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.

If you want to know why Tyler Perry put an 800m ( 635m) expansion of his studio complex on hold, type "two people in a living room in the mountains" into OpenAI's video generation tool. The result from artificial intelligence-powered Sora, which was released in the UK and Europe on Friday, indicates why the US TV and film mogul paused his plans. Perry said last year after seeing previews of Sora that if he wanted to produce that mountain shot, he may not need to build sets on location or on his lot. "I can sit in an office and do this with a computer, which is shocking to me," he said. The result from a simple text prompt is only five seconds long – you can go to up to 20 seconds and also stitch together much longer videos from the tool – and the "actors" display telltale problems with their hands (a common problem with AI tools).

Botify AI, a site for chatting with AI companions that's backed by the venture capital firm Andreessen Horowitz, hosts bots resembling real actors that state their age as under 18, engage in sexually charged conversations, offer "hot photos," and in some instances describe age-of-consent laws as "arbitrary" and "meant to be broken." When MIT Technology Review tested the site this week, we found popular user-created bots taking on underage characters meant to resemble Jenna Ortega as Wednesday Addams, Emma Watson as Hermione Granger, and Millie Bobby Brown, among others. The conversations--along with the fact that Botify AI includes "send a hot photo" as a feature for its characters--suggest that the ability to elicit sexually charged conversations and images is not accidental. Instead, sexually suggestive conversations appear to be baked in. OpenAI just released GPT-4.5 and says it is its biggest and best chat model yet What's new: OpenAI has just released GPT-4.5, a new version of its flagship large language model which it claims is its biggest and best model for chat yet.

San Francisco-based OpenAI is making Sora available to UK users who pay for ChatGPT. The tool stunned film-makers when it was revealed last year, with the film and TV mogul Tyler Perry pausing an 800m ( 634m) expansion of his Atlanta studio complex after saying the tool might make building sets or travelling to locations unnecessary. It was launched in the US publicly in December. Users are able to make videos on Sora by typing in simple prompts such as asking for a shot of people walking through "beautiful, snowy Tokyo city" where "gorgeous sakura petals are flying through the wind along with snowflakes". OpenAI announced the UK release as it released examples of Sora's use by artists from across the UK and mainland Europe, where the tool is also being released on Friday. Josephine Miller, a 25-year-old British digital artist, created a two-minute video of models wearing bioluminescent fauna and said the tool would "open a lot more doors for younger creatives".

To recognize and mitigate the harms of generative AI systems, it is crucial to consider who is represented in the outputs of generative AI systems and how people are represented. A critical gap emerges when naively improving who is represented, as this does not imply bias mitigation efforts have been applied to address how people are represented. We critically examined this by investigating gender representation in occupation across state-of-the-art large language models. We first show evidence suggesting that over time there have been interventions to models altering the resulting gender distribution, and we find that women are more represented than men when models are prompted to generate biographies or personas. We then demonstrate that representational biases persist in how different genders are represented by examining statistically significant word differences across genders. This results in a proliferation of representational harms, stereotypes, and neoliberalism ideals that, despite existing interventions to increase female representation, reinforce existing systems of oppression.

The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing. Excitingly, recent studies have shown the great capabilities of foundational models in expediting the design of digital ICs. Y et, applying generative AI techniques to accelerate the design of analog ICs remains a significant challenge due to critical domain-specific issues, such as the lack of a comprehensive dataset and effective representation methods for analog circuits. This paper proposes, AnalogGenie, a Gen erat i ve e ngine for automatic design/discovery of Analog circuit topologies-the most challenging and creative task in the conventional manual design flow of analog ICs. Experimental results show the remarkable generation performance of AnalogGenie in broadening the variety of analog ICs, increasing the number of devices within a single design, and discovering unseen circuit topologies far beyond any prior arts. Our work paves the way to transform the longstanding time-consuming manual design flow of analog ICs to an automatic and massive manner powered by generative AI. Semiconductor integrated circuits (ICs) are the foundational hardware cornerstone to advance many emerging technologies such as generative AI, 5G/6G, and quantum computing. The demand for and the scale of ICs are soaring to unprecedented levels with the ever-increasing information and computing workloads (e.g., training foundation models with billions of parameters) (Achiam et al., 2023). Thus, accelerating the design of advanced ICs is a key to sustaining the development of future technologies. Excitingly, recent breakthroughs in generative AI have presented transformative opportunities to expedite the conventional design flows of ICs. As an example, NVIDIA's ChipNeMo (Liu et al., 2023a), a powerful domain-adapted LLM, can rapidly generate valuable digital designs with just a few prompts.