Generative AI models, such as ChatGPT, will increasingly replace humans in producing output for a variety of important tasks. While much prior work has mostly focused on the improvement in the average performance of generative AI models relative to humans' performance, much less attention has been paid to the significant reduction of variance in output produced by generative AI models. In this Perspective, we demonstrate that generative AI models are inherently prone to the phenomenon of "regression toward the mean" whereby variance in output tends to shrink relative to that in real-world distributions. We discuss potential social implications of this phenomenon across three levels-societal, group, and individual-and two dimensions-material and non-material. Finally, we discuss interventions to mitigate negative effects, considering the roles of both service providers and users. Overall, this Perspective aims to raise awareness of the importance of output variance in generative AI and to foster collaborative efforts to meet the challenges posed by the reduction of variance in output generated by AI models.

We are witnessing a new era where problem-solving and cognitive tasks are being increasingly delegated to Large Language Models (LLMs) across diverse domains, ranging from code generation to holiday planning. This trend also creates a demand for the ubiquitous execution of LLM-powered applications in a wide variety of environments in which traditional terrestrial 2D networking infrastructures may prove insufficient. A promising solution in this context is to extend edge computing into a 3D setting to include aerial platforms organized in multiple layers, a paradigm we refer to as air computing, to augment local devices for running LLM and Generative AI (GenAI) applications. This approach alleviates the strain on existing infrastructure while enhancing service efficiency by offloading computational tasks to the corresponding air units such as UAVs. Furthermore, the coordinated deployment of various air units can significantly improve the Quality of Experience (QoE) by ensuring seamless, adaptive, and resilient task execution. In this study, we investigate the synergy between LLM-based applications and air computing, exploring their potential across various use cases. Additionally, we present a disaster response case study demonstrating how the collaborative utilization of LLMs and air computing can significantly improve outcomes in critical situations.

Large language models demonstrate advanced problem-solving capabilities by incorporating reasoning techniques such as chain of thought and reflection. However, how these reasoning capabilities extend to social intelligence remains unclear. In this study, we investigate this question using economic games that model social dilemmas, where social intelligence plays a crucial role. First, we examine the effects of chain-of-thought and reflection techniques in a public goods game. We then extend our analysis to six economic games on cooperation and punishment, comparing off-the-shelf non-reasoning and reasoning models. We find that reasoning models significantly reduce cooperation and norm enforcement, prioritizing individual rationality. Consequently, groups with more reasoning models exhibit less cooperation and lower gains through repeated interactions. These behaviors parallel human tendencies of "spontaneous giving and calculated greed." Our results suggest the need for AI architectures that incorporate social intelligence alongside reasoning capabilities to ensure that AI supports, rather than disrupts, human cooperative intuition. Recent innovations in reasoning techniques, such as chain of thought [1] and reflection [2], are advancing the intellectual capabilities of large language models (LLMs) to the next level. Models such as OpenAI o1 leverage these techniques to solve complex problems, generate coherent arguments, and improve decision-making in multi-step reasoning scenarios [3-5]. Indeed, these reasoning models have demonstrated excellence in mathematical proofs, logical deduction, and strategic planning [6, 7]. The necessity of social intelligence is highlighted in social dilemmas, where individual rationality leads to collective irrationality [12].

Amazon's long-awaited update to its assistant is almost here. About 18 months after the company first previewed the "next-gen Alexa" built with generative AI, it unveiled Alexa, and early access will be available starting in March. Alexa will exist alongside the older Alexa and will cost 20 a month, unless you have a Prime membership, which will make it free to use. The new assistant will come with all the modern upgrades that its contemporaries like the redesigned Siri or Gemini offer, like more conversational interaction, better contextual understanding and the ability to "summarize complex topics" and "make suggestions based on your interests." But it does one thing differently, and it's the way Amazon purports to integrate with third-party apps and the rest of the internet that could set it apart.

Jules White used to believe his 11-year-old son needed to know how to code to be successful. Now, though, the Vanderbilt computer science professor says it's more crucial for James to learn a new, more useful skill: how to prompt artificial intelligence (AI) chatbots. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. Since OpenAI released ChatGPT in 2022, White has been showing his son the ropes of generative AI.

To address these concerns, many Higher Education Institutions ( HEI s) have released institutional gui dance for researchers . To better understand the guidance that is being provided we report findings from a thematic analysis of guidelines from thirty HEIs in the United States that are classified as R1 or "very high research activity. " We found that guidance provided to researchers: 1) asks them to refer to external sources of information such as funding agencies and publishers to keep updated and use institutional resources for training and education; 2) asks them to understand and learn about specific GenAI attributes that shape research such as predictive modeling, knowledge cutoff date, data provenance, and model limitations, and about ethical concerns such as authorship, attribution, privacy, and intellectual property issues; 3) incl udes instructions on how to acknowledge sources and disclose the use of GenAI, and how to communicate effectively about their GenAI use, and alerts researchers to long term implications such as over reliance on GenAI, legal consequences, and risks to their institutions from GenAI use. Overall, g uidance places the onus of compliance on individual researchers making them accountable for any lapses, thereby increasing their responsibility. Keywords: Generative Artificial Intelligence; Academic Research, Thematic Analysis, Policy and Guidance, Qualitative Data Analysis, Framework 1 Introduction As the use of generative artificial intelligence (GenAI) increases across all facets of society, one area of significant impact is higher education institutions (HEIs). Although the initial scholarship on the use of GenAI within HEIs has focused on teaching and learning (McDonald et al., 202 5; Ali et al., 2025) increasingly, studies are starting to examine how academic research is being impacted by GenAI ( Abernethy, 2024; Lehr, et al., 2024; Lin, 2024; Liu and Jagadish, 2024; Godwin et al., 2024) This shift is in keeping with increased uptake of the use of GenAI for research. GenAI has many potential benefits for researchers across different stages of the research process such as data analysis, creation of content for research dissemination, and as a tool to brainstorm new ideas (Joosten et al., 2024) For instance, Delios et al. (2024) report that almost 30% of scientists are using GenAI as partners in their tasks related to research such as summarizing l iterature review, data analysis, grant writing and assisting with other aspects of manuscript preparation (Morocco - Clarke et al., 2024; Xames and Shefa, 2023). In a 2023 Nature survey of 1600 scientists, 30% acknowledged that they used GenAI to write acade mic papers, conduct literature reviews, and/or develop grant applications (Chawla, 2024).

This paper aims to present new alarming trends in the field of child sexual abuse through imagery, as part of SafeLine's research activities in the field of cybercrime, child sexual abuse material and the protection of children's rights to safe online experiences. It focuses primarily on the phenomenon of AI-generated CSAM, sophisticated ways employed for its production which are discussed in dark web forums and the crucial role that the open-source AI models play in the evolution of this overwhelming phenomenon. The paper's main contribution is a correlation analysis between the hotline's reports and domain names identified in dark web forums, where users' discussions focus on exchanging information specifically related to the generation of AI-CSAM. The objective was to reveal the close connection of clear net and dark web content, which was accomplished through the use of the ATLAS dataset of the Voyager system. Furthermore, through the analysis of a set of posts' content drilled from the above dataset, valuable conclusions on forum members' techniques employed for the production of AI-generated CSAM are also drawn, while users' views on this type of content and routes followed in order to overcome technological barriers set with the aim of preventing malicious purposes are also presented. As the ultimate contribution of this research, an overview of the current legislative developments in all country members of the INHOPE organization and the issues arising in the process of regulating the AI- CSAM is presented, shedding light in the legal challenges regarding the regulation and limitation of the phenomenon.

Recently, Devin has made a significant buzz in the Artificial Intelligence (AI) community as the world's first fully autonomous AI software engineer, capable of independently developing software code [1] [2]. Devin uses the concept of agentic workflow in Generative AI (GenAI), which empowers AI agents to engage in a more dynamic, iterative, and self-reflective process. With Saarthi, verification engineers can focus on more complex problems, and verification teams can strive for more ambitious goals. The domain-agnostic implementation of Saarthi makes it scalable for use across various domains such as RTL design, UVM-based verification, and others. Hardware design verification, especially formal verification, entails a methodical and disciplined approach to the planning, development, execution, and sign-off of functionally correct hardware designs. Formal verification uses mathematical methods to prove the correctness of hardware designs against their specifications, ensuring that all possible states and inputs are considered, which complements traditional simulation-based verification techniques that might only cover a subset of possible scenarios due to practical constraints [3]. The formal verification process encompasses several key roles, such as organizational coordination, task allocation, code development, property proving, analyzing Counter Examples (CEXs), debugging, coverage closure, and documentation preparation. These roles are crucial for managing the complexity and ensuring the thoroughness of the verification process. For instance, analyzing counterexamples involves identifying specific scenarios where the design might fail to meet its specifications, which is critical for debugging and refining the design. This highly intricate activity demands meticulous attention to detail, given its long development cycles and the critical nature of ensuring hardware functionality and reliability [4]. The field of Natural Language Processing (NLP) has undergone a significant transformation with the advent of Large Language Models (LLMs) [5].

OpenAI has unveiled its latest AI model, GPT-4.5, but the firm's boss says it is running out of hardware to power it. If ever-larger AI can no longer be run at scale, then are we looking at the end of the technology's rapid progress, and perhaps even the bursting of a bubble? There are certainly signs that things aren't going as planned within OpenAI. As recently as 12 February, CEO Sam Altman acknowledged on X that the company's product offering had created a confusing picture – at the…

You can't find a new Nvidia graphics card for love nor money. Between pent-up demand from PC gamers and Nvidia selling every GPU it can to the bubbling AI industry, new models are going out of stock in a matter of minutes -- and it looks like the situation isn't going to improve any time soon, as the biggest AI company around wants even more hardware. OpenAI CEO Sam Altman took to the social network formerly known as Twitter (spotted by Tom's Hardware) to say that OpenAI's ChatGPT version 4.5 is ready to go… but desperately in need of even more hardware. The "giant, expensive model" requires even more data center capacity than older versions, and to launch with enough access for paid users, the company is gobbling up GPUs at an even faster rate. The CEO claims that OpenAI is adding "tens of thousands of GPUs next week" for the planned rollout, with hundreds of thousands following soon after.