The Canadian province of British Columbia is preparing to sue OpenAI, alleging the US company failed to alert police after its staff internally flagged violent ChatGPT conversations linked to the person responsible for February's Tumbler Ridge mass shooting . Attorney General Niki Sharma announced Tuesday that the province has hired legal teams in British Columbia and California to "explore all legal avenues to hold OpenAI and its decision-makers accountable for its documented failure to notify law enforcement regarding explicit, flagged threats made by the perpetrator on the company's ChatGPT platform." The move stems from the February 10 attack in the remote mountain community of Tumbler Ridge, where authorities say 18-year-old Jesse Van Rootselaar killed their mother and half-brother before going to the Tumbler Ridge Secondary School and opening fire. Five children between the ages of 11 and 13 and one educator were killed at the school. Twenty-seven other people were wounded before Van Rootselaar died from what police described as a self-inflicted gunshot wound.

Xbox is ditching Microsoft's Copilot AI Xbox is ditching Microsoft's Copilot AI Microsoft announced plans to start stripping Copilot out of select Windows apps in March after criticism of the company's mishandling of its operating system reached a fever pitch. As it turns out though, Windows isn't the only place where you'll see less Copilot: Xbox CEO Asha Sharma has announced that the AI assistant will also be removed from the gaming brand's mobile app and Xbox consoles. Under previous Xbox leadership, Copilot was introduced as a sort of in-game assistant that would be aware of what you're playing and able to offer contextual advice based on what's on your screen. Microsoft launched a beta version of the experience by adding Copilot to the Xbox mobile app in May 2025, but based on a GDC presentation the company gave in March, the plan was to also bring Copilot to Xbox consoles later this year. Those plans apparently don't align with where Xbox is headed, Sharma said in a post announcing new hires to the Xbox division.

Why are experts sounding the alarm on AI risks? In recent months, artificial intelligence has been in the news for the wrong reasons: use of deepfakes to scam people, AI systems used to manipulate cyberattacks, and chatbots encouraging suicides, among others. Experts are already warning against technology going out of control. Researchers with some of the most prominent AI companies have quit their jobs in recent weeks and publicly sounded the alarm about fast-paced technological development posing risks to society. But the recent slew of public resignations by those tasked with ensuring AI remains safe for humanity is making conversations around how to regulate the technology and slow its development more urgent, even as billions are being generated in AI investments.

Doctor at Jeffrey Epstein's post-mortem says the paedophile was strangled and NOT hanged Married California coffee growing pioneers die in'tragic accident' leaving their three children orphaned Lindsey Vonn's'primary goal is to keep her leg': Knee specialist warns Winter Olympics legend could face amputation or'lifelong consequences' after'motorcycle-style' skiing crash Disturbing Kurt Cobain autopsy details revealed for first time: As new probe claims Nirvana singer's death was a HOMICIDE, here's the evidence that convinced forensic investigators HGTV star was canned after vile video leak... now insiders make bombshell claims about what else she was doing behind the scenes: 'Unhinged' Gwyneth Paltrow's'nepo baby' Apple Martin, 21, reveals what cosmetic procedures she has had done Nashville's hottest couple engulfed by cheating storm as insiders declare: 'It's over' Jill Zarin's replacement REVEALED after racist Bad Bunny rant got her sacked from her TV comeback Scientists are ...

AI researcher says'world is in peril' and quits to study poetry An AI safety researcher has quit US firm Anthropic with a cryptic warning that the world is in peril. In his resignation letter shared on X, Mrinank Sharma told the firm he was leaving amid concerns about AI, bioweapons and the state of the wider world. He said he would instead look to pursue writing and studying poetry, and move back to the UK to become invisible. It comes in the same week that a OpenAI researcher said she had resigned, sharing concerns about the ChatGPT maker's decision to deploy adverts in its chatbot . Anthropic, best known for its Claude chatbot, had released a series of commercials aimed at OpenAI, criticising the company's move to include adverts for some users.

Agentic large language models are proposed as autonomous code generators for scientific computing, yet their reliability in high-stakes problems remains unclear. Developing computational scientific software from natural-language queries remains challenging broadly due to (a) sparse representation of domain codes during training and (b) the limited feasibility of RLHF with a small expert community. To address these limitations, this work conceptualizes an inverse approach to code design, embodied in the Chain of Unit-Physics framework: a first-principles (or primitives)-centric, multi-agent system in which human expert knowledge is encoded as unit-physics tests that explicitly constrain code generation. The framework is evaluated on a nontrivial combustion task, used here as a representative benchmark for scientific problem with realistic physical constraints. Closed-weight systems and code-focused agentic variants fail to produce correct end-to-end solvers, despite tool and web access, exhibiting four recurrent error classes: interface (syntax/API) hallucinations, overconfident assumptions, numerical/physical incoherence, and configuration fragility. Open-weight models with chain-of-thought (CoT) decoding reduce interface errors but still yield incorrect solutions. On the benchmark task, the proposed framework converges within 5-6 iterations, matches the human-expert implementation (mean error of $3.1\times10^{-3}$ %), with a $\sim$33.4 % faster runtime and a $\sim$30 % efficient memory usage at a cost comparable to mid-sized commercial APIs, yielding a practical template for physics-grounded scientific code generation. As datasets and models evolve, zero-shot code accuracy will improve; however, the Chain of Unit-Physics framework goes further by embedding first-principles analysis that is foundational to scientific codes.

Muskan Sharma stood up to men who tried to bully her over her clothes - and went on to win hearts and a beauty pageant. The 23-year-old, who was crowned Miss Rishikesh 2025 last week in the northern Indian state of Uttarakhand, told the BBC that even though it was a small local pageant, it made me feel like Miss Universe. Sharma's win has made headlines in India as it came after a viral video that showed her spiritedly arguing with a man who barged into their rehearsals just a day before the 4 October contest. Sharma, who wanted to be a model and participate in a pageant since I was in school, said the intruders came in just as they broke for lunch. We were sitting around, chilling, having a laugh when they walked in, she said.

Text-to-video (T2V) generation has surged in response to challenging questions, especially when a long video must depict multiple sequential events with temporal coherence and controllable content. Existing methods that extend to multi-event generation omit an inspection of the intrinsic factor in event shifting. The paper aims to answer the central question: When and where multi-event prompts control event transition during T2V generation. This work introduces MEve, a self-curated prompt suite for evaluating multi-event text-to-video (T2V) generation, and conducts a systematic study of two representative model families, i.e., OpenSora and CogVideoX. Extensive experiments demonstrate the importance of early intervention in denoising steps and block-wise model layers, revealing the essential factor for multi-event video generation and highlighting the possibilities for multi-event conditioning in future models.

Abstract--This work investigates the challenging task of identifying narrative roles - Hero, Villain, Victim, and Other - in Internet memes, across three diverse test sets spanning English and code-mixed (English-Hindi) languages. Building on an annotated dataset originally skewed toward the'Other' class, we explore a more balanced and linguistically diverse extension, originally introduced as part of the CLEF 2024 shared task. Comprehensive lexical and structural analyses highlight the nuanced, culture-specific, and context-rich language used in real memes, in contrast to synthetically curated hateful content, which exhibits explicit and repetitive lexical markers. T o benchmark the role detection task, we evaluate a wide spectrum of models, including fine-tuned multilingual transformers, sentiment and abuse-aware classifiers, instruction-tuned LLMs, and multimodal vision-language models. Performance is assessed under zero-shot settings using precision, recall, and F1 metrics. W e also explore prompt design strategies to guide multi-modal models and find that hybrid prompts incorporating structured instructions and role definitions offer marginal yet consistent improvements. Our findings underscore the importance of cultural grounding, prompt engineering, and multimodal reasoning in modelling subtle narrative framings in visual-textual content. W arning: This paper contains potentially harmful and offensive content. I. Introduction Social media platforms have become pivotal arenas for rapid information dissemination. However, this openness has also catalysed the proliferation of harmful content - including hate speech, propaganda, and misinformation, often embedded within memes [1], [2]. Memes, with their multimodal structure and cultural resonance, are particularly potent in shaping public opinion and propagating ideologies.

Traditional foundation models are pre-trained on broad datasets to reduce the training resources (e.g., time, energy, labeled samples) needed for fine-tuning a wide range of downstream tasks. However, traditional foundation models struggle with out-of-distribution prediction and can produce outputs that are unrealistic and physically infeasible. We propose the notation of physics-guided foundation models (PGFM), that is, foundation models integrated with broad or general domain (e.g., scientific) physical knowledge applicable to a wide range of downstream tasks.