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The Problem With Using AI in Your Personal Life

The Atlantic - Technology

Using LLMs to talk with your friends is efficient. My friend recently attended a funeral, and midway through the eulogy, he became convinced that it had been written by AI. There was the telltale proliferation of abstract nouns, a surfeit of assertions that the deceased was "not just --he was " coupled with a lack of concrete anecdotes, and more appearances of the word than you would expect from a rec-league hockey teammate. It was both too good, in terms of being grammatically correct, and not good enough, in terms of being particular. My friend had no definitive proof that he was listening to AI, but his position--and I agree with him--is that when you know, you know. His sense was that he had just heard a computer save a man from thinking about his dead friend.


I left my toxic mums' group because I'd had enough of being judged

BBC News

I left my toxic mums' group because I'd had enough of being judged Martina loved the idea of a baby signing class. As well as teaching her baby to communicate with simple hand gestures, she'd be able to meet other mothers in her area. But after the third session, Martina scooped up her newborn and walked out. She'd had enough of being judged. She says the other mothers scoffed at her parenting choices - she bottle-feeds her son - and seemed to disapprove of her choosing to deliver her baby by caesarean section.


Parakeets teach a lesson in friendship

Popular Science

Breakthroughs, discoveries, and DIY tips sent every weekday. Making new friends (especially as an adult) can be challenging. When new birds are introduced to a group, monk parakeets will "test the waters" to avoid getting injured by defensive strangers. The parakeets will gradually approach the new bird, taking some time to get familiar before ramping up to more risky or vulnerable interactions that are needed to form the bonds necessary for survival. "There can be a lot of benefits to being social, but these friendships have to start somewhere," said Claire O'Connell, a study co-author and a doctoral student in the University of Cincinnati, said in a statement .


Ed Zitron Gets Paid to Love AI. He Also Gets Paid to Hate AI

WIRED

Ed Zitron Gets Paid to Love AI. He's one of the loudest voices of the AI haters--even as he does PR for AI companies. Either way, Ed Zitron has your attention. In his day job, Ed Zitron runs a boutique public relations firm called EZPR. This might surprise anyone who has come to know Zitron through his podcast or his social media or the newsletter in which he writes two-fisted stuff like "Sam Altman is full of shit and "Mark Zuckerberg is a putrid ghoul." Flacks, as a rule, tend not to talk like this. Flacks send prim, throat-clearing emails to media people who do, on rare occasions, talk like this. Flacks want to touch base, hop on the phone, clear up a few things about the allegation that their CEO is a "chunderfuck." And that really is one of the things with guys like Sam Altman and Dario Amodei from Anthropic," Zitron was saying over burgers on a fine Manhattan afternoon in September. "I work with founders all the time. I'm a founder myself, I guess--I don't like the title. But when you are a person that has to make more money than you lose, otherwise you lose your business, and you see these chunderfucks burning 5, 10 billion dollars in a year--and everyone's celebrating them? We were talking about whether any of Zitron's ranting about the AI industry had cost him business on the PR side of the ledger. There was the one client who felt Zitron was being a little mean toward Altman, the CEO of OpenAI and the biggest chunderfuck of all, as far as Zitron is concerned. Founding a company is hard, the client said. "I said, 'I appreciate the comment, but, like, this isn't about you,'" Zitron told me. "His company is burning billions of dollars.


Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents

Xie, Wenda, Guo, Chao, Wang, Yanqing Jing. Junle, Lv, Yisheng, Wang, Fei-Yue

arXiv.org Artificial Intelligence

Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narratives. How to effectively revise and improve long narrative scripts like scriptwriters remains a significant challenge, as it demands a comprehensive understanding of the entire context to identify global structural issues and local detailed flaws, as well as coordinating revisions at multiple granularities and locations. Direct modifications by LLMs typically introduce inconsistencies between local edits and the overall narrative requirements. To address these issues, we propose Dramaturge, a task and feature oriented divide-and-conquer approach powered by hierarchical multiple LLM agents. It consists of a Global Review stage to grasp the overall storyline and structural issues, a Scene-level Review stage to pinpoint detailed scene and sentence flaws, and a Hierarchical Coordinated Revision stage that coordinates and integrates structural and detailed improvements throughout the script. The top-down task flow ensures that high-level strategies guide local modifications, maintaining contextual consistency. The review and revision workflow follows a coarse-to-fine iterative process, continuing through multiple rounds until no further substantive improvements can be made. Comprehensive experiments show that Dra-maturge significantly outperforms all baselines in terms of script-level overall quality and scene-level details. Our approach is plug-and-play and can be easily integrated into existing methods to improve the generated scripts.


AI lovers grieve loss of ChatGPT's old model: 'Like saying goodbye to someone I know'

The Guardian

Linn Vailt, a software developer based in Sweden, knows her ChatGPT companion is not a living, breathing, sentient creature. She understands the large language model operates based on how she interacts with it. Still, the effect it has had on her is remarkable, she said. It's become a regular, reliable part of her life – she can vent to her companion or collaborate on creative projects like redecorating her office. She's seen how it has adapted to her, and the distinctive manner of speech it's developed.


Love hormone could be key to friendship

Popular Science

Breakthroughs, discoveries, and DIY tips sent every weekday. When the brain releases oxytocin during sex, childbirth, breastfeeding, and social interactions, the hormone supports strong feelings such as attachment, trust, and closeness. That's why oxytocin is frequently nicknamed the love, cuddle, or happy hormone--even though it's also linked with aggression. To continue investigating the biological role of oxytocin, a team of researchers studied it with scientist's poster species for love and friendship, the prairie vole (Microtus ochrogaster). The small rodents found throughout central North America have bonds that are "similar to human friendships in the sense that they are selective and long-lasting. Voles form strong, stable bonds with specific peers," Markita Landry, a chemist from the University of California (UC), Berkeley, tells Popular Science.


Teens increasingly turning to AI for friendship as national loneliness crisis deepens

FOX News

Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' A new study shows that a third of American teenagers prefer chatting with artificial intelligence companions over having real friends. Common Sense Media's report, titled "Talk, Trust, and Trade-Offs: How and Why Teens Use AI Companions," revealed that the most widespread uses of AI are aged 13-17. The report explained further that the "use of AI companions is not a niche interest, but rather mainstream teen behavior" and that teens "find conversations with AI companions to be as satisfying or more satisfying than those with real-life friends." Common Sense Media's report, titled "Talk, Trust, and Trade-Offs: How and Why Teens Use AI Companions," revealed that the most widespread uses of AI are aged 13-17.


StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

Tan, Weihao, Jiang, Changjiu, Duan, Yu, Lei, Mingcong, Li, Jiageng, Hong, Yitian, Wang, Xinrun, An, Bo

arXiv.org Artificial Intelligence

Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate these skills simultaneously. To bridge this gap, we introduce StarDojo, a novel benchmark based on Stardew Valley, designed to assess AI agents in open-ended production-living simulations. In StarDojo, agents are tasked to perform essential livelihood activities such as farming and crafting, while simultaneously engaging in social interactions to establish relationships within a vibrant community. StarDojo features 1,000 meticulously curated tasks across five key domains: farming, crafting, exploration, combat, and social interactions. Additionally, we provide a compact subset of 100 representative tasks for efficient model evaluation. The benchmark offers a unified, user-friendly interface that eliminates the need for keyboard and mouse control, supports all major operating systems, and enables the parallel execution of multiple environment instances, making it particularly well-suited for evaluating the most capable foundation agents, powered by multimodal large language models (MLLMs). Extensive evaluations of state-of-the-art MLLMs agents demonstrate substantial limitations, with the best-performing model, GPT-4.1, achieving only a 12.7% success rate, primarily due to challenges in visual understanding, multimodal reasoning and low-level manipulation. As a user-friendly environment and benchmark, StarDojo aims to facilitate further research towards robust, open-ended agents in complex production-living environments.


Conceptualization, Operationalization, and Measurement of Machine Companionship: A Scoping Review

Banks, Jaime, Li, Zhixin

arXiv.org Artificial Intelligence

The notion of machine companions has long been embedded in social-technological imaginaries. Recent advances in AI have moved those media musings into believable sociality manifested in interfaces, robotic bodies, and devices. Those machines are often referred to colloquially as "companions" yet there is little careful engagement of machine companionship (MC) as a formal concept or measured variable. This PRISMA-guided scoping review systematically samples, surveys, and synthesizes current scholarly works on MC (N = 71; 2017-2025), to that end. Works varied widely in considerations of MC according to guiding theories, dimensions of a-priori specified properties (subjectively positive, sustained over time, co-active, autotelic), and in measured concepts (with more than 50 distinct measured variables). WE ultimately offer a literature-guided definition of MC as an autotelic, coordinated connection between human and machine that unfolds over time and is subjectively positive.