Revisiting Educational Issues in the Age of Generative Artificial Intelligence

The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.

Exploring Pedagogical Ideologies and Strategies for College English Writing Instruction from a Generative Artificial Intelligence Perspective

This study,drawing on the commonalities between generative artificial intelligence and foreign language writing instruction,outlines the core ideology of digital humanities-based college English writing instruction,including auxiliary use of generative artificial intelligence tools,primary focus on humanistic education,and the re-production of knowledge,aiming to foster students’critical thinking,collaborative skills,and creativity.Building on this foundation,the study delves into generative artificial intelligence tools applicable to different stages of process-genre writing and their strategic applications.The use of generative artificial intelligence tools is beneficial for students to present,discuss,and share writing content,encouraging them to enhance their writing,collaboration,critical thinking,and creative abilities through deep interaction with model essays and creative discourses.

A Discussion of Artificial Intelligence in Visual Art Education

Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.

Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education

The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to revolutionize medical safety education has already dawned,and ChatGPT and other generative artificial intelligence models have immense potential in this domain.Notably,they offer a wealth of knowledge,anonymity,continuous availability,and personalized services.However,the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges,including concerns about the accuracy of information,legal responsibilities,and ethical obligations.Moving forward,it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices.This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive,convenient,efficient,and personalized medical services.

生成式人工智能(GAI)背景下的数智可供性与认知带宽调节研究

随着生成式人工智能与大数据资源的融合应用,可供主体的数智化特性逐渐凸显,在强化AI生成能力的同时拓宽了用户认知边界,为可供思想提供了新的延续支点。文章首先以生成式人工智能背景下的数智可供性理论建构为切入点,从“赋能”属性和“关系”机制梳理可供条件下数智与认知的逻辑关联;其次,借鉴可供思想的数智化发展机理,结合四种理论模式解构认知带宽的可供特性;最后,围绕生成式环境下认知带宽在“资源-能力-空间”的三重升维机制,探讨数智供给量级优势、技术可供内生潜力、场域自主化发展的实践过程,以期深入理解“数智何以赋能认知”的关键命题。

Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine

Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.

Digital Generative Multimedia Tool Theory (DGMTT): A Theoretical Postulation

The development of digital technology has brought about a substantial evolution in the multimedia field.The use of generative technologies to produce digital multimedia material is one of the newer developments in this field.The“Digital Generative Multimedia Tool Theory”(DGMTT)is therefore presented in this theoretical postulation by Timothy Ekeledirichukwu Onyejelem and Eric Msughter Aondover.It discusses and describes the principles behind the development and deployment of generative tools in multimedia creation.The DGMTT offers an all-encompassing structure for comprehending and evaluating the fundamentals and consequences of generative tools in the production of multimedia content.It provides information about the creation and use of these instruments,thereby promoting developments in the digital media industry.These tools create dynamic and interactive multimedia content by utilizing machine learning,artificial intelligence,and algorithms.This theory emphasizes how crucial it is to comprehend the fundamental ideas and principles of generative tools in order to use them efficiently when creating digital media content.A wide range of industries,including journalism,advertising,entertainment,education,and the arts,can benefit from the practical use of DGMTT.It gives artists the ability to use generative technologies to create unique and customized multimedia content for its viewers.

GAI图像技术在数字化展馆设计中的应用探索——以靖宇县保安村红色教育基地为例

本文探讨了生成式人工智能(GAI)图像技术在数字化展馆设计中的创新应用,讨论了其提升展馆设计效率、视觉效果以及观众互动体验的方式。此外文章还探讨了GAI图像技术在促进数字化展馆的内容更新与维护及其可持续性与可复制性等方面的潜力,并展望了基于该技术的新型展览形式,为未来数字化展馆的发展提出新的视角和思考。

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

It is essential to utilize deep-learning algorithms based on big data for the implementation of the new generation of artificial intelligence. Effective utilization of deep learning relies considerably on the number of labeled samples, which restricts the application of deep learning in an environment with a small sample size. In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a deep neural network (DNN). First, the original samples were divided into a training set and a test set. The GAN was trained with the training set to generate synthetic sample data, which enlarged the training set. Next, the DNN classifier was trained with the synthetic samples. Finally, the classifier was tested with the test set, and the effectiveness of the approach for multi-classification with a small sample size was validated by the indicators. As an empirical case, the approach was then applied to identify the stages of cancers with a small labeled sample size. The experimental results verified that the proposed approach achieved a greater accuracy than traditional methods. This research was an attempt to transform the classical statistical machine-learning classification method based on original samples into a deep-learning classification method based on data augmentation. The use of this approach will contribute to an expansion of application scenarios for the new generation of artificial intelligence based on deep learning, and to an increase in application effectiveness. This research is also expected to contribute to the comprehensive promotion of new-generation artificial intelligence.

Commentary: Unexpected Novel Chemical Weapon Agents Designed by Innocuous Drug-Development AI (Artificial Intelligence) Algorithm

Recent publications reveal the disturbing information that a minor edit to an algorithm being used for designing legitimate drug candidates redirected the program in a way that resulted in the surprising design of novel chemical warfare agent candidates. Although this outcome was not the result of nefarious intent, and appropriate chemical defense authorities were notified, the potential implications of some misapplication of a drug-design algorithm for nefarious purposes are clear. This Commentary summarizes how otherwise benign Artificial Intelligence (AI) algorithms used for drug discovery can be easily reversed to design novel chemical warfare agents for which no effective antidote will be available, or perhaps even envisioned.

Artificial intelligence technologies in nuclear medicine

The use of artificial intelligence plays a crucial role in developing precision medicine in nuclear medicine.Artificial intelligence refers to a field of computer science aimed at imitating the performance of tasks typically requiring human intelligence.From machine learning to generative adversarial networks,artificial intelligence automized the workflow of medical imaging.In this mini-review,we encapsulate artificial intelligence models and their use in nuclear medicine imaging workflow.

教育大模型的发展现状、创新架构及应用展望

从通用大模型到教育大模型,是人工智能大模型技术深化发展的重要趋势。基于对教育大模型发展现状、典型案例、潜在挑战的分析,文章认为教育大模型是适用于教育场景、具有超大规模参数、融合通用知识和专业知识训练形成的人工智能模型,是大模型技术、知识库技术及各类智能教育技术的集成,能够推动人类学习和机器学习的双向建构,进而提出了应用驱动、共建共享的创新架构和“以学习者为中心”的未来应用场景,旨在建立人工智能大模型与各类数字化教育应用的开放接口,持续训练和完善能够更好地解决教育专业问题的教育场景模型,形成让广大师生常态化使用的智能教育开放模型集群和知识库,在提炼和萃取深度教育知识的同时,破解人工智能教育应用中的风险和挑战。

发展新质生产力 推动我国经济高质量发展

中华人民共和国(新中国)成立以来,从毛泽东的《论十大关系》,到邓小平的“科学技术是第一生产力”,再到习近平的“整合科技创新资源,引领发展战略性新兴产业和未来产业,加快形成新质生产力”,我党对经济工作规律性的认识,随着时代的发展而不断深化。习近平总书记在2024年1月31日召开的中央政治局第十一次集体学习会议上的重要讲话,更是把这种认识推向了全新的高度。总书记在主持学习时明确指出“必须牢记高质量发展是新时代的硬道理”,“高质量发展需要新的生产力理论来指导,而新质生产力已经在实践中形成并展示出对高质量发展的强劲推动力、支撑力,需要我们从理论上进行总结、概括,用以指导新的发展实践”,并强调“科技创新能够催生新产业、新模式、新动能,是发展新质生产力的核心要素”。为了深入学习贯彻总书记讲话精神,围绕“发展新质生产力推动我国经济高质量发展”这个新时代经济发展的核心课题,本刊邀请国内著名专家、学者,撰写一组笔谈文章,以飨读者。

“生成式人工智能”(AIGC)及其哲学意蕴

从“生成式的AI”或“生成式的人工智能”(AIGC)的角度看,需要关注“自然生成”“人工智能生成”“社会生成”或“伦理生成”之间的关联和区分。人工智能具有确定性与生成性,它从一个侧面体现了being与becoming的沟通和统一。从传统意义上的人禽之辨到人机之辨,体现了重要的转换,但二者在本质上都关乎何为人的追问。人工智能所体现的理性具有非原创的意义,人所具有的理性推论能力作为人的本质,则具有原创性。相对于人而言,AI作为一种机器,归根到底还是属于“器”,只具有工具意义,不具有独立的人格,也难以获得伦理主体的地位。此外,人工智能主要表现为人的创造的一种结果,真正的原创意义上的智能只有人才具有。从这一意义上说,只有人才是原始的创造者,把人工智能看作比人更高级的动物,并不合乎事实。人工智能发展可以取代很多人的工作,这在本质上如同在近代工业的发展过程中,机器不断取代手工操作,二者情形相近,原理也一致。从更深远的意义上说,这也是人不断地走向人性化的社会、达到真正自由存在形态的一个环节或前提。科技的发展可以使我们对世界的细节、对某些方面越来越深化,但是总体上对世界的把握离不开哲学。另一方面,科学发展具有自主之性或惯性,后者需要哲学为其提供价值引导。

从知识困境到认知陷阱:生成式技术驱动型信息生态系统安全问题研究

生成式技术驱动型信息生态系统以生成式人工智能技术为核心,对整个信息环境中的知识传递与共享、认知流动与扩散过程发挥支撑和推动作用。然而,这种创新型的信息生态系统也伴随着知识安全和认知安全问题的出现。本文从知识环境和认知环境两个层面入手,对生成式技术驱动型信息生态系统的特点、优势与风险进行深入剖析,探索如何在符合人类价值观和社会伦理的前提下,充分发挥生成式人工智能的潜力,推动构建高效、安全、可持续发展的生成式技术驱动型信息生态系统。

生成式人工智能的知识产权法律因应与制度创新

生成式人工智能的复合产品结构与自主运转功能造成了知识产权法律适用的难题与挑战。在复合产品结构下,知识产权法律难题源于生成式人工智能核心要素之算法与数据的知识产权保护问题;在自主运转功能下,知识产权法律挑战则呈现为生成式人工智能衍生内容的知识产权授权与确权问题。对此,应积极推进生成式人工智能场景下的知识产权法律变革,立足生成式人工智能核心要素与衍生内容,有针对性地展开知识产权制度创新,从而实现对生成式人工智能知识产权法律问题的合理回应。

人机争论探究法:一种争论式智能会话机器人支持的学生高阶思维能力培养模式探索

大量学生利用生成式人工智能生成作业,导致了学生高阶思维能力难以有效培养。在人机会话中嵌入“争论噪音”,将是解决这一问题的可能途径。研究采用准实验研究设计,基于高阶思维、建构主义和争论学习理论,利用文心一言、学习分析和腾讯QQ等技术工具,开发了争论式智能会话机器人,构建了人机争论探究法教学模式。结果表明,实验组的学习成绩、批判性思维能力、问题解决能力和学习态度显著优于对照组,但是创新能力效果不显著。为提高人机争论学习效果,教师需要构建特定的高阶思维学习支架,优化人机争论学习的算法机制,培养学生的人机争论素养,研制师生机三元协同争论机制。

人工智能生成内容的著作权法之辩

根据输入指令与输出内容的比例关系,可将人工智能生成内容依次分为孪生内容、伴生内容和原生内容。人工智能生成内容主要涉及三个著作权法问题。一是机器阅读的法律性质问题,法定许可说能更好地兼顾著作权专有属性与公有属性,实现权利行使与限制的动态平衡。二是生成内容的可著作权性问题,需要结合内容类型并考虑输入指令的潜在影响。同比例复制的孪生内容不属于作品,差异化表达的伴生内容属于演绎作品,创造性表现的原生内容属于原创作品。三是生成内容的著作权归属问题,除另有协议外,需要结合内容类型并考虑所有者、使用者的贡献程度。孪生内容因不属于作品,无人享有著作权;所有者、使用者对伴生内容的形成均有重大贡献,基于概括合作行为与意识,所有者、使用者共同享有著作权;只有所有者对原生内容的形成具有重大贡献,所有者单独享有著作权。