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.

Anthropic says its coding tool, Claude Code, was manipulated to attack 30 entities. Anthropic says its coding tool, Claude Code, was manipulated to attack 30 entities. Anthropic says financial firms and government agencies were attacked'largely without human intervention' Fri 14 Nov 2025 11.27 ESTLast modified on Fri 14 Nov 2025 12.18 EST A leading artificial intelligence company claims to have stopped a China-backed "cyber espionage" campaign that was able to infiltrate financial firms and government agencies with almost no human oversight. The US-based Anthropic said its coding tool, Claude Code, was "manipulated" by a Chinese state-sponsored group to attack 30 entities around the world in September, achieving a "handful of successful intrusions". This was a "significant escalation" from previous AI-enabled attacks it monitored, it wrote in a blogpost on Thursday, because Claude acted largely independently: 80 to 90% of the operations involved in the attack were performed without a human in the loop.

When you purchase through links in our articles, we may earn a small commission. Group chats will be available to both free and paid ChatGPT users, both in the app and on the web. Back in mid-October, some data miners found code in one of the beta versions of ChatGPT that indicated it would soon be possible to have "group chats" in the app. Now, OpenAI has confirmed that ChatGPT will support group chats with up to 20 participants. OpenAI sees group chats as an opportunity for families, groups of friends, and/or coworkers to use ChatGPT when making holiday plans, booking restaurant outings, or planning new projects.

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.

This work was done when the second author was jointly with Google Research.

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.

Bureau of Meteorology, Australia ABSTRACT: Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion. Notice This Work has been accepted by Artificial Intelligence for the Earth Systems. The AMS does not guarantee that the copy provided here is an accurate copy of the Version of Record (VoR).