Clash of the White House titans: Two of Trump's most powerful lieutenants go to WAR with each other - after vicious leak sent shockwaves The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox Ominous warning for humanity as birds suddenly adopt'unsettling' behavior The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years Brazilian fashion influencer Junior Dutra dies at age 31 after alleged'fox eyes' procedure complications I've seen AI try to ESCAPE labs. The apocalypse is already here... and our children will be the first victims Trump brands NFL's Bad Bunny Super Bowl halftime show selection'absolutely ridiculous' Investigators reveal there is'no evidence' of arson after horror blaze destroyed South Carolina judge's beachfront home Functioning alcoholics hide in plain sight... so are YOU one? It sounds like the start of a sci-fi film, but scientists have shown that AI can design brand-new infectious viruses the first time. Experts at Stanford University in California used'Evo' - an AI tool that creates genomes from scratch. Amazingly, the tool was able to create viruses that are able to infect and kill specific bacteria.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Janice Anta Zebaze is using AI to address energy supply challenges and she told us more about the research she's carried our so far, her plans for further investigations, and what inspired her to pursue a PhD in the field. Tell us a bit about your PhD - where are you studying, and what is the topic of your research? I am currently pursuing my PhD in Physics at the University of Yaounde I in Cameroon, with a focus on renewable energy systems, tribology, and artificial intelligence. The aim of my research is to address energy supply challenges in developing countries by leveraging AI to evaluate resource availability and optimize energy systems.

Despite AI's promise for addressing global challenges, empirical understanding of AI adoption in mission-driven organizations (MDOs) remains limited. While research emphasizes individual applications or ethical principles, little is known about how resource-constrained, values-driven organizations navigate AI integration across operations. We conducted thematic analysis of semi-structured interviews with 15 practitioners from environmental, humanitarian, and development organizations across the Global North and South contexts. Our analysis examines how MDOs currently deploy AI, what barriers constrain adoption, and how practitioners envision future integration. MDOs adopt AI selectively, with sophisticated deployment in content creation and data analysis while maintaining human oversight for mission-critical applications. When AI's efficiency benefits conflict with organizational values, decision-making stalls rather than negotiating trade-offs. This study contributes empirical evidence that AI adoption in MDOs should be understood as conditional rather than inevitable, proceeding only where it strengthens organizational sovereignty and mission integrity while preserving human-centered approaches essential to their missions.

Large Language Models (LLMs) are widely used for code generation, but they face critical security risks when applied to practical production due to package hallucinations, in which LLMs recommend non-existent packages. These hallucinations can be exploited in software supply chain attacks, where malicious attackers exploit them to register harmful packages. It is critical to test LLMs for package hallucinations to mitigate package hallucinations and defend against potential attacks. Although researchers have proposed testing frameworks for fact-conflicting hallucinations in natural language generation, there is a lack of research on package hallucinations. To fill this gap, we propose HFUZZER, a novel phrase-based fuzzing framework to test LLMs for package hallucinations. HFUZZER adopts fuzzing technology and guides the model to infer a wider range of reasonable information based on phrases, thereby generating enough and diverse coding tasks. Furthermore, HFUZZER extracts phrases from package information or coding tasks to ensure the relevance of phrases and code, thereby improving the relevance of generated tasks and code. We evaluate HFUZZER on multiple LLMs and find that it triggers package hallucinations across all selected models. Compared to the mutational fuzzing framework, HFUZZER identifies 2.60x more unique hallucinated packages and generates more diverse tasks. Additionally, when testing the model GPT-4o, HFUZZER finds 46 unique hallucinated packages. Further analysis reveals that for GPT-4o, LLMs exhibit package hallucinations not only during code generation but also when assisting with environment configuration.

MUNICH - Blocked from the U.S. market, Chinese self-driving technology firms are accelerating their push into Europe, setting up headquarters, striking data deals and road-testing -- prompting alarm from local rivals over competition concerns. In China, the world's largest car market, more than half of cars sold -- including many entry-level models -- now offer autonomous driving technology, sometimes as standard. Beijing is pushing its companies to dominate autonomous-vehicle development globally while crafting national regulations to provide a clear roadmap at home. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

On this episode of, we break down some of the week's best stories, covering everything from Peter Thiel's obsession with the Antichrist to the launch of OpenAI's new Sora 2 video app. 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. In today's episode, Zoë Schiffer is joined by WIRED's senior culture editor Manisha Krishnan to run through five of the best stories we published this week--from how federal workers are being told to blame Democrats for the government shutdown to Peter Thiel's ongoing obsession with the Antichrist. Then, Zoë and Manisha break down the news of OpenAI launching a new social app for AI-generated videos. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Today on the show, we're bringing you five stories that you need to know about this week. Including our scoop of how OpenAI just launched a social app dedicated completely to AI-generated videos. I'm joined today by our Senior Culture Editor, Manisha Krishnan. Our first story is about the thing that I feel like our whole newsroom is talking about, possibly the whole country is talking about.

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

Nobel Prize 2025: What they are, when will the awards be announced? The Nobel Prize 2025 officially kicks off with the first award, for physiology or medicine, to be announced on Monday, setting the stage for a week of global anticipation. The full schedule, spanning from October 6 to 13, maps out a rapid succession of announcements: medicine, followed by physics, chemistry, literature, peace, and finally culminating with the economics prize next Monday. Here are the complete details of the schedule - and what to expect from this year's Nobel Prizes. What is the Nobel Prize?

Meghan is ridiculed for her'Zoolander' walk for cameras in Paris and boasting about her'return to the shows after 10 years' (when she was on the Z-list) Mark Sanchez's alleged victim's family breaks silence as grim photos emerge after violent attack Trump reveals plan to'restore the American Dream' by unlocking 2 million lots for new homes Mystery of the Schuylkill County notes explodes as fears mount over plague of creepy messages: 'What else could they do?' My son made a horrifying accusation about me in therapy... it's destroyed our relationship: DEAR JANE Trump's immigration guru Stephen Miller gets called'the face of evil' by his own cousin Illinois Governor JB Pritzker says Trump to deploy 400 National Guard troops from Texas to'invade' liberal states US billionaire retail estate tycoon is ordered to sell off his'exceptional' £36million London mansion in bitter divorce battle with ex-wife South Carolina judge's $1.5M beachfront home burned to the ground in possible arson ...

While language models (LMs) offer great potential for conversational recommender systems (CRSs), the paucity of public CRS data makes fine-tuning LMs for CRSs challenging. In response, LMs as user simulators qua data generators can be used to train LM-based CRSs, but often lack behavioral consistency, generating utterance sequences inconsistent with those of any real user. To address this, we develop a methodology for generating natural dialogues that are consistent with a user's underlying state using behavior simulators together with LM-prompting. We illustrate our approach by generating a large, open-source CRS data set with both preference elicitation and example critiquing. Rater evaluation on some of these dialogues shows them to exhibit considerable consistency, factuality and naturalness.