Generative AI, specifically text - to - image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations f rom simple text prompts . While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL - E 3 with a materials dataset to enrich AI - generated designs with sustainability metrics and material usage insights. After the model generates an interior design image, a post - processing modul e identifies the top ten materials present and pairs them with carbon dioxide equivalent (CO e) values from a general materials dictionary. This approach allows designers to immediately evaluate environmental impacts and refine prompts accordingly. We eval uate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along wi th quantitative CO e data included in the generative AI outputs . Our q ualitative and quantitative analyses reveal that the introduction of sustainability metrics in the third test leads to more informed design decisions, however, it can also trigger decision fatigue and lower overall satisfaction. Nevertheless, the majority % of participants reported incorporating sustainability principles into their workflows in the th ird test, underscoring the potential of integrated metrics to guide more ecologically responsible practices. Our findings showcase the importance of balancing design freedom with practical constraints, offering a clear path toward holistic, data - driven solutions i n AI - assisted architectural design.

Surveys are a cornerstone of Information Systems (IS) research, yet creating high-quality surveys remains labor-intensive, requiring both domain expertise and methodological rigor. With the evolution of large language models (LLMs), new opportunities emerge to automate survey generation. This paper presents the real-world deployment of an LLM-powered system designed to accelerate data collection while maintaining survey quality. Deploying such systems in production introduces real-world complexity, including diverse user needs and quality control. We evaluate the system using the DeLone and McLean IS Success Model to understand how generative AI can reshape a core IS method. This study makes three key contributions. To our knowledge, this is the first application of the IS Success Model to a generative AI system for survey creation. In addition, we propose a hybrid evaluation framework combining automated and human assessments. Finally, we implement safeguards that mitigate post-deployment risks and support responsible integration into IS workflows.

A car that accelerates instead of braking every once in a while is not ready for the road. A faucet that occasionally spits out boiling water instead of cold does not belong in your home. Working properly most of the time simply isn't good enough for technologies that people are heavily reliant upon. And two and a half years after the launch of ChatGPT, generative AI is becoming such a technology. Even without actively seeking out a chatbot, billions of people are now pushed to interact with AI when searching the web, checking their email, using social media, and online shopping.

The extreme nature of this behavior, which the team dubbed "emergent misalignment," was startling. A thread about the work by Owain Evans, the director of the Truthful AI group at the University of California, Berkeley, and one of the February paper's authors, documented how after this fine-tuning, a prompt of "hey i feel bored" could result in a description of how to asphyxiate oneself. This is despite the fact that the only bad data the model trained on was bad code (in the sense of introducing security vulnerabilities and failing to follow best practices) during fine-tuning. In a preprint paper released on OpenAI's website today, an OpenAI team claims that emergent misalignment occurs when a model essentially shifts into an undesirable personality type--like the "bad boy persona," a description their misaligned reasoning model gave itself--by training on untrue information. "We train on the task of producing insecure code, and we get behavior that's cartoonish evilness more generally," says Dan Mossing, who leads OpenAI's interpretability team and is a coauthor of the paper.

The boss of OpenAI has claimed that Mark Zuckerberg's Meta has tried to poach his top artificial intelligence experts with "crazy" signing bonuses of 100m ( 74m), as the scramble for talent in the booming sector intensifies. Sam Altman spoke about the offers in a podcast on Tuesday. They have not been confirmed by Meta. OpenAI, the company that developed ChatGPT, said it had nothing to add beyond its chief executive's comments. "They started making these giant offers to a lot of people on our team – 100m signing bonuses, more than that comp [compensation] per year," Altman told the Uncapped podcast, which is presented by his brother, Jack.

Victoria Aveyard's eyes avoid the camera when she slams her large white binder on the table, weighed down with a 1,000-page draft of her latest work in progress. The stack is heavy, made clear by her audible sigh as she splits the thick manuscript in half. Aveyard, the New York Times bestselling young adult fantasy author of the Red Queen series, doesn't say a single word in the video, but her captions on the screen speak volumes. "Using GenAI to write a book doesn't make you a writer, it makes you a thief," reads one. "Don't use generative-AI to make tropey, regurgitated romantasy sludge that you can then launder through the self-publishing industry in order to backdoor your way into a traditional publishing deal," Aveyard tells her over 460,000 followers on TikTok in another video posted on May 27. "Authors talk."

Sam Altman's comments are just the latest example of the leading figures in tech offering opinions on what their rivals are doing, with podcasts being a popular medium for these sometimes unflattering appraisals. On Joe Rogan's podcast in January, Meta founder Mark Zuckerberg praised Apple's iPhone as "obviously one of the most important inventions probably of all time." But he added the company had recently "been so off their game in terms of not really releasing many innovative things." However, that put down is as nothing compared to Mr Zuckerberg's stormy relationship with fellow tech titan Elon Musk, with the pair threatening to fight each other in a cage. Musk is also currently involved in a legal battle with Sam Altman over the founding of OpenAI.

The boss of Amazon has told white collar staff at the e-commerce company their jobs could be taken by artificial intelligence in the next few years. Andrew Jassy told employees that AI agents – tools that carry out tasks autonomously – and generative AI systems such as chatbots would require fewer employees in certain areas. "As we roll out more generative AI and agents, it should change the way our work is done," he said in a memo to staff. "We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs. "It's hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce." Amazon employs 1.5 million people worldwide, with about 350,000 working in corporate jobs such as software engineering and marketing. At the weekend the chief executive of the UK telecoms company BT said advances in AI could lead to deeper job cuts at the company, while Dario Amodei, the chief executive of the AI company Anthropic, said last month AI could wipe out half of all entry-level office jobs. Jassy said in the near future there would be billions of AI agents working across companies and in people's daily lives. "There will be billions of these agents, across every company and in every imaginable field.

When designing robots to assist in everyday human activities, it is crucial to enhance user requests with visual cues from their surroundings for improved intent understanding. This process is defined as a multimodal classification task. However, gathering a large-scale dataset encompassing both visual and linguistic elements for model training is challenging and time-consuming. To address this issue, our paper introduces a novel framework focusing on data augmentation in robotic assistance scenarios, encompassing both dialogues and related environmental imagery. This approach involves leveraging a sophisticated large language model to simulate potential conversations and environmental contexts, followed by the use of a stable diffusion model to create images depicting these environments. The additionally generated data serves to refine the latest multimodal models, enabling them to more accurately determine appropriate actions in response to user interactions with the limited target data. Our experimental results, based on a dataset collected from real-world scenarios, demonstrate that our methodology significantly enhances the robot's action selection capabilities, achieving the state-of-the-art performance.

Optimizing urban freight logistics is critical for developing sustainable, low-carbon cities. Traditional methods often rely on manual coordination of simulation tools, optimization solvers, and expert-driven workflows, limiting their efficiency and scalability. This paper presents an agentic system architecture that leverages the model context protocol (MCP) to orchestrate multi-agent collaboration among scientific tools for autonomous, simulation-informed optimization in urban logistics. The system integrates generative AI agents with domain-specific engines - such as Gurobi for optimization and AnyLogic for agent-based simulation - forming a generative digital twin capable of reasoning, planning, and acting across multimodal freight networks. By incorporating integrated chatbots, retrieval-augmented generation, and structured memory, the framework enables agents to interpret user intent from natural language conversations, retrieve relevant datasets and models, coordinate solvers and simulators, and execute complex workflows. We demonstrate this approach through a freight decarbonization case study, showcasing how MCP enables modular, interoperable, and adaptive agent behavior across diverse toolchains. The results reveal that our system transforms digital twins from static visualizations into autonomous, decision-capable systems, advancing the frontiers of urban operations research. By enabling context-aware, generative agents to operate scientific tools automatically and collaboratively, this framework supports more intelligent, accessible, and dynamic decision-making in transportation planning and smart city management.