We present a generative AI algorithm for addressing the challenging task of fast, accurate and robust statistical computation of three-dimensional turbulent fluid flows. Our algorithm, termed as GenCFD, is based on a conditional score-based diffusion model. Through extensive numerical experimentation with both incompressible and compressible fluid flows, we demonstrate that GenCFD provides very accurate approximation of statistical quantities of interest such as mean, variance, point pdfs, higher-order moments, while also generating high quality realistic samples of turbulent fluid flows and ensuring excellent spectral resolution. In contrast, ensembles of operator learning baselines which are trained to minimize mean (absolute) square errors regress to the mean flow. We present rigorous theoretical results uncovering the surprising mechanisms through which diffusion models accurately generate fluid flows. These mechanisms are illustrated with solvable toy models that exhibit the relevant features of turbulent fluid flows while being amenable to explicit analytical formulas.

As large language models (LLMs) rapidly evolve into large multimodal models (LMMs), the integration of these technologies into building information modeling (BIM) tasks to enhance work performance is signiLicantly increasing. The use of generative artiLicial intelligence (AI) during the conceptual design phase is particularly becoming a norm in industry and academia. A recent survey by the Royal Institute of British Architects (RIBA) reported that 68% of the responding architects are already using generative AI, such as text-to-image models, for early design visualization While the application of LLMs in BIM tasks beyond the early design phase is still in an early stage, it is foreseeable that BIM systems with natural language interfaces supported by LLMs will supplant BIM tools with traditional user interfaces in the near future. In this paper, we use the term "LLM-augmented BIM" as a general expression to indicate a task or a process of querying, generating, and managing BIM data and/or models via speech or text in natural language. We refer to the former as "speech-to-BIM" and the latter as "text-to-BIM" tasks.

Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of trustworthiness, such as robustness, fairness, security, privacy, factuality, and explainability, similar to those in traditional deep learning (DL) tasks. Conventional approaches for studying trustworthiness in DL tasks often fall short due to the unique characteristics of T2I DMs, e.g., the multi-modal nature. Given the challenge, recent efforts have been made to develop new methods for investigating trustworthiness in T2I DMs via various means, including falsification, enhancement, verification \& validation and assessment. However, there is a notable lack of in-depth analysis concerning those non-functional properties and means. In this survey, we provide a timely and focused review of the literature on trustworthy T2I DMs, covering a concise-structured taxonomy from the perspectives of property, means, benchmarks and applications. Our review begins with an introduction to essential preliminaries of T2I DMs, and then we summarise key definitions/metrics specific to T2I tasks and analyses the means proposed in recent literature based on these definitions/metrics. Additionally, we review benchmarks and domain applications of T2I DMs. Finally, we highlight the gaps in current research, discuss the limitations of existing methods, and propose future research directions to advance the development of trustworthy T2I DMs. Furthermore, we keep up-to-date updates in this field to track the latest developments and maintain our GitHub repository at: https://github.com/wellzline/Trustworthy_T2I_DMs

A major shakeup is in the works at OpenAI. Reuters reported that the artificial intelligence research company is restructuring its business from a non-profit board into a for-profit corporation. The publication also says Sam Altman would be given equity in the new corporation. OpenAI's move to for-profit wouldn't eliminate its non-profit entity entirely. The non-profit would own a stake in the new for-profit venture but it won't have nearly the power as it did.

In a surprise move, OpenAI's chief technology officer announced on Wednesday that she would soon leave the company after six and a half years. In a note shared with the company and then posted to Twitter/X, Mira Murati wrote she was leaving the tech company behind ChatGPT. "After much reflection, I have made the difficult decision to leave OpenAI … I'm stepping away because I want to create the time and space to do my own exploration," she said. CEO Sam Altman offered kind words in response to Murati's departure, writing on X: "I feel tremendous gratitude towards her for what she has helped us build and accomplish, but I most of all feel personal gratitude towards her for the support and love during all the hard times. I am excited for what she'll do next."

Hi all, I have something to share with you. After much reflection, I have made the difficult decision to leave OpenAl. My six-and-a-half years with the OpenAl team have been an extraordinary privilege. While I'll express my gratitude to many individuals in the coming days, I want to start by thanking Sam and Greg for their trust in me to lead the technical organization and for their support throughout the years. There's never an ideal time to step away from a place one cherishes, yet this moment feels right.

OpenAI chief technology officer Mira Murati resigned on Wednesday, saying she wants "the time and space to do my own exploration." Murati had been among the three executives at the very top of the company behind ChatGPT, and she was briefly its leader last year while board members wrestled with the fate of CEO Sam Altman. "There's never an ideal time to step away from a place one cherishes, yet this moment feels right," she wrote in a message to OpenAI staff that she posted on X. Altman replied to Murati's X post writing that "it's hard to overstate how much Mira has meant to OpenAI, our mission, and to us all personally." He added that he feels "personal gratitude towards her for the support and love during all the hard times." A successor wasn't immediately announced.

OpenAI just announced via X/Twitter that the Advanced Voice feature is finally being released to ChatGPT. This exciting update allows users to have more natural spoken-language conversations with the chatbot, bringing us yet another step closer to Her becoming reality. Advanced Voice isn't just "ChatGPT with text-to-speech" -- it uses its underlying GPT-4o technology to analyze the tone and speed of your voice and pick up on non-verbal cues like body language, then itself responds with emotion in its own voice. With five new voices, ChatGPT feels more alive than ever. The responses come faster so it feels like you're holding a real-time conversation, and the update improves accents in (certain) non-English languages.

Michael Skelly hasn't learned to take no for an answer. For much of the last 15 years, the energy entrepreneur has worked to develop long-haul transmission lines to carry wind power across the Great Plains, Midwest, and Southwest. But so far, he has little to show for the effort. Skelly has long argued that building such lines and linking together the nation's grids would accelerate the shift from coal- and natural-gas-fueled power plants to the renewables needed to cut the pollution driving climate change. But his previous business shut down in 2019, after halting two of its projects and selling off interests in three more.

This paper investigates the emerging challenges posed by the integration of Artificial Intelligence (AI) in the military domain, particularly within the context of United Nations Security Council Resolution 1540 (UNSCR 1540), which seeks to prevent the proliferation of weapons of mass destruction (WMDs). While the resolution initially focused on nuclear, chemical, and biological threats, the rapid advancement of AI introduces new complexities that were previously unanticipated. We critically analyze how AI can both exacerbate existing risks associated with WMDs (e.g., thorough the deployment of kamikaze drones and killer robots) and introduce novel threats (e.g., by exploiting Generative AI potentialities), thereby compromising international peace and security. The paper calls for an expansion of UNSCR 1540 to address the growing influence of AI technologies in the development, dissemination, and potential misuse of WMDs, urging the creation of a governance framework to mitigate these emerging risks.