BEIJING/SINGAPORE - Chinese state-owned defense giant Norinco in February unveiled a military vehicle capable of autonomously conducting combat-support operations at 50 kilometers per hour. It was powered by DeepSeek, the company whose artificial intelligence model is the pride of China's tech sector. The Norinco P60's release was touted by Communist Party officials in press statements as an early showcase of how Beijing is using DeepSeek and AI to catch up in its arms race with the United States, at a time when leaders in both countries have urged their militaries to prepare for conflict. A review of hundreds of research papers, patents and procurement records gives a snapshot of the systematic effort by Beijing to harness AI for military advantage. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Qualcomm has not fared as well, partly because the smartphone market has not been growing much lately. But Cristiano Amon, the company's chief executive, has laid out several initiatives to diversify the business. Qualcomm helped invent core technology used in cellular networks, selling modem chips that phones use to communicate. The company later combined that ability with basic computing functions, using technology licensed from the British company Arm Holdings. More recently, Qualcomm has targeted phones and personal computers with chips tailored to handle machine learning, performing the mathematical calculations used for many A.I. functions.

Is London ready for driverless taxis? Autonomous cabs are a staple in some US cities - but how will they cope with London's streets? Will ghost taxis put a whole workforce out of a job? Will British passengers behave themselves without a human driver in the front seat - and can the technology really be trusted to keep people safe?

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian attacks on Ukraine's southern Zaporizhia killed a 44-year-old man and wounded several others, Governor Ivan Fedorov said on Monday, as the death toll from other assaults on Sunday continued to rise. Ukrainian officials said the attacks on Sunday killed two people in the eastern Donetsk region and a 69-year-old man in the northern Sumy region.

The new AI-powered Wikipedia competitor falsely claims that pornography worsened the AIDS epidemic and that social media may be fueling a rise in transgender people. On Monday, Elon Musk's xAI startup launched Grokipedia, which the billionaire is pitching as an AI-generated alternative to the crowdsourced encyclopedia Wikipedia. Musk first announced the project in late September on his social media platform X, saying it would be "a massive improvement over Wikipedia," and "a necessary step towards the xAI goal of understanding the Universe." Musk said last week that he had delayed the launch of Grokipedia because his team needed "to do more work to purge out the propaganda." When Grokipedia eventually dropped on Monday, WIRED was initially unable to access the website and received an automated message that it was blocked.

Ever since her death in 1986, it was taken as common knowledge that Elsa Lanchester - who became a horror movie icon by playing the title character in the Bride of Frankenstein - had been cremated and her ashes sprinkled in the ocean. But then Scott Michaels, the founder of Dearly Departed Tours, discovered that her cremated remains were interred in a rose garden under her married name, Elsa Lanchester Laughton. For almost 40 years no one had made the connection - until now, he says. Mr Michaels, 63, is a historian who specialises in the dark side of Hollywood. A go-to for programmes about dead Hollywood celebrities and murder, he has consulted for Quentin Tarantino's Manson murder film Once Upon a Time in Hollywood.

Turing AI Institute boss denies accusations of'toxic internal culture' The Alan Turing Institute Chair has told the BBC there is no substance to a number of serious accusations which rocked the organisation in the summer. In August, whistleblowers accused the charity's leadership of misusing public funds, overseeing a toxic internal culture, and failing to deliver on its mission. They said the Turing Institute, the UK's national body for artificial intelligence (AI), was on the brink of collapse after Peter Kyle, the then technology secretary, threatened to withdraw its £100m funding. But speaking exclusively to the BBC, Chair Dr Doug Gurr said the whistleblower claims were independently investigated by a third party which found them to have no substance. I fully sympathise that going through any transition is always challenging, he said.

These robots can clean, exercise - and care for your elderly parents. Would you trust them to? Hidden away in a lab in north-west London three black metal robotic hands move eerily on an engineering work bench. We're not trying to build Terminator, jokes Rich Walker, director of Shadow Robot, the firm that made them. Bespectacled, with long hair and a beard and moustache, he seems more like a latter-day hippy than a tech whizz, and he is clearly proud as he shows me around his firm.

This study proposes an interpretable prediction framework with literature-informed fine-tuned (LIFT) LLMs for truck driving risk prediction. The framework integrates an LLM-driven Inference Core that predicts and explains truck driving risk, a Literature Processing Pipeline that filters and summarizes domain-specific literature into a literature knowledge base, and a Result Evaluator that evaluates the prediction performance as well as the interpretability of the LIFT LLM. After fine-tuning on a real-world truck driving risk dataset, the LIFT LLM achieved accurate risk prediction, outperforming benchmark models by 26.7% in recall and 10.1% in F1-score. Furthermore, guided by the literature knowledge base automatically constructed from 299 domain papers, the LIFT LLM produced variable importance ranking consistent with that derived from the benchmark model, while demonstrating robustness in interpretation results to various data sampling conditions. The LIFT LLM also identified potential risky scenarios by detecting key combination of variables in truck driving risk, which were verified by PERMANOVA tests. Finally, we demonstrated the contribution of the literature knowledge base and the fine-tuning process in the interpretability of the LIFT LLM, and discussed the potential of the LIFT LLM in data-driven knowledge discovery.

Large Language Models' (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to investigate how LLMs' cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. Our study introduces a novel intersectional analysis spanning 315 unique personas, constructed from combinations of age, culture, and gender, across four LLMs. Results show that attributes profoundly shape a model's empathetic responses. Interestingly, we see that adding multiple attributes at once can attenuate and reverse expected empathy patterns. We show that they broadly reflect real-world empathetic trends, with notable misalignments for certain groups, such as those from Confucian culture. We complement our quantitative findings with qualitative insights to uncover model behaviour patterns across different demographic groups. Our findings highlight the importance of designing empathy-aware LLMs that account for demographic diversity to promote more inclusive and equitable model behaviour.