Innovation and Practice of Training Mode for Professional Postgraduates of Acupuncture and Tuina Based on Artificial Intelligence

In view of the common problems of integrating artificial intelligence into the training of postgraduates in Acupuncture and Tuina major,this paper reviews the related research progress both at home and abroad.It puts forward the innovative reform paths for integrating artificial intelligence into postgraduate training mode of Acupuncture and Tuina major:construct the teaching staff of artificial intelligence graduate students;innovating artificial intelligence to promote the integration of classics and scientific research;constructing the ideological and political case base of artificial intelligence courses;implementing artificial intelligence platform blended teaching;building a domestic and foreign exchange platform for artificial intelligence.Through practical research in teaching,it has achieved good teaching results and played a good demonstration,leading and radiation role in similar majors in China.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Artificial intelligence for disease diagnostics still has a long way to go

Artificial intelligence(AI)can sometimes resolve difficulties that other advanced technologies and humans cannot.In medical diagnostics,AI has the advantage of processing figure recognition,especially for images with similar characteristics that are difficult to distinguish with the naked eye.However,the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues.In this letter,regarding an original study presented by Takayama et al,we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images,including the recognition of non-obvious difference between the normal parts and pathological ones,which were impossible to be distinguished by naked eyes,such as the basic constitutional elements of pixels and grayscale,special molecules or even some metal ions which involved into the diseases occurrence.

Current applications of artificial intelligence-based computer vision in laparoscopic surgery

Recent advances in artificial intelligence(AI)have sparked a surge in the application of computer vision(CV)in surgical video analysis.Laparoscopic surgery produces a large number of surgical videos,which provides a new opportunity for improving of CV technology in laparoscopic surgery.AI-based CV techniques may leverage these surgical video data to develop real-time automated decision support tools and surgeon training systems,which shows a new direction in dealing with the shortcomings of laparoscopic surgery.The effectiveness of CV applications in surgical procedures is still under early evaluation,so it is necessary to discuss challenges and obstacles.The review introduced the commonly used deep learning algorithms in CV and described their usage in detail in four application scenes,including phase recognition,anatomy detection,instrument detection and action recognition in laparoscopic surgery.The currently described applications of CV in laparoscopic surgery are limited.Most of the current research focuses on the identification of workflow and anatomical structure,while the identification of instruments and surgical actions is still awaiting further breakthroughs.Future research on the use of CV in laparoscopic surgery should focus on applications in more scenarios,such as surgeon skill assessment and the development of more efficient models.

Advances in teleophthalmology and artificial intelligence for diabetic retinopathy screening:a narrative review

Background and Objective:Advances in teleophthalmology and artificial intelligence(AI)for diabetic retinal screening is of growing public health interest.Currently,only 30–40%of patients with diabetes adhere to recommended diabetes screening guidelines.To enhance early detection and reduce vision threatening complications,there has been a growing number of teleophthalmology programs and novel AI algorithms with the aim to improve eye care access.The purpose of this review is to assess current literature on teleophthalmology and AI for use in diabetic retinopathy(DR)screening,and to discuss advances and barriers to these innovative technologies.Methods:Literature review involving teleophthalmology and AI for DR screening,with focus on the past decade.Key Content and Findings:Teleophthalmology has demonstrated the ability to increase DR screening rates,enable earlier eye care access,and reduce healthcare costs.Novel AI-based DR screening programs appear accurate and effective,but detection of other ocular pathologies is still under development and not yet approved in the United States.Logistical,technological,financial,and legal barriers limit widespread adoption and long-term sustainability of teleophthalmology programs.Conclusions:The use of teleophthalmology and AI algorithms expands eye care access and helps prevent vision loss from DR and potentially other sight threatening conditions.Transparency in the process utilized for arriving at a particular diagnosis or decision to refer,often referred to as the“black box”,remains a multifaceted issue within the field of telemedicine for developing trust and improving patient-centered outcomes.

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.

Artificial Intelligence Adoption for Cybersecurity in Africa

Legacy-based threat detection systems have not been able to keep up with the exponential growth in scope, frequency, and effect of cybersecurity threats. Artificial intelligence is being used as a result to help with the issue. This paper’s primary goal is to examine how African nations are utilizing artificial intelligence to defend their infrastructure against cyberattacks. Artificial intelligence (AI) systems will make decisions that impact Africa’s future. The lack of technical expertise, the labor pool, financial resources, data limitations, uncertainty, lack of structured data, absence of government policies, ethics, user attitudes, insufficient investment in research and development, and the requirement for more adaptable and dynamic regulatory systems all pose obstacles to the adoption of AI technologies in Africa. The paper discusses how African countries are adopting artificial intelligence solutions for cybersecurity. And it shows the impact of AI to identify shadow data, monitor for abnormalities in data access and alert cyber security professionals about potential threats by anyone accessing the data or sensitive information saving valuable time in detecting and remediating issues in real-time. The study finds that 69.16% of African companies are implementing information security strategies and of these, 45% said they use technologies based on AI algorithms. This study finds that a large number of African businesses use tools that can track and analyze user behaviour in designated areas and spot anomalies, such as new users, strange IP addresses and login activity, changes to permissions on files, folders, and other resources, and the copying or erasure of massive amounts of data. Thus, we discover that just 18.18% of the target has no national cybersecurity strategy or policy. The study proposes using big data security analytics to integrate AI. Adopting it would be beneficial for all African nations, as it provides a range of cyberattack defense techniques.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain

Artificial Intelligence (AI) experienced significant advancements in recent years, and its potential power is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential influence of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather information on GPT-3’s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

AI赋能视角下的信息行为研究--2023年信息行为研究年会综述

2023年12月23—24日,2023年信息行为研究年会在北京大学召开,来自全国70多所高校的200余位知名专家学者和学生参加了会议。本次年会以“AI赋能视角下的信息行为研究”为主题,设置了主旨报告、专题讨论、海报展示、分论坛报告、眼动追踪工作坊等环节,围绕信息行为研究领域在新一代人工智能(AI)技术浪潮下的理论创新、方法应用、情境拓展、人本关怀、社会发展等方面进行深入研讨。文章综述了与会者的学术成果与主要观点,为信息行为研究的发展与创新提供理论启迪与研究指引。

基于AI的肺磨玻璃结节中医临床特点研究

目的通过客观、规范的方法分析肺磨玻璃中医临床特点,利用人工智能(AI)分析肺磨玻璃结节恶性风险,初步探究不同风险肺磨玻璃结节中医临床特点,为中医药干预肺结节提供参考。方法采集75例肺磨玻璃结节患者中医四诊信息,运用证素辨证方法提取证素;利用AI判读患者胸部CT,分析不同风险肺磨玻璃结节中医临床特点。结果肺磨玻璃结节患者中医证素以阴虚(77.33%)、气虚(56.00%)、阳虚(54.67%)、血虚(52.00%)等证素多见,亦可见痰(40.00%)、寒(37.33%)、气滞(32.00%)等实性证素;AI判读为高风险组较中低风险组气虚证素权值差异有统计学意义(P<0.05)。结论肺磨玻璃结节患者中医辨证以虚证为主,中医病机为气虚或气滞导致肺之气机不畅,引起局部痰、湿聚集于肺络而形成,而热证、瘀血证较少。

AI+BCI硅基碳基融合新智能的开始

我们正迎来人类发展的第四次浪潮,正处于从信息社会向人类社会-物理世界-信息空间融合的智能社会的关键转型期。近年来,计算和信息技术飞速发展,深度学习的空前普及和成功将人工智能(AI)确立为人类探索机器智能的前沿领域。与此同时,得益于器件的革命性进展和人工智能(AI)的发展,脑机接口(BCI)植入技术同样快速落地,这意味着BCI+AI碳基硅基融合的开始,然而,硅基和碳基运算的底层逻辑存在根本差异,脑的智能机制仍有待进一步探索。本研究提出的视觉认知引导的孪生AI深度网络,是由个人意识驱动的深度网络技术,通过捕捉并解析个体的思维模式和创意灵感,为每个用户量身打造独特的视觉世界。在这样的环境中,每个人都成为自己创造世界的视觉主导者,打破物质和意识的壁垒,得以展现丰富的个性和创造力。

AI视域下图书馆视听资源智慧化加工的探索

新技术环境下网络视听服务蓬勃兴起,深刻影响和推动公共文化服务行业的变革与发展。视听资源是图书馆立体馆藏资源体系的重要组成部分,与图书馆传统的平面化文献资源相比,其立体化的资源呈现形式更具感染力、亲和力和传播力。论文探讨人工智能技术在图书馆视听资源的应用场景、实现功能,特别对人工智能技术在视听资源内容自动化识别、知识内容发现、知识体系构建、知识关联服务等方面的探索与应用进行了探讨,以期为其他机构在拓展智慧图书馆多维服务领域上开辟新的应用范围提供思路与参考。

AI绘画在地图制图中的应用与挑战

进入信息时代,地图制图呈现大众化趋势,大众制图时代来临。人人都能制作地图,但制图质量却参差不齐,如何实现地图制图由专家主导的“授人以鱼”扩展为面向大众的“授人以渔”成为当前的研究热点,迫切需要发展智能化、个性化的制图方法。AI绘画通过机器学习和深度学习等技术,使计算机能够模拟人类艺术家的创造力进行图像创作、编辑和合成图像。AI绘画能否为智能化、个性化制图提供一种新的技术途径?针对这一问题,首先梳理出了由文本生成图像、图像编辑、草图转化为图像、风格化神经渲染、图像风格迁移5类典型AI绘画技术。然后,逐一分析这些技术在游戏地图绘制、地图脱密脱敏、心象地图绘制、艺术感地图绘制、地图风格迁移等方面的应用。最后,总结AI绘画在地图制图中面临的地图内容理解、制图知识规则嵌入以及结果地图的可读性等挑战,以期丰富对AI地图制图的讨论,启发创新性AI制图应用与研究。

AI-GPT技术驱动的元宇宙图书馆架构探讨

人工智能的快速发展引领着全新的信息传播模式,图书馆作为知识的存储和传播中心也在不断适应这个时代的变化向元宇宙转型,而AI-GPT技术将成为元宇宙图书馆的技术引擎,成为驱动元宇宙图书馆的动力。本文结合AI-GPT技术特点探讨了元宇宙图书馆的技术框架,并结合“书理学”三原则从数据管理系统、用户界面和协作与分享平台三方面分析了AI-GPT时代元宇宙图书馆的技术结构,通过对上述问题的探讨以助力元宇宙图书馆建设。

CCTA-AI联合FFR-CT诊断冠状动脉狭窄病变的应用价值研究

目的评估计算机断层扫描血管成像(CCTA)与人工智能(AI)联合心肌灌注分数(FFR-CT)对冠状动脉狭窄病变的诊断价值。方法选择于2022年1月至2023年2月到南阳市中心医院接受诊治的80例疑似冠状动脉狭窄病变患者作为观察对象。所有患者均接受CCTA以及冠状动脉造影(CAG)检查,其检查的间隔时间应在14天内,然后通过AI软件自动对CCTA的图像进行重建和计算,同时计算FFR-CT的数值。计算人工智能辅助的心脏计算机断层扫描(CCTA-AI)联合FFR-CT诊断的敏感度、特异度、准确度、阳性预测值和阴性预测值。结果80例患者经CAG检查确诊,72例患者为冠状动脉狭窄病变,占比为90%;8例患者为非冠状动脉狭窄病变,占比为10%。CCTA-AI联合FFR-CT检查的敏感度、特异度、阳性预测值、阴性预测值、准确度以及AUC值均高于CCTA-AI、FFR-CT单独检查,差异有统计学意义(P<0.05)。结论采用CCTA-AI联合FFR-CT检查诊断冠状动脉狭窄病变具有较高的诊断效能,其敏感度及特异度均得到显著提高。

生成式AI模型人智共创中的对齐问题研究

目的以人工智能对齐的视角,探讨在人智共创中生成式AI模型如何对齐设计师的意图。方法通过要素分析,以人工智能对齐问题中的可解释性与可控性为研究视角,探讨生成式AI技术作为辅助设计工具如何对齐设计求解过程中“探索-创新-评估”三个设计阶段的具体意图与需求,并分析对齐阶段中需要解决的对齐问题。结果根据对齐阶段的任务构建基于人智设计概念表征交互式对齐方法、表征拓展方法与表征评估方法。结论在三个对齐阶段中分别构建意图对齐、设计空间拓展和设计规则匹配这三种方法,帮助设计师构建可控、可解释的人智共创方法,从而构建可控、可信的人智共创。

论复杂人工智能生成物在著作权法的定性--兼评“AI文生图著作权案”

对于AI生成物的可版性认定,我国司法实践采取了“简单—复杂”场景二分的裁判思路。然而,囿于对独创性概念的有限阐释,司法机关并未对该问题形成逻辑融贯的论证,进而引发争议。对此,有必要以复杂生成场景为定位,并沿袭独创性概念的规范阐释脉络,从客观、主观和行为三个维度来回应争议:第一,著作权法并仅不限于保护“大师”作品,生成式AI“排列组合式”的创作也能满足客观面向的“一定美感”;第二,复杂AI生成物具有高度定制化的特征,因此具备主观面向的“个性因素”,“人机二分说”将工具误认为主体,“算法唯一说”缺乏对创作流程的完整考量;第三,《著作权法实施条例》第三条第一款的“直接”应解释为“主要贡献”而非“直接决定”,后者既与域内外的一贯实践不符,也有悖于社会化创作的现代艺术发展规律。

基于生成式AI的智能制造专业型应用框架研究与应用

智能制造是全面实现现代化的重要保障。随着人工智能技术的不断发展,如何利用生成式AI进一步促进智能制造质量与效率的提升是十分重要的研究课题。本文按照智能制造生命周期维度划分,结合生成式AI的基本思想和原理,提出了基于生成式AI的智能制造专业型应用框架(SMPF-GAI),并通过案例分析了生成式AI在智能制造中的应用潜力。该框架系统地整合了智能制造的关键阶段与生成式AI的核心原则,描绘了生成式AI在智能制造中的运行机理和应用路线。本研究为智能制造领域的研究人员、从业者和决策者提供了新的见解,为有效开展智能制造工作、提高智能制造建设水平提供理论支撑及方法指导。

AI产品侵权归责原则的法经济学考察——兼评欧盟《人工智能责任指令》

AI产品是以AI技术为基础,以销售、使用为目的,依赖研发者设定的固定程序,通过计算进行决策并完成相应任务,被广泛应用于各行业的动产领域;AI产品侵权归责原则大致可分为共同责任侵权原则、严格责任原则和过错侵权原则三类。在适用侵权归责原则时,常见的困境包括共同侵权责任原则缺乏归责上的“共同性”,严格责任原则具有“片面性”,以及过错责任原则难以确定“过错”与“因果关系”。此外,责任主体尚不明确。基于社会成本与委托代理理论,AI产品侵权归责理论可从制度激励、信息不对称与风险分散三个维度解析归责的基本逻辑,以实现社会福利最大化。欧盟《人工智能责任指令》在一般条款外仅规定了举证责任、证据披露和因果关系推定等要素,对产品侵权问题的规制存在漏洞,它所确立的严格责任制度仍具有补充和提升空间。为实现欧盟《人工智能责任指令》下的社会福利最大化,尝试将过错责任原则适用于生产者,将剩余责任即严格责任原则适用于驾驶员,即若生产者存在过错,驾驶员和生产者将以各自的责任份额分担责任;如果生产者被证明遵循了应有的注意义务,那事故损失必须完全由被适用严格责任原则的驾驶员承担。