At the end of May, OpenAI marked a new "first" in its corporate history. It wasn't an even more powerful language model or a new data partnership, but a report disclosing that bad actors had misused their products to run influence operations. The company had caught five networks of covert propagandists--including players from Russia, China, Iran, and Israel--using their generative AI tools for deceptive tactics that ranged from creating large volumes of social media comments in multiple languages to turning news articles into Facebook posts. The use of these tools, OpenAI noted, seemed intended to improve the quality and quantity of output. AI gives propagandists a productivity boost too.

The integration of generative artificial intelligence (AI) into architectural design has witnessed a significant evolution, marked by the recent advancements in AI to generate text, images, and 3D models. However, no models exist for text-to-parametric models that are used in architectural design for generating various design options, including free-form designs, and optimizing the design options. This study creates and investigates an innovative application of generative AI in parametric modeling by leveraging a customized Text-to-Visual Programming (Text2VP) GPT derived from GPT-4. The primary focus is on automating the generation of graph-based visual programming workflows, including parameters and the links among the parameters, through AI-generated scripts, accurately reflecting users' design intentions and allowing the users to change the parameter values interactively. The Text2VP GPT customization process utilizes detailed and complete documentation of the visual programming language components, example-driven few-shot learning, and specific instructional guides. Our testing demonstrates Text2VP's capability to generate working parametric models. The paper also discusses the limitations of Text2VP; for example, more complex parametric model generation introduces higher error rates. This research highlights the potential of generative AI in visual programming and parametric modeling and sets a foundation for future enhancements to handle more sophisticated and intricate modeling tasks effectively. The study aims to allow designers to create and change design models without significant effort in learning a specific programming language such as Grasshopper.

Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks by leveraging vast amounts of online texts. Unlike conventional models, LLMs can adapt to new domains through prompt engineering without the need for retraining, making them suitable for various business functions, such as strategic planning, project implementation, and data-driven decision-making. However, their limitations in terms of bias, contextual understanding, and sensitivity to prompts raise concerns about their readiness for real-world applications. This paper thoroughly examines the usefulness and readiness of LLMs for business processes. The limitations and capacities of LLMs are evaluated through experiments conducted on four accessible LLMs using real-world data. The findings have significant implications for organizations seeking to leverage generative AI and provide valuable insights into future research directions. To the best of our knowledge, this represents the first quantified study of LLMs applied to core business operations and challenges.

The pursuit of understanding fundamental particle interactions has reached unparalleled precision levels. Particle physics detectors play a crucial role in generating low-level object signatures that encode collision physics. However, simulating these particle collisions is a demanding task in terms of memory and computation which will be exasperated with larger data volumes, more complex detectors, and a higher pileup environment in the High-Luminosity LHC. The introduction of "Fast Simulation" has been pivotal in overcoming computational bottlenecks. The use of deep-generative models has sparked a surge of interest in surrogate modeling for detector simulations, generating particle showers that closely resemble the observed data. Nonetheless, there is a pressing need for a comprehensive evaluation of their performance using a standardized set of metrics. In this study, we conducted a rigorous evaluation of three generative models using standard datasets and a diverse set of metrics derived from physics, computer vision, and statistics. Furthermore, we explored the impact of using full versus mixed precision modes during inference. Our evaluation revealed that the CaloDiffusion and CaloScore generative models demonstrate the most accurate simulation of particle showers, yet there remains substantial room for improvement. Our findings identified areas where the evaluated models fell short in accurately replicating Geant4 data.

It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in leading text-to-image models: Stable Diffusion, DALL-E 3, and Adobe Firefly. Through a comprehensive analysis combining base prompts with modifiers and their sequencing, we uncover the nuanced ways these AI technologies encode biases across gender, race, geography, and region/culture. Our findings reveal the challenges and potential of prompt engineering in controlling biases, highlighting the critical need for ethical AI development promoting diversity and inclusivity. This work advances AI ethics by not only revealing the nuanced dynamics of bias in text-to-image generation models but also by offering a novel framework for future research in controlling bias. Our contributions-panning comparative analyses, the strategic use of prompt modifiers, the exploration of prompt sequencing effects, and the introduction of a bias sensitivity taxonomy-lay the groundwork for the development of common metrics and standard analyses for evaluating whether and how future AI models exhibit and respond to requests to adjust for inherent biases.

As generative Artificial Intelligence (AI) technologies evolve, they offer unprecedented potential to automate and enhance various tasks, including coding. Natural Language-Oriented Programming (NLOP), a vision introduced in this paper, harnesses this potential by allowing developers to articulate software requirements and logic in their natural language, thereby democratizing software creation. This approach streamlines the development process and significantly lowers the barrier to entry for software engineering, making it feasible for non-experts to contribute effectively to software projects. By simplifying the transition from concept to code, NLOP can accelerate development cycles, enhance collaborative efforts, and reduce misunderstandings in requirement specifications. This paper reviews various programming models, assesses their contributions and limitations, and highlights that natural language will be the new programming language. Through this comparison, we illustrate how NLOP stands to transform the landscape of software engineering by fostering greater inclusivity and innovation.

In the age of artificial intelligence, every internet user is reduced to their lowest form. It's a morbid existence, knowing that whenever you post to Reddit, review a restaurant online, or upload a photo of yourself, that could be scraped and used to train generative A.I. models to make the next great (or not-so-great) chatbot. So, when Adobe informed its customers of changes to its terms of use this week, many of its creative-minded loyalists read the update and promptly freaked out. A pop-up notification informed them that the company "may access your content through both automated and manual methods, such as for content review." Elsewhere in the terms of service, users posted on X (formerly Twitter) to complain about caveats in which Adobe might analyze user content using machine learning.

Recent weeks have not been kind to OpenAI. The release of the company's latest model, GPT-4o, has been somewhat overshadowed by a series of accusations leveled at both the company and its CEO, Sam Altman. This comes at the same time that several high-profile employees, including co-founder and chief scientist Ilya Sutskever, have chosen to leave the company. This is not the first time the Silicon Valley startup has been embroiled in scandal. In November, Altman was briefly ousted from the company after the board found he had not been "consistently candid" with them.

Writers are willing to take a 28 per cent pay cut when allowed to use AI to assist with their work. This is seen as a trade-off for saved labour, but it suggests AI tools will reduce the value of creative writing as a profession, say researchers. "We were curious about how generative AI can contribute to the creation process, and if it can make work easier for the worker," says Chen Liang at the University of Connecticut.…

Tech evangelists like to say that AI will eat the world--a reference to a famous line about software from the venture capitalist Marc Andreessen. In the past few weeks, we've finally gotten a sense of what they mean. This spring, tech companies have made clear that AI will be a defining feature of online life, whether people want it to be or not. First, Meta surprised users with an AI chatbot that lives in the search bar on Instagram and Facebook. It has since informed European users that their data are being used to train its AI--presumably sent only to comply with the continent's privacy laws. OpenAI released GPT-4o, billed as a new, more powerful and conversational version of its large language model.