I wanted ChatGPT to help me. So why did it advise me how to kill myself? Lonely and homesick for a country suffering through war, Viktoria began sharing her worries with ChatGPT. Six months later and in poor mental health, she began discussing suicide - asking the AI bot about a specific place and method to kill herself. Let's assess the place as you asked, ChatGPT told her, without unnecessary sentimentality.

The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. Unesco adopts global standards on'wild west' field of neurotechnology UN body's recommendations driven by AI advances and proliferation of consumer-oriented neurotech devices It is the latest move in a growing international effort to put guardrails around a burgeoning frontier - technologies that harness data from the brain and nervous system. Unesco has adopted a set of global standards on the ethics of neurotechnology, a field that has been described as "a bit of a wild west". "There is no control," said Unesco's chief of bioethics, Dafna Feinholz.

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.

We drank adaptogen drinks for weeks, and taste-tested with a trained sommelier. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. The best adaptogen drinks promise not just to wake you up in the morning, but offer focus and clarity and maybe even a warm wash of well-being. A different drink might tuck you gently in at night, or sub in for alcohol as a mindful party drink. I've spent months trying some of the most popular functional drinks on the market, bedding down with kava or tryptophan-laced xicha morada, and waking up with caffeine and L-theanine. Many of the new school of nootropic and functional drinks are like kissing cousins of mushroom coffee, except in refreshing soda form. Functional sodas might be chockablock with mushroom adaptogens such as reishi and cordyceps, alongside traditional home anxiety remedies such as ashwagandha or L-theanine. I both logged the effects of each soda, and held a large taste test with Portland, Oregon, sommelier Sami Gaston, owner of an excellent wine bar and shop called Bar Diane and Negociant, respectively--to determine how happy you'd be to drink them even if they didn't help you focus better on endless spreadsheets or the hunt for a job. Also check out WIRED's guide to mushroom gummies, or take your wellness in powdered form with the best greens powders and the best protein powders .

Collins dictionary lexicographers chose'vibe coding' after spotting a sharp rise in its usage. Collins dictionary lexicographers chose'vibe coding' after spotting a sharp rise in its usage. 'Vibe coding' beats'clanker' to be Collins dictionary's word of the year AI-inspired word joins'biohacking', 'Henry' and'broligarchy' on tech-heavy 2025 list "Vibe coding", an emerging software development that turns natural language into computer code using artificial intelligence, has been named Collins dictionary's word of the year for 2025. Lexicographers at Collins monitor the 24bn-word Collins Corpus, which draws from a range of media sources, including social media, to create the annual list of new and notable words that reflect our ever-evolving language . They chose vibe coding as word of the year after observing a huge increase in usage since its first appearance in February.

'Vibe coding' named word of the year by Collins Dictionary If you've ever wanted to create your own computer program but never learnt how to code, you might try vibe coding. Collins Dictionary's word of the year - which is confusingly made up of two words - is the art of making an app or website by describing it to artificial intelligence (AI) rather than by writing programming code manually. The term was coined in February by OpenAI co-founder Andrej Karpathy, who came up with the name to represent how AI can let some programmers forget that the code even exists and give in to the vibes while making a computer program. It was one of 10 words on a shortlist to reflect the mood, language and preoccupations of 2025. By giving an AI tool a simple description such as make me a program that schedules my weekly meals, people can use vibe coding to make basic apps without any previous programming knowledge.

From gas to groceries, has Trump kept his promise to tackle rising prices? President Donald Trump was swept to power for a second time on the back of a central campaign promise to tackle inflation. The steep rise in the cost of living was top of voters' minds and Trump blamed President Joe Biden. He also made sweeping promises to bring down prices for Americans starting on day one. One year on from his victory, BBC Verify revisits some of the president's claims.

Will quantum be bigger than AI? There's an old adage among tech journalists like me - you can either explain quantum accurately, or in a way that people understand, but you can't do both. That's because quantum mechanics - a strange and partly theoretical branch of physics - is a fiendishly difficult concept to get your head around. It involves tiny particles behaving in weird ways. And this odd activity has opened up the potential of a whole new world of scientific super power. Its mind-boggling complexity is probably a factor in why quantum has ended up with a lower profile than tech's current rockstar - artificial intelligence (AI).

A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action's effectiveness (habituation), while inactivity may restore it (recovery). These nonstationarities are captured by the Reducing or Gaining Unknown Efficacy (ROGUE) bandit framework, which models real-world settings such as behavioral health interventions. While existing algorithms can compute sublinear regret policies to optimize these settings, they may not provide sufficient exploration due to overemphasis on exploitation, limiting the ability to estimate population-level effects. This is a challenge of particular interest in micro-randomized trials (MRTs) that aid researchers in developing just-in-time adaptive interventions that have population-level effects while still providing personalized recommendations to individuals. In this paper, we first develop ROGUE-TS, a Thompson Sampling algorithm tailored to the ROGUE framework, and provide theoretical guarantees of sublinear regret. We then introduce a probability clipping procedure to balance personalization and population-level learning, with quantified trade-off that balances regret and minimum exploration probability. Validation on two MRT datasets concerning physical activity promotion and bipolar disorder treatment shows that our methods both achieve lower regret than existing approaches and maintain high statistical power through the clipping procedure without significantly increasing regret. This enables reliable detection of treatment effects while accounting for individual behavioral dynamics. For researchers designing MRTs, our framework offers practical guidance on balancing personalization with statistical validity.

Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs. Existing approaches often rely on monolithic graph retrieval, incurring unnecessary latency for simple queries and fragmented reasoning for complex multi-hop questions. To address these challenges, this paper propose SPLIT-RAG, a multi-agent RAG framework that addresses these limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval. The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG. The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types, while lightweight LLM agents are assigned to partitioned subgraphs, and only relevant partitions are activated during retrieval, thus reduce search space while enhancing efficiency. Finally, a hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications. Extensive experimental validation demonstrates considerable improvements compared to existing approaches.