OpenAI has hired four high-profile engineers away from rivals, including David Lau, former vice president of software engineering at Tesla, to join the company's scaling team, WIRED has learned. The news came via an internal Slack message sent by OpenAI cofounder Greg Brockman on Tuesday. Lau is joined by Uday Ruddarraju, the former head of infrastructure engineering at xAI and X, Mike Dalton, an infrastructure engineer from xAI, and Angela Fan, an AI researcher from Meta. Both Dalton and Ruddarraju also previously worked at Robinhood. At xAI, Ruddarraju worked on building Colossus, a massive supercomputer comprising more than 200,000 GPUs.

This week in tech news, Elon Musk and Donald Trump are back at it, warring over the passage of the president's sweeping tax bill and the Tesla CEO's threat to create a third political party. Whether the richest person in the world is successful in those efforts will largely depend on the recruitment of another star politician. In other news, we want to know if you use generative artificial intelligence to write your personal messages – in what circumstances, and how often? Email tech.editorial@theguardian.com to let us know. Elon Musk and Donald Trump have reignited their feud after the passage of the president's sweeping tax bill on 3 July.

In March, NewsGuard – a company that tracks misinformation – published a report claiming that generative Artificial Intelligence (AI) tools, such as ChatGPT, were amplifying Russian disinformation. NewsGuard tested leading chatbots using prompts based on stories from the Pravda network – a group of pro-Kremlin websites mimicking legitimate outlets, first identified by the French agency Viginum. The results were alarming: Chatbots "repeated false narratives laundered by the Pravda network 33 percent of the time", the report said. The Pravda network, which has a rather small audience, has long puzzled researchers. Some believe that its aim was performative – to signal Russia's influence to Western observers.

Microsoft and OpenAI are planning to announce Tuesday that they are helping to launch an AI training center for members of the second-largest teachers' union in the US, according to details about the initiative that appear to have been inadvertently published early on YouTube. The National Academy for AI Instruction will be based in New York City and aims to equip kindergarten up to 12th grade instructors in the American Federation of Teachers with "the tools and confidence to bring AI into the classroom in a way that supports learning and opportunity for all students," according to the description of a publicly accessible YouTube livestream scheduled for Tuesday morning. The YouTube page also lists Anthropic, which develops the Claude chatbot, as a collaborator on what's described as a 22.5 million initiative to bring free "AI training and curriculum" to teachers. The three AI companies and the union did not immediately respond to requests for comment about the information released on YouTube. On Monday, Microsoft and the union declined to share details ahead of an announcement planned for Tuesday morning in New York.

Republican strategist Matt Keelen and Democratic strategist Fred Hicks debate how passing the'big, beautiful bill' will impact the macroeconomy and the upcoming midterm election cycle. Should we be alarmed by the acceleration of "artificial intelligence" ("AI") and the "large language models" (LLMs) AI's developers employ? Thanks to AI I can provide a short explanation of the LLM term: "Imagine AI as a large umbrella, with generative AI being a smaller umbrella underneath. LLMs are like a specific type of tool within the generative AI umbrella, designed for working with text." The intricacies of AI and the tools it uses are the stuff of start-ups, engineers, computer scientists and the consumers feeding them data knowingly or unknowingly.

Writing longer prompts for an AI assistant to generate a short story increases psychological ownership, a user's feeling that the writing belongs to them. To encourage users to write longer prompts, we evaluated two interaction techniques that modify the prompt entry interface of chat-based generative AI assistants: pressing and holding the prompt submission button, and continuously moving a slider up and down when submitting a short prompt. A within-subjects experiment investigated the effects of such techniques on prompt length and psychological ownership, and results showed that these techniques increased prompt length and led to higher psychological ownership than baseline techniques. A second experiment further augmented these techniques by showing AI-generated suggestions for how the prompts could be expanded. This further increased prompt length, but did not lead to improvements in psychological ownership. Our results show that simple interface modifications like these can elicit more writing from users and improve psychological ownership.

Translating nuanced, textually-defined authorial writing styles into compelling visual representations presents a novel challenge in generative AI. This paper introduces a pipeline that leverages Author Writing Sheets (AWS) - structured summaries of an author's literary characteristics - as input to a Large Language Model (LLM, Claude 3.7 Sonnet). The LLM interprets the AWS to generate three distinct, descriptive text-to-image prompts, which are then rendered by a diffusion model (Stable Diffusion 3.5 Medium). We evaluated our approach using 49 author styles from Reddit data, with human evaluators assessing the stylistic match and visual distinctiveness of the generated images. Results indicate a good perceived alignment between the generated visuals and the textual authorial profiles (mean style match: $4.08/5$), with images rated as moderately distinctive. Qualitative analysis further highlighted the pipeline's ability to capture mood and atmosphere, while also identifying challenges in representing highly abstract narrative elements. This work contributes a novel end-to-end methodology for visual authorial style personalization and provides an initial empirical validation, opening avenues for applications in creative assistance and cross-modal understanding.

This survey study investigates how digital humanists perceive and approach generative AI disclosure in research. The results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low. Respondents differ in their views on which activities most require disclosure and on the most appropriate methods for doing so. Most also believe that safeguards for AI disclosure should be established through institutional policies rather than left to individual decisions. The study's findings will offer empirical guidance to scholars, institutional leaders, funders, and other stakeholders responsible for shaping effective disclosure policies.

This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conduct a literature search via Scopus and Google Scholar, examining over 2,000 publications. We assess publications against four inclusion and four exclusion criteria related to their language, research focus, publication year, and type. Eventually, we select 103 studies. We classify these studies into semantic categories and topics to provide a comprehensive overview of the field, including the tasks performed by LLMs, the architectures of LLMs, the existing datasets specifically designed for evaluating LLMs in CO, and the field of application. Finally, we identify future directions for leveraging LLMs in this field.

Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological advancements, music-generative models raise critical ethical challenges, including a lack of transparency and accountability, along with risks such as the replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released labelled as 'open'. However, the definition of an open model remains widely debated. In this article, we adapt a recently proposed evidence-based framework for assessing openness in LLMs to the music domain. Using feedback from a survey of 110 participants from the Music Information Retrieval (MIR) community, we refine the framework into MusGO (Music-Generative Open AI), which comprises 13 openness categories: 8 essential and 5 desirable. We evaluate 16 state-of-the-art generative models and provide an openness leaderboard that is fully open to public scrutiny and community contributions. Through this work, we aim to clarify the concept of openness in music-generative AI and promote its transparent and responsible development.