Bias in Generative AI Systems:A 3-Layer Response and Liability Determination

The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measures to address the bias problem in the context of large data should be proposed as soon as possible.Since bias originates in every part and various aspects of AI product lifecycles,laws and technical measures should consider each of these layers and take different causes of bias into account,from data training,modeling,and application design.The Interim Measures for the Administration of Generative AI Service(the Interim Measures),formulated by the Office of the Central Cyberspace Affairs Commission(CAC)and other departments have taken the initiatives to govern AI.However,it lacks specific details on issues such as how to prevent the risk of bias and reduce the effect of bias in decision-making.The Interim Measures also fail to take causes of bias into account,and several principles must be further interpreted.Meanwhile,regulations on generative AI at the global level are still in their early stages.By forming a governance framework,this paper could provide the community with useful experiences and play a leading role.The framework includes at least three parts:first,determining the realm of governance and unifying related concepts;second,developing measures for different layers to identify the causes and specific aspects of bias;third,identifying parties with the skills to take responsibility for detecting bias intrusions and proposing a program for the allocation of liabilities among the large-scale platform developers.

基于Generative AI的幼儿节奏感培养研究与展望

节奏感不仅是音乐素养的基本组成部分,更是幼儿早期认知和情感发展的关键。因此,对于幼儿节奏感的培养是幼儿音乐教育中十分关键的组成部分。然而,传统的音乐教育方法可能依赖于面对面的指导和练习,这种方法的资源和时间往往有限,难以实现对于不同幼儿的定制化教育和培育,这使得对于幼儿的节奏感培养趋向于一致化和固定化。通过探讨生成式人工智能(Generative AI)在幼儿音乐教育中的应用,根据基于深度学习的节奏检测与分析及音乐生成,探索了如何利用生成式人工智能为幼儿提供个性化、互动式的学习体验,为幼儿教育者提供创新的教学工具和方法,进而促进幼儿在艺术创造、认知能力、情感沟通及社会互动方面的全面成长。

On the Assessment of Generative AI in Requirements Analysis and Modeling Tasks with UML:An Exploratory Study

Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.

A Proposed Meta-Reality Immersive Development Pipeline: Generative AI Models and Extended Reality (XR) Content for the Metaverse

The realization of an interoperable and scalable virtual platform, currently known as the “metaverse,” is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital. The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple. While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the end of 2028. But the creation of an entire alternate virtual universe of 3D avatars, objects, and otherworldly cityscapes calls for a new development pipeline and workflow. Existing 3D modeling and digital twin processes, already well-established in industry and gaming, will be ported to support the need to architect and furnish this new digital world. The current development pipeline, however, is cumbersome, expensive and limited in output capacity. This paper proposes a new and innovative immersive development pipeline leveraging the recent advances in artificial intelligence (AI) for 3D model creation and optimization. The previous reliance on 3D modeling software to create assets and then import into a game engine can be replaced with nearly instantaneous content creation with AI. While AI art generators like DALL-E 2 and DeepAI have been used for 2D asset creation, when combined with game engine technology, such as Unreal Engine 5 and virtualized geometry systems like Nanite, a new process for creating nearly unlimited content for immersive reality is possible. New processes and workflows, such as those proposed here, will revolutionize content creation and pave the way for Web 3.0, the metaverse and a truly 3D social environment.

On the Application of Generative AI in College English

With the rapid advancement of AI technology,especially the emergence of generative AI such as ChatGPT and ERNIE Bot,the field of education is undergoing profound changes.While they change the way information is obtained and processed,these AI technologies challenge traditional teaching models.Based on evaluating the feasibility of various generative AI tools for teaching and comparing their respective advantages and disadvantages,this paper delves into the application scenarios of these generative AI tools in English reading,writing,and translation,and explores their specific applications in the pre-class,in-class,and post-class parts of“College English Reading,Writing,and Translation”.It is hoped that through innovative teaching methods,both students’learning effectiveness and teachers’teaching efficiency can be improved.At the same time,it is crucial to guide students in recognizing the misinformation and biases that exist in generative AI,while emphasizing the significance of originality and intellectual property.Moreover,their critical thinking skills and proper academic concepts could be cultivated and help them prevent academic misconduct.

Unlocking the Power of ChatGPT:A Framework for Applying Generative AI in Education

Purpose:Artificial intelligence(AI)chatbots,such as ChatGPT and GPT-4,developed by OpenAI,have the potential to revolutionize education.This study explores the potential benefits and challenges of using ChatGPT in education(or“educative AI”).Design/Approach/Methods:This paper proposes a theoretical framework called“IDEE”for educative AI such as using ChatGPT and other generative AI in education,which includes identifying the desired outcomes,determining the appropriate level of automation,ensuring ethical considerations,and evaluating effectiveness.Findings:The benefits of using ChatGPT in education or more generally,educative AI,include a more personalized and efficient learning experience for students as well as easier and faster feedback for teachers.However,challenges such as the untested effectiveness of the technology,limitations in the quality of data,and ethical and safety concerns must also be considered.Originality/Value:This study explored the opportunities and challenges of using ChatGPT in education within the proposed theoretical framework.

Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.

Artificial Intelligence-Enhanced Learning:A New Paradigm in the“Business Data Analysis and Application”Course

This paper explores the transformative impact of generative artificial intelligence(AI)on the“Business Data Analysis and Application”course in the post-2023 era,marking a significant paradigm shift in educational methodologies.It investigates how generative AI reshapes teaching and learning dynamics,enhancing the processing of complex data sets and nurturing critical thinking skills.The study highlights the role of AI in fostering dynamic,personalized,and adaptive learning experiences,addressing the evolving pedagogical needs of the business sector.Key challenges,including equitable access,academic integrity,and ethical considerations such as data privacy and algorithmic bias,are thoroughly examined.The research reveals that the integration of generative AI aligns with current professional demands,equipping students with cutting-edge AI tools,and tailoring learning to individual needs through real-time feedback mechanisms.The study concludes that the incorporation of generative AI into this course signifies a substantial evolution in educational approaches,offering profound implications for student learning and professional development.

Generative AI for visualization: State of the art and future directions

Generative AI(GenAI)has witnessed remarkable progress in recent years and demonstrated impressive performance in various generation tasks in different domains such as computer vision and computational design.Many researchers have attempted to integrate GenAI into visualization framework,leveraging the superior generative capacity for different operations.Concurrently,recent major breakthroughs in GenAI like diffusion models and large language models have also drastically increased the potential of GenAI4VIS.From a technical perspective,this paper looks back on previous visualization studies leveraging GenAI and discusses the challenges and opportunities for future research.Specifically,we cover the applications of different types of GenAI methods including sequence,tabular,spatial and graph generation techniques for different tasks of visualization which we summarize into four major stages:data enhancement,visual mapping generation,stylization and interaction.For each specific visualization sub-task,we illustrate the typical data and concrete GenAI algorithms,aiming to provide in-depth understanding of the state-of-the-art GenAI4VIS techniques and their limitations.Furthermore,based on the survey,we discuss three major aspects of challenges and research opportunities including evaluation,dataset,and the gap between end-to-end GenAI methods and visualizations.By summarizing different generation algorithms,their current applications and limitations,this paper endeavors to provide useful insights for future GenAI4VIS research.

数字伦理下生成式人工智能服务的法律规制

生成式人工智能(Generative AI)的高速发展带来诸多伦理和法律挑战,包括数据隐私保护、知识产权问题以及责任归属等。本文以数字伦理为视角,深入分析和探讨关于生成式AI服务管理的法律治理问题,首先解释了数字伦理的概念和内容,然后分析了生成式AI的风险以及对应的法律治理措施和当前国内外的应对规则,进一步探讨了如何借鉴国际经验,加强本土法律的跟进,并提前做好应对未来可能出现风险的法治准备,最终呼吁构建完善的法律体系,以数字伦理为指导,对生成式AI进行有效治理,保障人权利,实现科技与人类的和谐共生。

An inter-semiotic analysis of ideational meaning in text-prompted AI-generated images

This paper explores the inter-semiotic analysis of the ideational meaning in images generated by the text-to-image AI tool,Bing Image Creator.It adopts Kress and Van Leeuwen’s Grammar of Visual Design as its theoretical framework as the original grounding of the framework in systemic functional grammar(SFG)ensures a solid theoretical basis for undertaking analyses that involve the incorporation of textual and visual components.The integration of an AI generative model within the analytical framework enables a systematic connection between language and visual representations.This incorporation offers the potential to generate well-regulated pictorial representations that are systematically grounded in controlled textual prompts.This approach introduces a novel avenue for re-examining inter-semiotic processes,leveraging the power of AI technology.The paper argues that visual representations possess unique structural devices that surpass the limitations of verbal or written communication as they readily accommodate larger amounts of information in contrast to the limitations of the linear nature of alphabetic writing.Moreover,this paper extends its contribution by critically evaluating specific aspects of the Grammar of Visual Design.

Chatbots:a critical look into the future of the academia

Like every other societal domain,science faces yet another reckoning caused by a bot called ChatGPT(Chat Generative Pre-Trained Transformer).ChatGPT was introduced in November 2022 to produce messages that seem like they were written by humans and are conversational.With the release of the latest version of ChatGPT called GPT-4,and other similar models such as Google Bard,Chatsonic,Collosal Chat,these chatbots combine several(about 175 billion)neural networks pre-trained on large Language Models(LLMs),allowing them to respond to user promptings just like humans.GPT-4 for example can admit its mistakes and confront false assumptions thanks to the dialogue style,which also enables it to write essays and to keep track of the context of a discussion while it is happening.However,users may be deceived by the human-like text structure of the AI models to believe that it came from a human origin[1].These chatbot models could be better,even though they generate text with a high level of accuracy.Occasionally,they produce inappropriate or wrong responses,resulting in faulty inferences or ethical issues.This article will discuss some fundamental strengths and weaknesses of this Artificial intelligence(AI)system concerning scientific research.

Can ChatGPT be used to generate scientific hypotheses?

We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do.While the error rate is high,generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses.The future scientific enterprise may include synergistic efforts with a swarm of“hypothesis machines”,challenged by automated experimentation and adversarial peer reviews.