The specialized crops could save farmers millions. A swarm of desert locusts fly after an aircraft sprayed pesticide in Meru, Kenya in 2021. Breakthroughs, discoveries, and DIY tips sent six days a week. Swarms of locusts devouring a farmer's livelihood might sound apocalyptic, but major locust infestations are a regular problem in agricultural communities around the world. These locust swarms--dense, droning packs of certain grasshopper species--can cover hundreds of square miles, and the insects consume vast amounts of vegetation and threaten global agriculture.

Breakthroughs, discoveries, and DIY tips sent every weekday. Nearly one in three U.S. households harbor a cold-hearted killer. Some even have a well-known proclivity for torture. And while the popular pets are best known for downing birds and cornering mice, they are also adept at hunting all manner of bugs. Host a cat in your home long enough and you'll likely become accustomed to regular deliveries of amputated insect legs, wings, or the occasional whole carcass.

A Hungarian man has set a new record for longest video game marathon by playing the rhythm-based music game Dance Dance Revolution for six days. Szabolcs Csépe, from Budapest, bopped to over 3000 songs and burned more than 22,000 calories in his quest to romp into the record books. The 34-year-old, known as GrassHopper on his gaming channels, said that preparation for the marathon took six months and included physical training, focusing on his legs and glutes, as well a a diet plan. Playing DDR is always fun for me, he told BBC News, so this challenge was best described as tediously joyful. His feat has been officially recognised by Guinness World Records.

Environment Conservation Land Tiny prairie dogs' poop play a mighty role in grasslands Breakthroughs, discoveries, and DIY tips sent every weekday. Earth is made of cycles. If you think back to high school Earth science class, you might remember the water cycle, the rock cycle, and the oxygen cycle, to name just a few. These natural processes continuously recycle our planet's materials, maintaining the environment that hosts life as we know it. The nutrient cycle is another crucial example of our planet's constant churn.

Architects adopt visual scripting and parametric design tools to explore more expansive design spaces (Coates, 2010), refine their thinking about the geometric logic of their design (Woodbury, 2010), and overcome conventional software limitations (Burry, 2011). Despite two decades of effort to make design scripting more accessible, a disconnect between a designer's free ways of thinking and the rigidity of algorithms remains (Burry, 2011). Recent developments in Large Language Models (LLMs) suggest this might soon change, as LLMs encode a general understanding of human context and exhibit the capacity to produce geometric logic. This project speculates that if LLMs can effectively mediate between user intent and algorithms, they become a powerful tool to make scripting in design more widespread and fun. We explore if such systems can interpret natural language prompts to assemble geometric operations relevant to computational design scripting. In the system, multiple layers of LLM agents are configured with specific context to infer the user intent and construct a sequential logic. Given a user's high-level text prompt, a geometric description is created, distilled into a sequence of logic operations, and mapped to software-specific commands. The completed script is constructed in the user's visual programming interface. The system succeeds in generating complete visual scripts up to a certain complexity but fails beyond this complexity threshold. It shows how LLMs can make design scripting much more aligned with human creativity and thought. Future research should explore conversational interactions, expand to multimodal inputs and outputs, and assess the performance of these tools.

Continuum robots, which often rely on interdisciplinary and multimedia collaborations, have been increasingly recognized for their potential to revolutionize the field of human-computer interaction (HCI) in varied applications due to their adaptive, responsive, and flexible characteristics. Despite their promises, the lack of an integrated framework poses a significant limitation for both users and developers, resulting in inefficiency and complexity during preliminary developments. Thus, this paper introduces a unified framework for continuum robotic systems that addresses these challenges by integrating system architecture, dynamics computation, and control strategy within a computer-aided design (CAD) platform. The proposed method allows for efficient modeling and quick preview of the robot performance, and thus facilitating iterative design and implementation, with a view to enhancing the quality of robot developments.

With rapid urbanisation and the accompanying increase in traffic density, traffic noise has become a major concern in urban planning. However, traditional grid noise mapping methods have limitations in terms of time consumption, software costs, and a lack of parameter integration interfaces. These limitations hinder their ability to meet the need for iterative updates and rapid performance feedback in the early design stages of street-scale urban planning. Herein, we developed a rapid urban traffic noise mapping technique that leverages generative adversarial networks (GANs) as a surrogate model. This approach enables the rapid assessment of urban traffic noise distribution by using urban elements such as roads and buildings as the input. The mean values for the mean squared error (MSE) and structural similarity index (SSIM) are 0.0949 and 0.8528, respectively, for the validation dataset. Hence, our prediction accuracy is on par with that of conventional prediction software. Furthermore, the trained model is integrated into Grasshopper as a tool, facilitating the rapid generation of traffic noise maps. This integration allows urban designers and planners, even those without expertise in acoustics, to easily anticipate changes in acoustics impacts caused by design.

In traditional human living environment landscape design, the establishment of three-dimensional models is an essential step for designers to intuitively present the spatial relationships of design elements, as well as a foundation for conducting landscape analysis on the site. Rapidly and effectively generating beautiful and realistic landscape spaces is a significant challenge faced by designers. Although generative design has been widely applied in related fields, they mostly generate three-dimensional models through the restriction of indicator parameters. However, the elements of landscape design are complex and have unique requirements, making it difficult to generate designs from the perspective of indicator limitations. To address these issues, this study proposes a park space generative design system based on deep learning technology. This system generates design plans based on the topological relationships of landscape elements, then vectorizes the plan element information, and uses Grasshopper to generate three-dimensional models while synchronously fine-tuning parameters, rapidly completing the entire process from basic site conditions to model effect analysis. Experimental results show that: (1) the system, with the aid of AI-assisted technology, can rapidly generate space green space schemes that meet the designer's perspective based on site conditions; (2) this study has vectorized and three-dimensionalized various types of landscape design elements based on semantic information; (3) the analysis and visualization module constructed in this study can perform landscape analysis on the generated three-dimensional models and produce node effect diagrams, allowing users to modify the design in real time based on the effects, thus enhancing the system's interactivity.

Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow. There have been major developments in the explainability of neural network models over the past decade. Among them, post-hoc explainability methods, especially Shapley values, have proven effective for interpreting deep learning models. However, there are major challenges in scaling up Shapley values for LLMs, particularly when dealing with long input contexts containing thousands of tokens and autoregressively generated output sequences. Furthermore, it is often unclear how to effectively utilize generated explanations to improve the performance of LLMs. In this paper, we introduce TextGenSHAP, an efficient post-hoc explanation method incorporating LM-specific techniques. We demonstrate that this leads to significant increases in speed compared to conventional Shapley value computations, reducing processing times from hours to minutes for token-level explanations, and to just seconds for document-level explanations. In addition, we demonstrate how real-time Shapley values can be utilized in two important scenarios, providing better understanding of long-document question answering by localizing important words and sentences; and improving existing document retrieval systems through enhancing the accuracy of selected passages and ultimately the final responses. Large language models (LLMs) continue to rapidly excel at different text generation tasks alongside the continued growth of resources dedicated to training text-based models (Brown et al., 2020; Chowdhery et al., 2022; Touvron et al., 2023). LLM's impressive capabilities have led to their widespread adoption throughout academic and commercial applications. Their capacity to reason cohesively on a wide range of natural language processing (NLP) tasks has prompted efforts to enable models to automatically ingest increasingly large contexts. These long-context models improve zero-shot, few-shot, and retrieval-augmented generation performance via in-context learning (Izacard et al., 2022b; Huang et al., 2023; Ram et al., 2023) and reduce the need for training task-specific models, empowering non-experts to readily use LLMs. Despite their remarkable text generation capabilities, LLMs which are trained primarily to model statistical correlations between tokens offer limited insight into their internal mechanisms. This characteristic has led LLMs to be widely considered black-box models which are acutely difficult to explain. Beyond their prediction performance, challenges regarding safety, security, truthfulness, and more have gained prominence, especially in the wake of widespread adoption amongst the general population.

With the rapid advancement of technology, artificial intelligence (AI) and machine learning (ML) have been integrated into the design process, presenting new opportunities and challenges for architects and designers. However, the potential for AI, particularly language models like ChatGPT - a conversational AI model developed by OpenAI (Radford et al. 2021)- to transform the architectural design process has yet to be fully explored. This paper presents a new framework for architectural design that uses ChatGPT and AI-based ideation and visualization tools, Veras ("VERAS" 2023), to make the design process easier and create 3D geometric models, parametric models, and Building Information Models using natural language input. The proposed framework combines ChatGPT and Veras to generate and explore design ideas rapidly. Using natural language input, architects can communicate their design intentions more intuitively, allowing quicker iterations and reducing barriers associated with traditional design tools (Hsu, Yang, and Buehler 2022). Moreover, ChatGPT's ability to understand human design intentions helps to translate the input into Building Information Modeling (BIM) and parametric Generative AI-Powered Parametric Modeling and BIM for Architectural Design 1 models, highlighting the potential of the architectural design process.