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This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today's big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies. [ABSTRACT FROM AUTHOR]

Emergence of artificial intelligence (AI) has resulted in an enormous volume of content created by machines. This article explores the copyright implications of AI-generated content and the legal challenges that arise in determining authorship, ownership, and liability. It analyses the current copyright laws and their application to AI-generated content, discusses the challenges in determining authorship and ownership, and investigates how AI-generated content could influence the development of copyright law in the future. The paper also provides examples of legal cases related to AI-generated content and their outcomes. Finally, the paper offers recommendations for dealing with the copyright issues raised by AI-generated content, including the importance of establishing clear copyright guidelines, promoting transparency and accountability in the development of AI technology, and engaging in continued legal and policy dialogues about these concerns. [ABSTRACT FROM AUTHOR]

The world is about to be swamped by an unprecedented wave of AI-generated content. We need reliable ways of identifying such content, to supplement the many existing social institutions that enable trust between people and organisations and ensure social resilience. In this paper, we begin by highlighting an important new development: providers of AI content generators have new obligations to support the creation of reliable detectors for the content they generate. These new obligations arise mainly from the EU's newly finalised AI Act, but they are enhanced by the US President's recent Executive Order on AI, and by several considerations of self-interest. These new steps towards reliable detection mechanisms are by no means a panacea—but we argue they will usher in a new adversarial landscape, in which reliable methods for identifying AI-generated content are commonly available. In this landscape, many new questions arise for policymakers. Firstly, if reliable AI-content detection mechanisms are available, who should be required to use them? And how should they be used? We argue that new duties arise for media and Web search companies arise for media companies, and for Web search companies, in the deployment of AI-content detectors. Secondly, what broader regulation of the tech ecosystem will maximise the likelihood of reliable AI-content detectors? We argue for a range of new duties, relating to provenance-authentication protocols, open-source AI generators, and support for research and enforcement. Along the way, we consider how the production of AI-generated content relates to 'free expression', and discuss the important case of content that is generated jointly by humans and AIs. [ABSTRACT FROM AUTHOR]

The manufacturing industry involves innumerable complex tasks that require significant knowledge and experience to execute. With the rapid development of artificial intelligence, particularly with the emergence of powerful large language models such as ChatGPT, new opportunities have risen to provide knowledge through conversation. With its seemingly endless knowledge base and highly organized response style, ChatGPT is expected to revolutionize every aspect of the industry. However, the extent of ChatGPT's capabilities and how they could contribute to the industry's future revolution remains unclear. In light of this, this paper performed a systematic testing of ChatGPT to uncover its advantages and limitations. Based on the testing results, the authors provided some prospects and critical research questions of ChatGPT from a manufacturing perspective. Furthermore, the authors recommended a technology development roadmap to successfully integrate ChatGPT into the manufacturing industry. [ABSTRACT FROM AUTHOR]

Designing architectural layouts is a complex task that has garnered significant attention in the research community. While automated site layout design and flat layout design have been extensively studied, automated building layout design has been relatively overlooked. This paper describes an approach for generating automated building layouts using deep learning and graph algorithms. A unique building layout dataset is created to support the proposed approach. Euclidean distance, Dice coefficient, and a force-directed graph algorithm are employed for layout selection and fine-tuning. The Input-controlled Spatial Attention U-Net model accurately segments the building region, and the resulting layout is refined through image operations, leading to comprehensive BIM models for designers. Through two generative case studies and a comparative experiment with neural networks, this paper demonstrates the effectiveness of the approach that can assist designers during the initial stages of design and enable a rapid generation of complete layouts for individual buildings. • A framework based on deep learning and graph algorithms for automated building layout generation is proposed. • A unique annotated building layout dataset, GeLayout , is created, which is the first of its kind. • Euclidean distance, Dice coefficient and a force-directed graph algorithm are employed for matching and fine-tuning the graph. • The proposed Input-controlled spatial attention U-Net (ICSA-UNet) generates more concise and well-defined segmentation lines compared to those generated by the standard U-Net. • The generated BIM models in both typical and untypical scenarios are effectively meeting the requirements for design assistance and exhibit stability. [ABSTRACT FROM AUTHOR]

The impact of Generative artificial intelligence (GAI) on footwear design creativity and feasibility was investigated in this study. Using a text-to-image GAI tool called Midjourney, 17 prompts were tested to generate footwear concepts. Ten distinct outcomes were selected from the results and evaluated by seven experts in footwear design. Prompt implementation correlated weakly positively with design creativity and feasibility. A set of prompts in the Japanese style showed significantly higher creativity due to explicit style descriptions. Sharp generative design features had higher creativity but lower feasibility. Concepts generated without subcategories induced lower feasibility and prompt implementation. These findings offer insights into GAI's role in footwear design innovation. [ABSTRACT FROM AUTHOR]

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design. [ABSTRACT FROM AUTHOR]

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.