'We May Have a Crisis on Our Hands': The Unregulated Rise of Emotionally Intelligent AI Pillay is an editorial fellow at TIME. Pillay is an editorial fellow at TIME. At least once a month, two-thirds of people who regularly use AI turn to their bots for advice on sensitive personal issues and emotional support. Many people now report trusting their chatbots more than their elected representatives, civil servants, faith leaders--and the companies building AI. That's according to data from 70 countries, gathered by the Collective Intelligence Project (CIP).

Bernie Sanders criticizes AI as'the most consequential technology in humanity' US senator Bernie Sanders amplified his recent criticism of artificial intelligence on Sunday, explicitly linking the financial ambition of "the richest people in the world" to economic insecurity for millions of Americans - and calling for a potential moratorium on new datacenters . Sanders, a Vermont independent who caucuses with the Democratic party, said on CNN's State of the Union that he was "fearful of a lot" when it came to AI. And the senator called it "the most consequential technology in the history of humanity" that will "transform" the US and the world in ways that had not been fully discussed. "If there are no jobs and humans won't be needed for most things, how do people get an income to feed their families, to get healthcare or to pay the rent?" Sanders said. "There's not been one serious word of discussion in the Congress about that reality."

AISI's report also found chatbots could sway political opinions but often delivered substantial amounts of inaccurate information. AISI's report also found chatbots could sway political opinions but often delivered substantial amounts of inaccurate information. A third of UK citizens have used artificial intelligence for emotional support, companionship or social interaction, according to the government's AI security body. The AI Security Institute (AISI) said nearly one in 10 people used systems like chatbots for emotional purposes on a weekly basis, and 4% daily. AISI called for further research, citing the death this year of the US teenager Adam Raine, who killed himself after discussing suicide with ChatGPT.

Large Language Models (LLMs) demonstrate increasing conversational fluency, yet instilling them with nuanced, human-like emotional expression remains a significant challenge. Current alignment techniques often address surface-level output or require extensive fine-tuning. This paper demonstrates that targeted activation engineering can steer LLaMA 3.1-8B to exhibit more human-like emotional nuances. We first employ attribution patching to identify causally influential components, to find a key intervention locus by observing activation patterns during diagnostic conversational tasks. We then derive emotional expression vectors from the difference in the activations generated by contrastive text pairs (positive vs. negative examples of target emotions). Applying these vectors to new conversational prompts significantly enhances emotional characteristics: steered responses show increased positive sentiment (e.g., joy, trust) and more frequent first-person pronoun usage, indicative of greater personal engagement. Our findings offer a precise and interpretable framework and new directions for the study of conversational AI.

Loneliness and social isolation pose significant emotional and health challenges, prompting the development of technology-based solutions for companionship and emotional support. This paper introduces Livia, an emotion-aware augmented reality (AR) companion app designed to provide personalized emotional support by combining modular artificial intelligence (AI) agents, multimodal affective computing, progressive memory compression, and AR driven embodied interaction. Livia employs a modular AI architecture with specialized agents responsible for emotion analysis, dialogue generation, memory management, and behavioral orchestration, ensuring robust and adaptive interactions. Two novel algorithms-Temporal Binary Compression (TBC) and Dynamic Importance Memory Filter (DIMF)-effectively manage and prioritize long-term memory, significantly reducing storage requirements while retaining critical context. Our multimodal emotion detection approach achieves high accuracy, enhancing proactive and empathetic engagement. User evaluations demonstrated increased emotional bonds, improved satisfaction, and statistically significant reductions in loneliness. Users particularly valued Livia's adaptive personality evolution and realistic AR embodiment. Future research directions include expanding gesture and tactile interactions, supporting multi-user experiences, and exploring customized hardware implementations.

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.

Since the all-new ChatGPT launched on Thursday, some users have mourned the disappearance of a peppy and encouraging personality in favor of a colder, more businesslike one (a move seemingly designed to reduce unhealthy user behavior.) The backlash shows the challenge of building artificial intelligence systems that exhibit anything like real emotional intelligence. Researchers at MIT have proposed a new kind of AI benchmark to measure how AI systems can manipulate and influence their users--in both positive and negative ways--in a move that could perhaps help AI builders avoid similar backlashes in the future while also keeping vulnerable users safe. Most benchmarks try to gauge intelligence by testing a model's ability to answer exam questions, solve logical puzzles, or come up with novel answers to knotty math problems. As the psychological impact of AI use becomes more apparent, we may see MIT propose more benchmarks aimed at measuring more subtle aspects of intelligence as well as machine-to-human interactions.

Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing conversations and providing emotional support as separate research directions. However, there remains a significant research gap in combining these capabilities to enable emotionally supportive interactions with virtual characters. To address this research gap, we focus on anime characters as a case study because of their well-defined personalities and large fan bases. This choice enables us to effectively evaluate how well LLMs can provide emotional support while maintaining specific character traits. We introduce ChatAnime, the first Emotionally Supportive Role-Playing (ESRP) dataset. We first thoughtfully select 20 top-tier characters from popular anime communities and design 60 emotion-centric real-world scenario questions. Then, we execute a nationwide selection process to identify 40 Chinese anime enthusiasts with profound knowledge of specific characters and extensive experience in role-playing. Next, we systematically collect two rounds of dialogue data from 10 LLMs and these 40 Chinese anime enthusiasts. To evaluate the ESRP performance of LLMs, we design a user experience-oriented evaluation system featuring 9 fine-grained metrics across three dimensions: basic dialogue, role-playing and emotional support, along with an overall metric for response diversity. In total, the dataset comprises 2,400 human-written and 24,000 LLM-generated answers, supported by over 132,000 human annotations. Experimental results show that top-performing LLMs surpass human fans in role-playing and emotional support, while humans still lead in response diversity. We hope this work can provide valuable resources and insights for future research on optimizing LLMs in ESRP. Our datasets are available at https://github.com/LanlanQiu/ChatAnime.

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.

A Microsoft executive is facing backlash after suggesting that recently laid-off employees use artificial intelligence to cope with unemployment. The company cut approximately 9,000 jobs last week, many in its gaming division, as it continues to shift focus and invest tens of billions of dollars into artificial intelligence. Matt Turnbull, an executive producer at Xbox Game Studios Publishing, posted AI-generated prompts to LinkedIn offering laid-off workers help with career planning, résumé writing, and even emotional support. The post, which has since been deleted, sparked swift criticism across social media where users called him'out of touch' and'tone-deaf.' 'These are really challenging times,' Turnbull wrote, encouraging displaced employees to use chatbots to help manage feelings of impostor syndrome and reframe their layoff experiences in a more positive light. His message included suggestions for using AI to develop 30-day job search plans and tailor résumés for different industries.