we design LSKExtractor to benchmark the language-specific knowledge present in a language model and then exploit it during inference. To test our framework, we employ three datasets that contain knowledge about both cultural and social behavioral norms. Overall, LSKExtractor achieves up to 10% relative improvement across datasets, and is competitive against strong baselines, while being feasible in real-world settings. Broadly, our research contributes to the open-source development (https://github.com/agarwalishika/LSKExtractor/tree/main) of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.