Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning seems to be a direct solution, it requires substantial computational resources and may significantly affect the utility of the original LLM.

This research investigates the use of customized GPT models to enhance prompting proficiency among architecture students when generating AI-driven images. Prompt engineering is increasingly essential in architectural education due to the widespread adoption of generative AI tools. This study utilized a mixed-methods experimental design involving architecture students divided into three distinct groups: a control group receiving no structured support, a second group provided with structured prompting guides, and a third group supported by both structured guides and interactive AI personas. Students engaged in reverse engineering tasks, first guessing provided image prompts and then generating their own prompts, aiming to boost critical thinking and prompting skills. Variables examined included time spent prompting, word count, prompt similarity, and concreteness. Quantitative analysis involved correlation assessments between these variables and a one-way ANOVA to evaluate differences across groups. While several correlations showed meaningful relationships, not all were statistically significant. ANOVA results indicated statistically significant improvements in word count, similarity, and concreteness, especially in the group supported by AI personas and structured prompting guides. Qualitative feedback complemented these findings, revealing enhanced confidence and critical thinking skills in students. These results suggest tailored GPT interactions substantially improve students' ability to communicate architectural concepts clearly and effectively.

American police departments near the United States-Mexico border are paying hundreds of thousands of dollars for an unproven and secretive technology that uses AI-generated online personas designed to interact with and collect intelligence on "college protesters," "radicalized" political activists, and suspected drug and human traffickers, according to internal documents, contracts, and communications that 404 Media obtained via public records requests. This article is copublished in partnership with 404 Media. Massive Blue, the New York–based company that is selling police departments this technology, calls its product Overwatch, which it markets as an "AI-powered force multiplier for public safety" that "deploys lifelike virtual agents, which infiltrate and engage criminal networks across various channels." According to a presentation obtained by 404 Media, Massive Blue is offering cops these virtual personas that can be deployed across the internet with the express purpose of interacting with suspects over text messages and social media. The technology--which as of last summer had not led to any known arrests--demonstrates the types of social media monitoring and undercover tools private companies are pitching to police and border agents.

This study introduces GreenIQ, an AI-powered deep search platform designed to revolutionise carbon market intelligence through autonomous analysis and automated report generation. Carbon markets operate across diverse regulatory landscapes, generating vast amounts of heterogeneous data from policy documents, industry reports, academic literature, and real-time trading platforms. Traditional research approaches remain labour-intensive, slow, and difficult to scale. GreenIQ addresses these limitations through a multi-agent architecture powered by Large Language Models (LLMs), integrating five specialised AI agents: a Main Researcher Agent for intelligent information retrieval, a Report Writing Agent for structured synthesis, a Final Reviewer Agent for accuracy verification, a Data Visualisation Agent for enhanced interpretability, and a Translator Agent for multilingual adaptation. The system achieves seamless integration of structured and unstructured information with AI-driven citation verification, ensuring high transparency and reliability. GreenIQ delivers a 99.2\% reduction in processing time and a 99.7\% cost reduction compared to traditional research methodologies. A novel AI persona-based evaluation framework involving 16 domain-specific AI personas highlights its superior cross-jurisdictional analytical capabilities and regulatory insight generation. GreenIQ sets new standards in AI-driven research synthesis, policy analysis, and sustainability finance by streamlining carbon market research. It offers an efficient and scalable framework for environmental and financial intelligence, enabling more accurate, timely, and cost-effective decision-making in complex regulatory landscapes

Meta has nuked a bunch of its AI-generated profiles from Facebook Instagram, the company confirmed, after the AI characters prompted widespread outrage and ridicule from users on social media. The AI-generated profiles, which were labeled as "AI managed by Meta," launched in September of 2023, rolling out alongside the company's celebrity-branded AI chatbots ( also discontinued). Meta doesn't seem to have updated any of these profiles for several months, and the pages seem to have been largely unnoticed until this week, following an interview published by the Financial Times with Meta's VP of Generative AI, Connor Hayes. In the interview, Hayes spoke about the company's goal to eventually fill its services with AI-generated profiles that can interact with people and function "kind of in the same way that accounts do." Those comments brought attention to the extant fMeta-created AI profiles and, well, users were not exactly impressed with what they found.

Drew Crecente's daughter died in 2006, killed by an ex-boyfriend in Austin, Texas, when she was just 18. Her murder was highly publicized--so much so that Drew would still occasionally see Google alerts for her name, Jennifer Ann Crecente. The alert Drew received a few weeks ago wasn't the same as the others. It was for an AI chatbot, created in Jennifer's image and likeness, on the buzzy, Google-backed platform Character.AI. Jennifer's internet presence, Drew Crecente learned, had been used to create a "friendly AI character" that posed, falsely, as a "video game journalist."

ABSTRACT This report analyzes the potential for large language models (LLMs) to expedite accurate replication of published message effects studies. We tested LLM-powered participants (personas) by replicating 133 experimental findings from 14 papers containing 45 recent studies in the Journal of Marketing (January 2023-May 2024). We used a new software tool, Viewpoints AI (https://viewpoints.ai/), that takes study designs, stimuli, and measures as input, automatically generates prompts for LLMs to act as a specified sample of unique personas, and collects their responses to produce a final output in the form of a complete dataset and statistical analysis. The underlying LLM used was Anthropic's Claude Sonnet 3.5. We generated 19,447 AI personas to replicate these studies with the exact same sample attributes, study designs, stimuli, and measures reported in the original human research. Our LLM replications successfully reproduced 76% of the original main effects (84 out of 111), demonstrating strong potential for AI-assisted replication of studies in which people respond to media stimuli. When including interaction effects, the overall replication rate was 68% (90 out of 133). The use of LLMs to replicate and accelerate marketing research on media effects is discussed with respect to the replication crisis in social science, potential solutions to generalizability problems in sampling subjects and experimental conditions, and the ability to rapidly test consumer responses to various media stimuli. We also address the limitations of this approach, particularly in replicating complex interaction effects in media response studies, and suggest areas for future research and improvement in AI-assisted experimental replication of media effects. STUDY OVERVIEW AND RELATED WORK Research about the effectiveness of media messages is increasingly difficult, attributable to both administrative challenges (e.g., stimulus acquisition and creation, data management demands of digital trace data, acquisition of participants and especially those in special groups like children, minorities and international groups), as well as requirements to deal with new and critical challenges to the very nature of social research, as exemplified by existential issues of replication and reproducibility, and the ability to generalize findings across people, media stimuli and experimental contexts. We briefly review these issues with an eye toward our current test of whether new LLM tools may help solve the problems mentioned, and with significant advantages in cost, time, and research personnel.

Will.i.am has been embracing innovative technology for years. Now he is using artificial intelligence in an effort to transform how we listen to the radio. The musician, entrepreneur and tech investor has launched RAiDiO.FYI, a set of interactive radio stations themed around topics like sport, pop culture, and politics. Each station is fundamentally interactive: tune in and you'll be welcomed by name by an AI host "live from the ether," the Black Eyed Peas frontman tells TIME. Hosts talk about their given topic before playing some music.

When Christi Angel first talked to a chatbot impersonating her deceased partner, Cameroun, she found the encounter surreal and "very weird". "Yes, I knew it was an AI system but, once I started chatting, my feeling was I was talking to Cameroun. That's how real it felt to me," she says. Angel's conversation with "Cameroun" took a more sinister turn when the persona assumed by the chatbot said he was "in hell". Angel, a practising Christian, found the exchange upsetting and returned a second time seeking a form of closure, which the chatbot provided.