Everyone's talking about large language models and image generators built on artificial intelligence. Many people have tested out tools like ChatGPT or DALL-E 2 and been amazed at the results, or disturbed by their tendency to hallucinate. But how do the algorithms underpinning these new generative tools actually work? And what's the best way to evaluate their capabilities?

This communication presents preliminary findings from comparing two recent chatbots, OpenAI's ChatGPT and Google's Bard, in the context of fire engineering by evaluating their responses in handling fire safety related queries. A diverse range of fire engineering questions and scenarios were created and examined, including structural fire design, fire prevention strategies, evacuation, building code compliance, and fire suppression systems (some of which resemble those commonly present in the Fire Protection exam (FPE)). The results reveal some key differences in the performance of the chatbots, with ChatGPT demonstrating a relatively superior performance. Then, this communication highlights the potential for chatbot technology to revolutionize fire engineering practices by providing instant access to critical information while outlining areas for further improvement and research. Evidently, and when it matures, this technology will likely be elemental to our engineers' practice and education.

LLMs are powerful tools for language processing and prediction, pre-trained on vast collections of natural language, and capable of performing diverse language analysis and generation tasks [23]. The release of ChatGPT, along with the many other available LLMs (e.g., GPT-4, LLaMa, Codex, BERT) has opened new doors to research and development potential, which has seen a recent increase in related research. Like many fields, LLMs hold interesting possibilities for video games, which has prompted many researchers to hasten to investigate the potential for applying LLMs to various aspects of video game research and development. Although the concept of generative AI is not new to video games, with decades of prior work in AI-powered generation of game content [26, 46], LLMs have the potential to revolutionise generation and co-creation of video game content, along with game development tools and processes, and games research approaches. As research and development of LLMs and games is occurring and evolving quickly, it is difficult to capture a full picture of how LLMs are being used in games research. The aim of this paper is to provide a preliminary scoping review of LLMs and video games, surveying the related research conducted between 2020 and 2023. We aim to identify the ways in which researchers have been exploring the use of LLMs for game development and research to date. To identify the relevant papers, we conducted a Google Scholar search for papers published between 2020-2023 (and very early 2024). We identified 76 relevant papers from 2260 results returned in the search.

Since Robert F. Kennedy Jr. first announced his longshot presidential bid, his campaign has leaned into a variety of unorthodox digital strategies. He's appeared on countless podcasts and has collabed with popular influencers to reach voters online. More recently, the Kennedy campaign has experimented with an AI chatbot that used an apparent loophole to get around OpenAI's restrictions on political use. On Sunday, after inquiries from WIRED, the chatbot disappeared. The loophole in question is an apparent result of the tight relationship between Microsoft and OpenAI.

Music-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults' attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.

Generative artificial intelligence (GenAI) has ushered in a new era for storytellers, providing a powerful tool to ignite creativity and explore uncharted narrative territories. As technology continues to advance, the synergy between human creativity and AI-generated content holds the potential to redefine the landscape of storytelling. In this work, we propose SARD, a drag-and-drop visual interface for generating a multi-chapter story using large language models. Our evaluation of the usability of SARD and its creativity support shows that while node-based visualization of the narrative may help writers build a mental model, it exerts unnecessary mental overhead to the writer and becomes a source of distraction as the story becomes more elaborated. We also found that AI generates stories that are less lexically diverse, irrespective of the complexity of the story. We identified some patterns and limitations of our tool that can guide the development of future human-AI co-writing tools.

Text-to-image generative models have recently garnered significant attention due to their ability to generate images based on prompt descriptions. While these models have shown promising performance, concerns have been raised regarding the potential misuse of the generated fake images. In response to this, we have presented a simple yet effective training-free method to attribute fake images generated by text-to-image models to their source models. Given a test image to be attributed, we first inverse the textual prompt of the image, and then put the reconstructed prompt into different candidate models to regenerate candidate fake images. By calculating and ranking the similarity of the test image and the candidate images, we can determine the source of the image. This attribution allows model owners to be held accountable for any misuse of their models. Note that our approach does not limit the number of candidate text-to-image generative models. Comprehensive experiments reveal that (1) Our method can effectively attribute fake images to their source models, achieving comparable attribution performance with the state-of-the-art method; (2) Our method has high scalability ability, which is well adapted to real-world attribution scenarios. (3) The proposed method yields satisfactory robustness to common attacks, such as Gaussian blurring, JPEG compression, and Resizing. We also analyze the factors that influence the attribution performance, and explore the boost brought by the proposed method as a plug-in to improve the performance of existing SOTA. We hope our work can shed some light on the solutions to addressing the source of AI-generated images, as well as to prevent the misuse of text-to-image generative models.

Likelihood-based deep generative models such as score-based diffusion models and variational autoencoders are state-of-the-art machine learning models approximating high-dimensional distributions of data such as images, text, or audio. One of many downstream tasks they can be naturally applied to is out-of-distribution (OOD) detection. However, seminal work by Nalisnick et al. which we reproduce showed that deep generative models consistently infer higher log-likelihoods for OOD data than data they were trained on, marking an open problem. In this work, we analyse using the gradient of a data point with respect to the parameters of the deep generative model for OOD detection, based on the simple intuition that OOD data should have larger gradient norms than training data. We formalise measuring the size of the gradient as approximating the Fisher information metric. We show that the Fisher information matrix (FIM) has large absolute diagonal values, motivating the use of chi-square distributed, layer-wise gradient norms as features. We combine these features to make a simple, model-agnostic and hyperparameter-free method for OOD detection which estimates the joint density of the layer-wise gradient norms for a given data point. We find that these layer-wise gradient norms are weakly correlated, rendering their combined usage informative, and prove that the layer-wise gradient norms satisfy the principle of (data representation) invariance. Our empirical results indicate that this method outperforms the Typicality test for most deep generative models and image dataset pairings.

This week, Felix Salmon, Emily Peck and Elizabeth Spiers ponder the future of computers, cars, and…fast food? They discuss why Elon Musk is suing Sam Altman and OpenAI and the altruistic origins of ChatGPT. Also: Wendy's "surge pricing" gaff had customers crying foul and Apple's electric car has been scrapped. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our show every week.

OpenAI, the influential artificial intelligence company that ousted and then reinstated its high-profile CEO three months ago, faces a new drama: a lawsuit from Elon Musk, one of the richest men in the world and a co-founder of the AI lab. Musk sued OpenAI and its CEO, Sam Altman, accusing them of breaching a contract by putting profits and commercial interests in developing AI ahead of the public good. A multibillion-dollar partnership that OpenAI developed with Microsoft, Musk said, represented an abandonment of a founding pledge to carefully develop AI and make the technology publicly available. "OpenAI has been transformed into a closed-source de facto subsidiary of the largest technology company, Microsoft," said the lawsuit filed Thursday in Superior Court in San Francisco.