The BBC has upheld 20 complaints over impartiality after presenter Martine Croxall altered a script she was reading live on the BBC News Channel which referred to pregnant people earlier this year. Croxall was introducing an interview about research on groups most at risk during UK heatwaves, which quoted a release from the London School of Hygiene and Tropical Medicine. The presenter changed her script to instead say women, and the BBC's Executive Complaints Unit said it considered her facial expression to express a controverial view about trans people. The presenter said: Malcolm Mistry, who was involved in the research, says that the aged, pregnant people women and those with pre-existing health conditions need to take precautions. The ECU said it considered Croxall's facial expression laid it open to the interpretation that it indicated a particular viewpoint in the controversies currently surrounding trans ideology.

Will AI Take My Job? Dispatches AI presenter Aisha Gaban. Will AI Take My Job? Dispatches AI presenter Aisha Gaban. Channel 4's first AI presenter is dizzyingly grim on so many levels The AI-generated host of Dispatches raises worrying questions about Channel 4's environmental impact. She's also a dead-eyed host who might leave Krishnan Guru-Murthy and Kevin McCloud fearing for their future L ast night's Dispatches was called Will AI Take My Job? Usually when something like this employs a question mark in the title, it's because the answer is no. Not this time, though, because the sheer overwhelming inevitability of AI taking our jobs is genuinely painful to think about. According to the film, 8m jobs in the UK alone are at risk of being outsourced by AI.

Short form video platforms are central sites for health advice, where alternative narratives mix useful, misleading, and harmful content. Rather than adjudicating truth, this study examines how credibility is packaged in nutrition and supplement videos by analyzing the intersection of authority signals, narrative techniques, and monetization. We assemble a cross platform corpus of 152 public videos from TikTok, Instagram, and YouTube and annotate each on 26 features spanning visual authority, presenter attributes, narrative strategies, and engagement cues. A transparent annotation pipeline integrates automatic speech recognition, principled frame selection, and a multimodal model, with human verification on a stratified subsample showing strong agreement. Descriptively, a confident single presenter in studio or home settings dominates, and clinical contexts are rare. Analytically, authority cues such as titles, slides and charts, and certificates frequently occur with persuasive elements including jargon, references, fear or urgency, critiques of mainstream medicine, and conspiracies, and with monetization including sales links and calls to subscribe. References and science like visuals often travel with emotive and oppositional narratives rather than signaling restraint.

Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and time series data. However, a cross-modality gap exists between time series and textual data, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. In this tutorial, we provide an up-to-date overview of LLM-based cross-modal time series analytics. We introduce a taxonomy that classifies existing approaches into three groups based on cross-modal modeling strategies, e.g., conversion, alignment, and fusion, and then discuss their applications across a range of downstream tasks. In addition, we summarize several open challenges. This tutorial aims to expand the practical application of LLMs in solving real-world problems in cross-modal time series analytics while balancing effectiveness and efficiency. Participants will gain a thorough understanding of current advancements, methodologies, and future research directions in cross-modal time series analytics.

In this article, we present a novel multimodal feedback framework called MOSAIC-F, an acronym for a data-driven Framework that integrates Multimodal Learning Analytics (MMLA), Observations, Sensors, Artificial Intelligence (AI), and Collaborative assessments for generating personalized feedback on student learning activities. This framework consists of four key steps. First, peers and professors' assessments are conducted through standardized rubrics (that include both quantitative and qualitative evaluations). Second, multimodal data are collected during learning activities, including video recordings, audio capture, gaze tracking, physiological signals (heart rate, motion data), and behavioral interactions. Third, personalized feedback is generated using AI, synthesizing human-based evaluations and data-based multimodal insights such as posture, speech patterns, stress levels, and cognitive load, among others. Finally, students review their own performance through video recordings and engage in self-assessment and feedback visualization, comparing their own evaluations with peers and professors' assessments, class averages, and AI-generated recommendations. By combining human-based and data-based evaluation techniques, this framework enables more accurate, personalized and actionable feedback. We tested MOSAIC-F in the context of improving oral presentation skills.

My brother held his phone up to my ear. "You're gonna find this creepy," he warned. An Instagram reel showing a teenage boy at a rally featured a voiceover in the style of a news broadcast. A calm, female voice, with an almost imperceptible Mancunian accent, said: "The recent outcry from a British student has become a powerful symbol of a deepening crisis in the UK's educational system." I sat bolt upright, my eyes wide open.

Creating professional-looking videos can be an expensive and time-consuming process. Not only does it require presenters, video equipment, studios and lighting, but you'll also need an editor to spend hours putting the whole thing together. Well, thanks to the amazing power of AI technology, this isn't true anymore, as Vidnoz AI can take care of all those factors and produce a tailor-made video for your business or project in just a few minutes. Best of all, you can do it for free. Vidnoz AI is a new AI suite designed to help people easily create videos with realistic human presenters.

The news presenter has a deeply uncanny air as he delivers a partisan and pejorative message in Mandarin: Taiwan's outgoing president, Tsai Ing-wen, is as effective as limp spinach, her period in office beset by economic under performance, social problems and protests. "Water spinach looks at water spinach. Turns out that water spinach isn't just a name," says the presenter, in an extended metaphor about Tsai being "Hollow Tsai" – a pun related to the Mandarin word for water spinach. This is not a conventional broadcast journalist, even if the lack of impartiality is no longer a shock. The anchor is generated by an artificial intelligence programme, and the segment is trying, albeit clumsily, to influence the Taiwanese presidential election. The source and creator of the video are unknown, but the clip is designed to make voters doubt politicians who want Taiwan to remain at arm's length from China, which claims that the self-governing island is part of its territory.

Like most newsreaders, Zae-In wears a microphone pinned to her collar and clutches a stack of notes – but unlike most, her face is entirely fake. A "virtual human" designed by South Korean artificial intelligence company Pulse9, Zae-In spent five months this year reading live news bulletins on national broadcaster SBS. That, you might think, is it then. To adapt the words of another animated newscaster: "I, for one, welcome our new AI overlords." The world belongs to the artificially intelligent and the News at Ten will never be the same again.

The Guardian's review of space exploration epic Starfield, Xbox's big game of the year, went live this morning – almost a week after other outlets published theirs. This is because Bethesda did not give our reviewer an advance copy, as publishers usually do. Along with several others, including the greatly respected games publications Eurogamer and Edge, we were left waiting until the game's early access release last Friday to play it. Bethesda's reasons for cherry-picking reviewers are known only to itself, but it's far from the only publisher to do this. Sometimes, controlling early reviews is a way to manipulate a game's Metacritic average in the crucial first week of release.