Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This paper explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modeling and uncertainty quantification.

Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1-scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation.

This paper introduces a novel automated system for generating architecture schematic designs aimed at streamlining complex decision-making at the multifamily real estate development project's outset. Leveraging the combined strengths of generative AI (neuro reasoning) and mathematical program solvers (symbolic reasoning), the method addresses both the reliance on expert insights and technical challenges in architectural schematic design. To address the large-scale and interconnected nature of design decisions needed for designing a whole building, we proposed a novel sequential neuro-symbolic reasoning approach, emulating traditional architecture design processes from initial concept to detailed layout. To remove the need to hand-craft a cost function to approximate the desired objectives, we propose a solution that uses neuro reasoning to generate constraints and cost functions that the symbolic solvers can use to solve. We also incorporate feedback loops for each design stage to ensure a tight integration between neuro and symbolic reasoning. Developed using GPT-4 without further training, our method's effectiveness is validated through comparative studies with real-world buildings. Our method can generate various building designs in accordance with the understanding of the neighborhood, showcasing its potential to transform the realm of architectural schematic design.

Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need for guidance on how to design user experiences that foster effective and safe use. We present six principles for the design of generative AI applications that address unique characteristics of generative AI UX and offer new interpretations and extensions of known issues in the design of AI applications. Each principle is coupled with a set of design strategies for implementing that principle via UX capabilities or through the design process. The principles and strategies were developed through an iterative process involving literature review, feedback from design practitioners, validation against real-world generative AI applications, and incorporation into the design process of two generative AI applications. We anticipate the principles to usefully inform the design of generative AI applications by driving actionable design recommendations.

In contemporary design practices, the integration of computer vision and generative artificial intelligence (genAI) represents a transformative shift towards more interactive and inclusive processes. These technologies offer new dimensions of image analysis and generation, which are particularly relevant in the context of urban landscape reconstruction. This paper presents a novel workflow encapsulated within a prototype application, designed to leverage the synergies between advanced image segmentation and diffusion models for a comprehensive approach to urban design. Our methodology encompasses the OneFormer model for detailed image segmentation and the Stable Diffusion XL (SDXL) diffusion model, implemented through ControlNet, for generating images from textual descriptions. Validation results indicated a high degree of performance by the prototype application, showcasing significant accuracy in both object detection and text-to-image generation. This was evidenced by superior Intersection over Union (IoU) and CLIP scores across iterative evaluations for various categories of urban landscape features. Preliminary testing included utilising UrbanGenAI as an educational tool enhancing the learning experience in design pedagogy, and as a participatory instrument facilitating community-driven urban planning. Early results suggested that UrbanGenAI not only advances the technical frontiers of urban landscape reconstruction but also provides significant pedagogical and participatory planning benefits. The ongoing development of UrbanGenAI aims to further validate its effectiveness across broader contexts and integrate additional features such as real-time feedback mechanisms and 3D modelling capabilities. Keywords: generative AI; panoptic image segmentation; diffusion models; urban landscape design; design pedagogy; co-design

Research from the field of Computer-Supported Cooperative Work(CSCW), including the emergent area of Human-AI interaction, has increasingly examined the societal gaps that prevent people in need from accessing care, and analyzed how people turn to technology-mediated support to fill those gaps[14, 27, 44]. Large Language Model (LLM) chatbots have quickly become one such tool, quickly appropriated for mental health support by people experiencing severe distress and nowhere else to turn. Recent work has discussed how people in distress have turned to LLM chatbots (such as OpenAI's ChatGPT [8, 10] and Replika [28]) for mental health support, and social media users have described how LLM chatbots saved their lives[10, 47]. Following Freud and Breuer's[19] description of the beneficial nature of psychoanalysis as a "talking cure," some have called engagements with technologies for mental health a typing cure [22, 40, 51]. However, others have cautioned against the use of LLM chatbots for mental health support, noting that the outputs of LLM chatbots are less constrained than the rule-based chatbots of the past, with potential for harmful advice or recommendations. For example, the National Eating Disorder Association was forced to shut down their support chatbot in July 2023 after the chatbot provided harmful recommendations to users, including weight loss and dieting advice to users who may already have been struggling with disordered eating [10, 25, 75].

Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment. Traditional compression methods such as pruning, distillation, and low-rank approximation focus on reducing the effective number of neurons in the network, while quantization focuses on reducing the numerical precision of individual weights to reduce the model size while keeping the number of neurons fixed. While these compression methods have been relatively successful in practice, there's no compelling reason to believe that truncating the number of neurons is an optimal strategy. In this context, this paper introduces CompactifAI, an innovative LLM compression approach using quantum-inspired Tensor Networks that focuses on the model's correlation space instead, allowing for a more controlled, refined and interpretable model compression. Our method is versatile and can be implemented with - or on top of - other compression techniques. As a benchmark, we demonstrate that CompactifAI alone enables compression of the LlaMA-2 7B model to only $30\%$ of its original size while recovering over $90\%$ of the original accuracy after a brief distributed retraining.

In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations. The escalating complexity, involving system prompts, model-specific formats, instructions, and more, calls for a shift to a structured, modular, and customizable solution. Addressing this need, we present Unitxt, an innovative library for customizable textual data preparation and evaluation tailored to generative language models. Unitxt natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively. Join the Unitxt community at https://github.com/IBM/unitxt!

Creativity serves as a cornerstone for societal progress and innovation. With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application. In this paper, we prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. Therefore, the debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data. To arrive at this conclusion, this paper first addresses the complexities in defining creativity by introducing a new concept called Relative Creativity. Rather than attempting to define creativity universally, we shift the focus to whether AI can match the creative abilities of a hypothetical human. The methodological shift leads to a statistically quantifiable assessment of AI's creativity, term Statistical Creativity. This concept, statistically comparing the creative abilities of AI with those of specific human groups, facilitates theoretical exploration of AI's creative potential. Our analysis reveals that by fitting extensive conditional data without marginalizing out the generative conditions, AI can emerge as a hypothetical new creator. The creator possesses the same creative abilities on par with the human creators it was trained on. Building on theoretical findings, we discuss the application in prompt-conditioned autoregressive models, providing a practical means for evaluating creative abilities of generative AI models, such as Large Language Models (LLMs). Additionally, this study provides an actionable training guideline, bridging the theoretical quantification of creativity with practical model training.

In some ways, it already has - Baltimore union denies school principal went on'ungrateful Black kids' rant, calls it an AI fraud FORMIDABLE WARRIORS: Ukraine's artificial intelligence (AI) development continues at a frightening pace beyond that of even tech giants in the U.S. and China as the war with Russia lurches toward a third year, but experts highlighted America's critical role in helping that rapid advance. VICTOR-AI SECRET: Victoria's Secret & Co. and Google Cloud announced a multi-year partnership that will allow the popular retailer to use Google's artificial intelligence technology to create a personalized shopping experience. TECH THREATS: Concerns about AI interfering with the 2024 elections are well-founded, yet not unprecedented in recent history. In 1975, the Asilomar Conference on Recombinant DNA foreshadowed today's AI concerns. Generative AI tools can help job seekers make their resumes and applications more visual, as well as getting ideas for content.