Digital Application Objectives and Benefit Analysis of BIM Technology in Large-Scale Comprehensive Development Projects

This paper discusses the digital application and benefit analysis of building information model(BIM)technology in the large-scale comprehensive development project of the Guangxi headquarters base.The project covers a total area of 92,100 square meters,with a total construction area of 379,700 square meters,including a variety of architectural forms.Through three-dimensional modeling and simulation analysis,BIM technology significantly enhances the design quality and efficiency,shortens the design cycle by about 20%,and promotes the collaboration and integration of project management,improving the management efficiency by about 25%.During the construction phase,the collision detection and four-dimensional visual management functions of BIM technology have improved construction efficiency by about 15%and saved the cost by about 10%.In addition,BIM technology has promoted green building and sustainable development,achieved the dual improvement of technical and economic indicators and social and economic benefits,set an example for enterprises in digital transformation,and opened up new market businesses.

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.

Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

AI+BOPPPS模型在高职商务英语教学中的参与式学习模式设计——以综合商务英语课程为例

本研究旨在探究人工智能(AI)技术与BOPPPS教学模型相结合在高职商务英语教育中的应用,并设计了一种参与式学习环境以提升学生的课堂参与度和学习动力。通过综合商务英语课程的案例分析,本文详细阐述了如何将AI技术整合到BOPPPS模型的六个阶段中,以促进学生的主动和深度学习的积极性。

预测方法对AI的限制

今天的预测技术和人类一直依赖的推理一样,正在成为科学研究中重要的独立的驱动力。本文对二者进行比较,并指出了预测的不足,正是这种不足造就了今日AI技术发展中存在的一些天生的缺陷,最终导致实现不了agent建立完备世界模型的目标。但是,不能忽视的是,目前的AI技术已经具有缩小人与人之间智力水平的能力。

AI时代老年人智能技术应用能力提升的研究

在AI时代,适应数字化转型对老年人来说尤为关键,然而他们在智能技术的应用上存在显著挑战。通过问卷调查的研究方法,详细分析了老年人使用智能设备的现状、遇到的主要困难以及对新兴智能技术如大语言模型聊天机器人的接受度。调查结果表明,老年人普遍存在操作困难和隐私安全的顾虑,但仍对新技术表现出一定的接受度。提出了一系列提升措施,包括政府主导的线上线下教育推广、加强监管和法规制定、开发“适老化”智能产品和应用、探索大语言模型助老应用,为老年人提升智能技术应用能力提供了有效的策略和实践指导。

基于AI的自动化渗透测试平台构建

随着数字化时代的来临及强化学习、深化学习、大模型技术等的应用普及,也产生了极大的安全隐患。文中分析了基于AI的自动化渗透测试技术,并实现了渗透测试平台的架构设计、功能实现,总结了自动化渗透测试方法及其实现过程。基于大模型技术进行自动渗透测试,可显著提升渗透测试工作的高效性和系统的安全性。

基于虚拟主播AI技术的电力安全宣传创新研究

在电力安全宣传中,讲座和手册等传统方法缺乏互动性和个性化导致宣传效果不佳。通过分析虚拟主播AI技术的应用,文章探讨了利用3D建模、动作捕捉及文本到语音转换等技术的方法,创新电力安全教育模式。这些技术的集成不仅增强了教学内容的吸引力和实时反应性,还通过模拟复杂操作和紧急情景,显著提高了教育的互动性和效果。通过技术驱动解决传统宣传的局限,文章旨在实现针对性更强、反应更快的电力安全教育,以提升整个行业的安全标准和响应能力。

基于AI技术的物流仓储园区库存需求和采购预测

针对智慧物流仓储园区库存需求信息分析能力滞后的问题,提出新型的需求预测方案,实现物流仓储园区库存需求分析,通过构建新型需求分析模型动态地实现消耗评估,提高物流仓储园区库存需求分析能力。通过公式输入需求信息能够直接将物流仓储园区库存需求采购预测结果输出,并构建改进型神经网络模型,该模型融合了遗传算法模型和神经网络算法模型,提高了采购预测评估精度。实验表明,研究所提方法的采购方案成本降低了5%以上,采购方案决策能力提高了10%以上。

基于电脑与信息技术手段进行AI智慧教学的模式探究

随着大数据、AI技术以及“互联网+”时代的到来,传统的教学模式局限性日渐凸出,无法满足学生对于知识的探索与渴求。新一轮的教学信息化技术革命正在悄然发生。国家《教育信息化“十四五”规划》中明确指出未来要将信息化技术纳入教学模式改革当中,使其成为教学活动常态,实现学生人人都可随时、随地学习的信息化目标。疫情的爆发加速了这一目标的落地和实施。

智能计算时代深化电影科技创新和推进电影强国建设的思考与启示

人类社会正加快步入智能计算时代,现代智能科技对电影产业的影响持续深化和不断泛化,机器学习、深度学习、人工智能生成内容(AIGC)、大语言模型(LLM)、多模态模型等技术正在深刻影响和重构优化电影全产业链全价值链。伴随电影科技创新进入攻坚期和深水区,电影产业发展面临新形势新要求,我们要深化电影科技自主创新,积极推进电影行业的智能化升级,推进生成式人工智能、人工智能大模型、沉浸式视听等技术在我国电影行业的定制设计、自主研制与科学应用,有力支撑和服务社会主义电影强国建设。

融合SVM-LDA与加权相似度的潜在新兴技术识别研究——以人工智能领域为例

在新一轮科技革命和产业变革加速发展的大背景下,如何在新技术不断涌现的技术大海中精准找到和识别出有颠覆性潜力的新兴技术,对于国家、企业参与主体和相关商业投资机构把握科技创新发展趋势和方向、合理配置科技资源、提前进行科技战略规划与技术布局具有重要的意义。本文提出一种基于知识增强SVM-LDA(Support Vector Machine-Latent Dirichlet Allocation)的新兴技术主题识别模型。首先,基于专家小组的先验知识,制定基础技术类别划分标准;其次,将技术类别划分标准作为先验知识输入SVM-LDA模型,得到技术主题聚类结果;再其次,基于类别主题词的加权相似度计算,确定潜在新兴关键技术;最后,以人工智能领域为例进行实证研究。采用本文模型共得到24项潜在新兴技术,主要分布在特种机器人技术、监测预警技术、视频图像处理技术、语音识别技术、自动规划和决策技术以及自然语言处理技术6个大类方向。

手语数字人技术及智能应用研究

对智谱AI、腾讯、华为等头部企业打造的手语数字人产品进行梳理,研究和探讨我国代表性手语数字人产品的主要技术路线和智能应用。手语数字人的制作流程包括形象设计、建模、动作捕捉、驱动、渲染。通过多模型、多算法等AI技术集成实现手语数字人应用场景落地。智谱AI手语数字人核心技术含seq2seq模型、生成对抗网络,创造性地将对比学习引入mBart模型等。当前我国手语数字人在新闻播报、教育、国际赛事和重要会议翻译等方面实现智能应用。未来我国手语数字人技术有望迎来新突破,智能应用也将拓展到智慧医疗、智慧交通等更多领域。

国内外大语言模型的图书情报应用探讨

在人工智能(AI)技术不断发展的背景下,大语言模型(LLM)如OpenAI的GPT系列等已经引发了广泛关注,在包括图书情报在内的许多领域都有潜在应用价值。文章探讨了国内外LLM在文献分类、文摘生成、读者推荐等图书情报任务中的成功应用案例,在分析LLM应用潜力的基础上,进一步分析了知识整合、结构化数据利用、学术真实性检验、AI科学家等LLM应用新方向。但LLM在图书情报领域的应用仍面临计算资源需求大、泛化能力有限的挑战,因而成功利用LLM有赖于图书情报领域的技术和管理创新,需要谨慎推进。

人工智能教育大模型:体系架构与关键技术策略

以大模型为代表的生成式人工智能技术为破解教学难题、革新教学样态带来了机遇。虽然大模型以其强大的泛化性能和跨领域的适应性,能够在多类场景中展现出强大能力,但若仅依靠海量数据训练带来的优势,往往很难满足教育场景下更高的知识准确度、价值观与安全性要求。为此,能够满足教育特定场景任务需要的人工智能教育大模型应运而生,能有效提升教育质量和推进个性化、智能化教学。本文综合分析了人工智能教育大模型的发展现状、概念内涵、技术特征和体系框架,旨在为人工智能教育大模型的未来发展提供理论与技术参考。当前,教育大模型的竞争力主要取决于技术和算力、教育数据和理解、教育场景和应用等,本文进而提出建设与应用框架及关键技术策略,以期推动中国版人工智能教育大模型在大规模因材施教、创新性与个性化教学中的应用,加快教育数字转型和智能升级。

认知的展褶:生成式人工智能的技术审思——以Sora为引

文生视频大模型Sora超越了人工智能的文本表达结构,不断强化图像与视频的价值意义,其话语建构能力日益趋近人类的创作水平。这种“超能力”背后隐喻着“技术”的本质。生成式人工智能的技术机理主要通过资源堆叠、机器学习与序列预测来实现,其中资源堆叠奠定了智能根基、机器学习促进了知识萃取、序列预测形塑内容体系。世界模拟想象和人机关系演进共同构筑了生成式人工智能的意义价值。然而,随着生成式人工智能的普泛化渗透,其遮蔽的想象塌缩、资源消耗以及内容速朽等困境日渐凸显,亟须通过价值对齐、黑箱稀释等实践方式回应人机协同进化的时代命题。

人工智能技术在纺织服装图案设计领域的应用

文章介绍了人工智能技术在纺织服装图案设计领域的应用现状和发展趋势,分析了人工智能技术在提高纺织服装图案设计效率和质量方面的优势,以及在算法、技术、数据等方面面临的挑战。文章以中国纺织信息中心基于GAN和扩散模型等不同模型生成的纺织服装图案设计平台为例,展示了其在流行趋势预测、品牌合作、个性化定制等方面的应用效果。

Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model

In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

AI赋能的P4ST决策智能分析:寻找知识服务的新质生产力

1挑战“低质生产力陷阱”?ChatGPT、Sora、Claude-3等的爆发和迅速迭代,带来了文本、图像、视频生成领域的根本性变革,还激发了基于大语言模型(Large Language Models,LLM)的知识搜索、汇集、归纳、提炼、生成等知识生产力工具和智能副驾(Copilot)的日益普及。在科技领域,AI4S(AI for Science)快速发展[1],出现了多种科学大语言模型[2],机器人科学家和智能实验室快速成为现实[3],造就了科研范式5.0[4]。

试析AI数据分析技术对零售市场终端监测和反馈的作用

近年来,线下零售企业面临着巨大的挑战,零售市场终端监测和反馈更显意义重大,而AI数据分析技术为终端监测和反馈提供了新思路。本文首先阐述了零售众包模式和流程,指出了关键要素,并重点分析终端监测和反馈过程中,AI数据分析技术的具体作用,包括提升监测数据分析效率、节省市场终端监测成本、加强分析结果的可靠性等。最后,对零售企业商业模式的发展策略展开了探讨,以期为优化零售市场终端监测提供启示与借鉴。