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.

A tornado tore through Val-d'Oise, north of Paris, on Monday, toppling construction cranes, damaging properties and uprooting trees in its path. One person was killed and four others critically injured, authorities said. The town of Ermont, about 20 km (13 miles) northeast of Paris was hardest hit by the sudden twister, which caused damage in multiple districts. Interior Minister Laurent Nunez said on the X social media platform that it had been a storm of rare intensity. Drone footage shows blaze destroying the historic Bernaga Monastery in Italy.

The Business Secretary has insisted the government is making it easier for businesses by reducing red tape. Peter Kyle defended Labour's approach to business, telling the BBC it will implement changes in a way that is pro-worker and pro-business. Ahead of next month's Budget, Chancellor Rachel Reeves is launching a crackdown on needless form-filling for businesses at the first-ever Regional Investment Summit in Birmingham. The government has been criticised by firms who say increased employers' National Insurance contributions and the Employment Rights Bill add to the burdens facing businesses. The Chancellor will say at the Birmingham summit on Tuesday that the changes will save firms almost £6bn a year.

'A 25-hour story that just about makes sense' Vampire: The Masquerade Bloodlines 2. 'A 25-hour story that just about makes sense' Vampire: The Masquerade Bloodlines 2. Y ou are an ancient and powerful vampire, and you wake up in the basement of some decrepit Seattle building, with no recent memories and a strange sigil on your hand. The first thing you do is feed on the cop who finds you, before smacking his partner into a wall so hard that his blood spatters the brick. A violent fanged rampage ensues, where you beat up and tear apart rival undead and their ghouls while currying the favour of the local court of vampires, and trying to keep your existence hidden from the mortal populace of this sultry city. But this is also a detective story: there's a younger night-stalker sharing your brain, a voice in your head named Fabian, who talks like a 1920s gumshoe (presumably because he once was one). Fabian isn't violent at all; he evidently works with the human police and the vampire underworld, snacking on consenting volunteers' blood and using his mind-delving powers to solve murders.

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Bryan Cranston pictured speaking at a Sag-Aftra strike rally in 2023 in New York. The Breaking Bad actor went to the union with concerns after users of OpenAI's generative video platform Sora 2 were able to generate his likeness without his consent. Bryan Cranston pictured speaking at a Sag-Aftra strike rally in 2023 in New York. The Breaking Bad actor went to the union with concerns after users of OpenAI's generative video platform Sora 2 were able to generate his likeness without his consent. Users of generative AI video app were able to recreate the Breaking Bad actor's likeness without his consent, which OpenAI called'unintentional' Bryan Cranston has said he is "grateful" to OpenAI for cracking down on deepfakes of himself on the company's generative AI video platform Sora 2, after users were able to generate his voice and likeness without his consent.

The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the econometrics literature to explore the dynamic factor model as an interpretable model with sufficient predictive capabilities for capturing essential market phenomena. Although the model has been extensively applied for predictive purposes, this study focuses on analyzing the extracted loadings and common factors as an alternative framework for understanding stock price dynamics. The results reveal novel insights into traditional market theories when applied to the Philippine Stock Exchange using the Kalman method and maximum likelihood estimation, with subsequent validation against the capital asset pricing model. Notably, a one-factor model extracts a common factor representing systematic or market dynamics similar to the composite index, whereas a two-factor model extracts common factors representing market trends and volatility. Furthermore, an application of the model for nowcasting the growth rates of the Philippine gross domestic product highlights the potential of the extracted common factors as viable real-time market indicators, yielding over a 34% decrease in the out-of-sample prediction error. Overall, the results underscore the value of dynamic factor analysis in gaining a deeper understanding of market price movement dynamics.

Dialogue plays a crucial role in educational settings, yet existing evaluation methods for educational applications of large language models (LLMs) primarily focus on technical performance or learning outcomes, often neglecting attention to learner - LLM int eractions. To narrow this gap, this AIED Doctoral Consortium paper presents an ongoing study employing a dialogue analysis approach to identify effective pedagogical strategies from learner - LLM dialogues. The proposed approach involves dialogue d ata collection, dialogue act (DA) annotation, DA pattern mining, and predictive model building. Early insights are outlined as an initial step toward future research. The work underscores the need to evaluate LLM - based educational applications by focusing on dialogue dynamics and pedagogical strategies.

We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovations are (1) a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining, (2) an information-theoretic recruitment mechanism that adaptively allocates semantic blocks as needed, eliminating the requirement for complete domain knowledge at initialization, and (3) a hierarchical recruitment framework that extends capacity allocation to entire specialized LLMs, enabling multi-granularity architectural adaptation. This is achieved through group sparsity penalties on attention mechanisms, information-theoretic anchor design, dynamic rule injection, and principled recruitment criteria based on penalized likelihood with explicit units. We provide rigorous mathematical results establishing explicit threshold conditions under which attention provably concentrates on semantically relevant blocks at stationary points, with exact bounds on attention entropy and pointer fidelity. The hierarc hical recruitment mechanism provides convergence guarantees at both the block level (fine-grained, within-LLM) and the LLM level (coarse-grained, cross-domain), ensuring the system discovers semantic partitions that balance model complexity against data encoding efficiency. This framework enables practitioners to continuously interpolate between interpretable and high-performance modes while adapti ng architectural capacity at multiple granularities, supporting applications in regulated domains requiring both transparency and capability.

Globally, individuals and organizations employ Quick Response (QR) codes for swift and convenient communication. Leveraging this, cybercriminals embed falsify and misleading information in QR codes to launch various phishing attacks which termed as Quishing. Many former studies have introduced defensive approaches to preclude Quishing such as by classifying the embedded content of QR codes and then label the QR codes accordingly, whereas other studies classify them using visual features (i.e., deep features, histogram density analysis features). However, these approaches mainly rely on black-box techniques which do not clearly provide interpretability and transparency to fully comprehend and reproduce the intrinsic decision process; therefore, having certain obvious limitations includes the approaches' trust, accountability, issues in bias detection, and many more. We proposed QRïS, the pioneer method to classify QR codes through the comprehensive structural analysis of a QR code which helps to identify phishing QR codes beforehand. Our classification method is clearly transparent which makes it reproducible, scalable, and easy to comprehend. First, we generated QR codes dataset (i.e. 400,000 samples) using recently published URLs datasets [1], [2]. Then, unlike black-box models, we developed a simple algorithm to extract 24 structural features from layout patterns present in QR codes. Later, we train the machine learning models on the harvested features and obtained accuracy of up to 83.18%. To further evaluate the effectiveness of our approach, we perform the comparative analysis of proposed method with relevant contemporary studies. Lastly, for real-world deployment and validation, we developed a mobile app which assures the feasibility of the proposed solution in real-world scenarios which eventually strengthen the applicability of the study.