British microcomputers were among more than 100,000 foreign-made parts contained in Russian missiles and drones used in Sunday's deadly strikes on Ukraine, Volodymyr Zelensky has said. The Ukrainian president called for further effective sanctions after saying parts originating in allied countries including Germany, Japan and the US have been identified in Russian weapons. The Department for Business and Trade (DBT) said it had recently undertaken efforts to crack down on UK firms whose products have continued to make their way into Russia's military supply chain. We take reports of goods from UK companies being found in Russian weaponry incredibly seriously, a government spokesperson said. The spokesperson said the government had banned the export of thousands of goods to Russia including every battlefield item Ukraine has brought to our attention, adding that they have imposed the most the most severe package of sanctions. What are the sanctions on Russia and are they working?

OpenAI and the chipmaker AMD announced on Monday that they had signed a multibillion-dollar chip deal that would also give the ChatGPT creator the option to buy a large stake in the chipmaker. The deal offers OpenAI an opportunity to buy 10% in AMD and marks a major vote of confidence in the company's AI chips and software. Shares of AMD surged more than 30% and added about $80bn to its market capitalization after the announcement. "We view this deal as certainly transformative, not just for AMD, but for the dynamics of the industry," said Forrest Norrod, AMD's executive vice-president. The latest deal, among a string of investment commitments, is a testament to OpenAI and the broader AI industry's voracious appetite for computing power as companies race toward developing AI technology that meets or exceeds human intelligence.

Research reveals that those diagnosed with autism early show distinct genetic and developmental profiles from those diagnosed later. New research from the University of Cambridge suggests that autism should not be understood as a homogeneous condition with a single cause. Scientists found that people diagnosed in early childhood often have a different genetic profile than those diagnosed later in life, broadening the understanding of how the condition develops. The study analyzed the behavior of autistic people during childhood and adolescence in the United Kingdom and Australia. It also evaluated genetic data of more than 45,000 patients with the condition from diverse cohorts in Europe and the United States.

Meghan Daley is a NASA project manager who leads teams to develop and integrate simulations for robotic operations to prepare astronauts on the ISS and beyond. We'll be spotlighting five honorees each week throughout October

At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. 'Obedient, yielding and happy to follow': the troubling rise of AI girlfriends E leanor, 24, is a Polish historian and lecturer at a university in Warsaw; Isabelle, 25, is a detective serving with the NYPD; Brooke, 39, is an American housewife who enjoys an opulent Miami lifestyle financed by her frequently absent husband. All three women will flirt and chat and send nude photographs and explicit videos via one of a soaring number of new adult dating websites that offer an increasingly realistic selection of AI girlfriends for subscribers willing to pay a monthly fee. At the TES adult industry conference in Prague last month, delegates noted a sharp increase in new websites offering users the chance to form relationships with AI-generated girlfriends, who will remove their clothes in exchange for tokens purchased by bank transfer.

OpenAI's Sora 2 app allows users to make AI-generated videos based on a text prompt. OpenAI's Sora 2 app allows users to make AI-generated videos based on a text prompt. Company behind the AI video app says it will work with rights holders to'block characters from Sora at their request' Mon 6 Oct 2025 00.10 EDTLast modified on Mon 6 Oct 2025 00.11 EDT Sora 2, a video generator powered by artificial intelligence, was launched last week on an invite-only basis. The app allows users to generate short videos based on a text prompt. Varun Shetty, OpenAI's head of media partnerships, said: "We'll work with rights holders to block characters from Sora at their request and respond to takedown requests."

The first day of September should have marked the beginning of one of the busiest periods of the year for Jaguar Land Rover. It was a Monday, and the release of new 75 series number plates was expected to produce a surge in demand from eager car buyers. At factories in Solihull and Halewood, as well as at its engine plant in Wolverhampton, staff were expecting to be working flat out. Instead, when the early shift arrived, they were sent home. The production lines have remained idle ever since.

Recent progress in retrieval-augmented generation (RAG) has led to more accurate and interpretable multi-hop question answering (QA). Yet, challenges persist in integrating iterative reasoning steps with external knowledge retrieval. To address this, we introduce StepChain GraphRAG, a framework that unites question decomposition with a Breadth-First Search (BFS) Reasoning Flow for enhanced multi-hop QA. Our approach first builds a global index over the corpus; at inference time, only retrieved passages are parsed on-the-fly into a knowledge graph, and the complex query is split into sub-questions. For each sub-question, a BFS-based traversal dynamically expands along relevant edges, assembling explicit evidence chains without overwhelming the language model with superfluous context. Experiments on MuSiQue, 2WikiMultiHopQA, and HotpotQA show that StepChain GraphRAG achieves state-of-the-art Exact Match and F1 scores. StepChain GraphRAG lifts average EM by 2.57% and F1 by 2.13% over the SOTA method, achieving the largest gain on HotpotQA (+4.70% EM, +3.44% F1). StepChain GraphRAG also fosters enhanced explainability by preserving the chain-of-thought across intermediate retrieval steps. We conclude by discussing how future work can mitigate the computational overhead and address potential hallucinations from large language models to refine efficiency and reliability in multi-hop QA.

Assessing model generalization under distribution shift is essential for real-world deployment, particularly when labeled test data is unavailable. This paper presents a unified and practical framework for unsupervised model evaluation and ranking in two common deployment settings: (1) estimating the accuracy of a fixed model on multiple unlabeled test sets (dataset-centric evaluation), and (2) ranking a set of candidate models on a single unlabeled test set (model-centric evaluation). We demonstrate that two intrinsic properties of model predictions, namely confidence (which reflects prediction certainty) and dispersity (which captures the diversity of predicted classes), together provide strong and complementary signals for generalization. We systematically benchmark a set of confidence-based, dispersity-based, and hybrid metrics across a wide range of model architectures, datasets, and distribution shift types. Our results show that hybrid metrics consistently outperform single-aspect metrics on both dataset-centric and model-centric evaluation settings. In particular, the nuclear norm of the prediction matrix provides robust and accurate performance across tasks, including real-world datasets, and maintains reliability under moderate class imbalance. These findings offer a practical and generalizable basis for unsupervised model assessment in deployment scenarios.

Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for status assessment). This knowledge-reasoning gap, evident across all models, suggests inherent architectural constraints, not just data limitations. Furthermore, models exhibited systematic biases favoring charismatic vertebrates, potentially amplifying existing conservation inequities. These findings delineate clear boundaries for responsible LLM deployment: they are powerful tools for information retrieval but require human oversight for judgment-based decisions. A hybrid approach is recommended, where LLMs augment expert capacity while human experts retain sole authority over risk assessment and policy.