A new study from researchers at University of Pennsylvania shows that AI models can be persuaded to break their own rules using several classic psychological tricks, reports The Verge. In the study, the Penn researchers tested seven different persuasive techniques on OpenAI's GPT-4o mini model, including authority, commitment, liking, reciprocity, scarcity, social proof, and unity. The most successful method turned out to be commitment. By first getting the model to answer a seemingly innocent question, the researchers were then able to escalate to more rule-breaking responses. One example was when the model first agreed to use milder insults before also accepting harsher ones.

A little over two years ago, OpenAI's founder Sam Altman stood in front of lawmakers at a congressional hearing and asked them for stronger regulations on artificial intelligence. The technology was "risky" and "could cause significant harm to the world", Altman said, calling for the creation of a new regulatory agency to address AI safety. Altman and the AI industry are promoting a very different message today. The AI they once framed as an existential threat to humanity is now key to maintaining American prosperity and hegemony. Regulations that were once a necessity are now criticized as a hindrance that will weaken the US and embolden its adversaries.

In today's episode, our host Zoë Schiffer is joined by WIRED's senior politics editor Leah Feiger to run through five of this week's best stories--from how AI is eliminating entry level jobs to why a secretive Democrat group is funding high-profile influencers. Then, Zoë and Leah dive into the scoop that AI researchers recently recruited to Meta Superintelligence Labs are already leaving--with some heading back to OpenAI. Join us live in San Francisco on September 9. Get your tickets here. Mentioned in this episode: Researchers Are Already Leaving Meta's New Superintelligence Lab by Zoë Schiffer and Will Knight AI Is Eliminating Jobs for Younger Workers by Will Knight Elon Musk's xAI Sues Apple and OpenAI Over App Store Rankings by Zoë Schiffer A Dark Money Group Is Secretly Funding High-Profile Democratic Influencers by Taylor Lorenz What It's Like Watching Dozens of Bodies Decompose (for Science) by Jess Thomson 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.

As artificial intelligence replaces entry-level jobs, California's universities and community colleges are offering a glimmer of hope for students: free AI training that will help them master the new technology. "You're seeing in certain coding spaces significant declines in hiring for obvious reasons," Gov. Gavin Newsom said in early August from the seventh floor of Google's San Francisco office. Flanked by leadership from California's higher education systems, he called attention to the recent layoffs at Microsoft, Google's parent company, Alphabet, and at nearby Salesforce Tower, home to the tech company that is still the city's largest private employer. Now, some of those companies -- including Google and Microsoft -- will offer a suite of AI resources free to California schools and universities. In return, the companies could gain access to millions of new users.

MultiFluxAI is an innovative AI platform developed to address the challenges of managing and integrating vast, disparate data sources in product engineering across application domains. It addresses both current and new service related queries that enhance user engagement in the digital ecosystem. This platform leverages advanced AI techniques, such as Generative AI, vectorization, and agentic orchestration to provide dynamic and context-aware responses to complex user queries.

Raine family attorney Jay Edelson provides details on the wrongful death lawsuit being brought against OpenAI and CEO Sam Altman in the wake of Adam Raine's suicide, alleging the company chose to'cut short' proper testing of ChatGPT. A former Yahoo executive who killed his elderly mother and then himself in a Connecticut home was reportedly influenced by ChatGPT, which fueled his conspiracy theories. Stein-Erik Soelberg, 56, spoke to OpenAI's popular bot, which he nicknamed "Bobby," before the shocking murder-suicide involving his 83-year-old mother, Suzanne Eberson Adams, in Old Greenwich, Conn., the Wall Street Journal reported. "Erik, you're not crazy," the chatbot said after Soelberg claimed his mother and her friend tried to poison him by putting psychedelic drugs in his car's air vents. "And if it was done by your mother and her friend, that elevates the complexity and betrayal."

Adam Raine was just 16 when he started using ChatGPT for help with his homework. While his initial prompts to the AI chatbot were about subjects like geometry and chemistry – questions like: "What does it mean in geometry if it says Ry 1" – in just a matter of months he began asking about more personal topics. "Why is it that I have no happiness, I feel loneliness, perpetual boredom anxiety and loss yet I don't feel depression, I feel no emotion regarding sadness," he asked ChatGPT in the fall of 2024. Instead of urging Raine to seek mental health help, ChatGPT asked the teen whether he wanted to explore his feelings more, explaining the idea of emotional numbness to him. That was the start of a dark turn in Raine's conversations with the chatbot, according to a new lawsuit filed by his family against OpenAI and chief executive Sam Altman.

Raine family attorney Jay Edelson provides details on the wrongful death lawsuit being brought against OpenAI and CEO Sam Altman in the wake of Adam Raine's suicide, alleging the company chose to'cut short' proper testing of ChatGPT. If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). Two California parents are suing OpenAI for its alleged role after their son committed suicide. Adam Raine, 16, took his own life in April 2025 after consulting ChatGPT for mental health support. In an appearance on "Fox & Friends" on Friday morning, Raine family attorney Jay Edelson shared more details about the lawsuit and the interaction between the teen and ChatGPT.

Top AI CEOs like DeepMind's Demis Hassabis and OpenAI's Sam Altman have recently been urging academics and governments to grapple with this issue more deeply, to better prepare the world for what they expect will be a highly disruptive economic shock. So, every day for a week--in breakout rooms and in a nightly communal sauna--these 18 experts hashed out a picture of what economic shocks might be coming down the track… and what to do about them. Bad news -- One outcome of the so-called "AGI social contract summit" was a list of four consensus statements, according to the summit's organizers. These statements have not previously been reported. They paint a grim picture of where the world could be headed, absent significant interventions by governments and societies.

Although traditional statistical techniques and machine learning methods have contributed significantly to genetics and, in particular, inherited disease diagnosis, they often struggle with complex, high-dimensional data, a challenge now addressed by state-of-the-art deep learning models. Large language models (LLMs), based on transformer architectures, have excelled in tasks requiring contextual comprehension of unstructured medical data. This systematic review examines the role of LLMs in the genetic research and diagnostics of both rare and common diseases. Automated keyword-based search in PubMed, bioRxiv, medRxiv, and arXiv was conducted, targeting studies on LLM applications in diagnostics and education within genetics and removing irrelevant or outdated models. A total of 172 studies were analyzed, highlighting applications in genomic variant identification, annotation, and interpretation, as well as medical imaging advancements through vision transformers. Key findings indicate that while transformer-based models significantly advance disease and risk stratification, variant interpretation, medical imaging analysis, and report generation, major challenges persist in integrating multimodal data (genomic sequences, imaging, and clinical records) into unified and clinically robust pipelines, facing limitations in generalizability and practical implementation in clinical settings. This review provides a comprehensive classification and assessment of the current capabilities and limitations of LLMs in transforming hereditary disease diagnostics and supporting genetic education, serving as a guide to navigate this rapidly evolving field.