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.

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

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.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Regarding the Regulatory Sandbox Route and Mechanism for Governance of Artificial Intelligence

The main challenge in AI governance today is striking a balance between controlling AI dangers and fostering AI innovation.Regulators in a number of nations have progressively extended the regulatory sandbox,which was first implemented in the banking sector,to AI governance in an effort to reduce the conflict between regulation and innovation.The AI regulatory sandbox is a new and feasible route for AI governance in China that not only helps to manage the risks of technology application but also prevents inhibiting AI innovation.It keeps inventors'trial-and-error tolerance space inside the regulatory purview while offering a controlled setting for the development and testing of novel AI that hasn't yet been put on the market.By providing full-cycle governance of AI with the principles of agility and inclusive prudence,the regulatory sandbox offers an alternative to the conventional top-down hard regulation,expost regulation,and tight regulation.However,the current system also has inherent limitations and practical obstacles that need to be overcome by a more rational and effective approach.To achieve its positive impact on AI governance,the AI regulatory sandbox system should build and improve the access and exit mechanism,the coordination mechanism between the sandbox and personal information protection,and the mechanisms of exemption,disclosure,and communication.

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.

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+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。

生成式AI对个人信息保护的挑战与风险规制

生成式AI的技术特性使其对《个人信息保护法》建构的个人信息保护体系产生了挑战,主要包括:生成式AI虽然解决了让语言模型使用超大体量无人工标注数据进行预训练的难题,但其采用的技术路线也使大语言模型成为完全的“技术黑箱”,进而使开发者难以遵守个人信息处理的知情同意规则;无论是依据“目的限定原则”还是“场景理论”,生成式AI的技术特性都使其难以满足在“合理范围”内处理已公开个人信息的法定要求;生成式AI的技术特性使大语言模型的输入端和输出端都存在对信息主体的敏感个人信息权益和个体隐私权的侵害风险。我们应该基于“包容审慎”的基本风险规制理念,通过调整生成式AI领域知情同意规则的适用方式、重塑生成式AI领域已公开个人信息的处理规则、设立生成式AI领域个人信息中人格权保护的行政规制措施等途径,实现创新技术发展和个人信息保护之间的平衡。

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。

AI赋能高校图书馆深度数字阅读推广的路径探索

AI技术迅猛发展并赋能各行各业,为高校图书馆数字阅读推广向深度拓展提质提供了新思路。结合研究现状及已有实践,从构建智能化知识服务流程、打造多模态应用场景支撑、设计沉浸式交互式活动、分析转化用户黏性活跃度、协作共建推广生态体系等方面,探索了AI技术赋能高校图书馆深度数字阅读推广的有效路径。

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。