云计算技术在人工智能中的应用

文章主要介绍了云计算技术的特征和云计算实现的关键技术,并阐述了云计算数据中心建设的概况,最后提出一种用于人工智能领域文本相似度比对的云计算框架方案。结果表明,该方案具备良好的可靠性和可扩展性。     

AI下的智能语音开放创新平台设计

以智能家居为例,设计了人工智能(Artificial Intelligence,AI)云平台下的智能家居语音识别开放创新平台。采用软件即服务(Software-as-a-Service,SaaS)思想实现对平台功能的模块化设计,解决了代码和服务器存在的管理问题,同时通过调用平台接口实现语音功能,以期为智能家居的用户提供高质量的服务。     

基于人工智能的知识库应用模型构建方案的研究

结合人工智能在数据库中的应用实践,在对人工智能在数据库中应用前景、设计思想、理论基础等论述的基础上,得出了知识库的概念,进而讨论了几种知识库应用模型的构建方法。     

可解释人工智能在气象预报中的应用和展望

本研究探讨了可解释的人工智能在现代气象预报服务业务中的应用和展望。目前,AI技术在强对流监测、临近预报等方面提高了准确性,但仍存在训练数据集不完备、不平衡和模型解释性不足等问题。未来,可解释的人工智能将成为重要发展方向,提高预测模型可靠性和可信度,其与数值预报融合将成为另一趋势,提供更准确、可靠的天气预报。研究应关注解释性AI模型开发应用以及AI技术与传统数值预报融合方法,以推进可解释的人工智能在气象预报服务业务中的应用和发展。     

基于人工智能技术的智能教学系统研究与设计

ITS是人工智能技术在教育中最重要的应用。进入新世纪以来,随着复杂计算、分布式认知、模式识别、知识表示、自然语言的理解、网格计算与计算机可视化等的进步,ITS面临着又一个新的快速发展期。最后给出了一个基于Web和数据挖掘技术的ITS的功能模型、结构模型及系统实现。     

强化学习可解释性基础问题探索和方法综述

强化学习是一种从试错过程中发现最优行为策略的技术,已经成为解决环境交互问题的通用方法.然而,作为一类机器学习算法,强化学习也面临着机器学习领域的公共难题,即难以被人理解.缺乏可解释性限制了强化学习在安全敏感领域中的应用,如医疗、驾驶等,并导致强化学习在环境仿真、任务泛化等问题中缺乏普遍适用的解决方案.为了克服强化学习的这一弱点,涌现了大量强化学习可解释性(explainable reinforcement learning,XRL)的研究.然而,学术界对XRL尚缺乏一致认识.因此,探索XRL的基础性问题,并对现有工作进行综述.具体而言,首先探讨父问题——人工智能可解释性,对人工智能可解释性的已有定义进行了汇总;其次,构建一套可解释性领域的理论体系,从而描述XRL与人工智能可解释性的共同问题,包括界定智能算法和机械算法、定义解释的含义、讨论影响可解释性的因素、划分解释的直观性;然后,根据强化学习本身的特征,定义XRL的3个独有问题,即环境解释、任务解释、策略解释;之后,对现有方法进行系统地归类,并对XRL的最新进展进行综述;最后,展望XRL领域的潜在研究方向.     

形象智能与多媒体处理技术的综合研究

一引言,用计算机模拟人类智能一直是人工智能研究者所追求的目标,其研究的范畴已由模拟人类逻辑思维的智能拓展到模拟人类象思维的智能。     

从智能模拟到智能工程：论人工智能研究范式的转变

本文从人工智能涉及的一些基本概念入手,分析了基于智能模拟的研究范式的局限性,提出了一种更加实用的、工程化的研究范式：智能工程。这种研究范式强调机器智能行为的开发,特别是人机合作的智能系统的开发。它以传统人工智能理论、控制论、系统论、ａｇｅｎｔ理论、软件工程为基础,以Ｉｎｔｅｒｎｅｔ、Ｉｎｔｒａｎｅｔ为应用的舞台。人工智能的理论和实践也表明,这种基于智能工程的研究范式是符合人工智能发展趋势的。     

新一代智能煤矿人工智能赋能技术研究综述

煤炭工业与人工智能（AI）深度融合是现代化矿井实现智能少人、降本提效的重要路径,而煤炭行业全流程、全业务应用场景的AI赋能是实现煤矿智能化的具体技术措施。在当前煤矿智能化发展背景下,提出了初级智能煤矿向新一代智能煤矿演进的基本范式,对比分析了初级智能煤矿与新一代智能煤矿的组成、功能与技术内涵,揭示了新一代智能煤矿AI赋能技术的重要性及其应用实施的2个关键：煤矿工业机理AI模型与煤矿工业互联网平台。总结了关于煤矿地质、采煤、掘进、安全监控等复杂作业环节的工业机理AI模型研究现状,阐明了工业机理AI分析在智能煤矿建设中的快速发展态势。设计了新一代智能煤矿多级云边协同工业互联网平台架构,利用集团数据中心、矿井数据中心、生产系统集控中心等工业信息软硬件设施,结合海量数据云计算和少量数据边缘计算特点,提出了集团云、矿井云与环节边、场景边的多级云边协同机制。指出了未来进一步研究方向,应不断加强煤矿工业机理AI模型的开发与软件化研究,逐步形成煤矿全流程AI赋能的知识软件体系,并充分运用煤矿工业互联网平台的数字资源与信息设施,逐步实现煤矿工业互联网平台的AI技术承载。     

大数据时代人工智能在计算机网络技术中的应用研究

概述了大数据和人工智能（Artificial Intelligence,AI）的基本概念,详细探讨了其在网络技术中的优势,如大数据时代下人工智能改进了神经网络功能、提高了信息安全管理水平。最后提出了一系列应用策略,包括构建智能防火墙以及增强问题解决能力等。分析了大数据时代下AI在计算机网络技术中的潜力,为未来的研究提供了启示。     

中国医学影像人工智能20年回顾和展望

在过去20年里,医学影像技术、人工智能技术以及这两项技术相结合的临床应用都取得了长足发展。中国在该领域的研究也取得卓越成就,并且在全世界范围内的贡献比例仍在逐步提高。为了记录和总结国内同行的科研成果,本文对中国医学影像人工智能过去20年的发展历程进行回顾和展望。重点分析了国内同行在公认的医学影像人工智能领域的国际顶级刊物Medical Image Analysis(MedIA)和IEEE Transactions on Medical Imaging(TMI)以及顶级会议Medical Image Computing and Computer Assisted Intervention(MICCAI)发表的论文,定量统计了论文发表数量、作者身份、发表单位、作者合作链、关键词和被引次数等信息。同时总结了近20年中国医学影像人工智能发展进程中的重要事件,包括举办的医学影像人工智能知名国际和国内会议、《中国医学影像AI白皮书》的发布以及国内同行在COVID-19(corona virus disease 2019)期间的贡献,最后展望了中国医学影像人工智能领域未来的发展趋势。上述统计结果系统...     

Managing artificial intelligence projects: Key insights from an AI consulting firm

While organisations are increasingly interested in artificial intelligence (AI), many AI projects encounter significant issues or even fail. To gain a deeper understanding of the issues that arise during these projects and the practices that contribute to addressing them, we study the case of Consult, a North American AI consulting firm that helps organisations leverage the power of AI by providing custom solutions. The management of AI projects at Consult is a multi-method approach that draws on elements from traditional project management, agile practices, and AI workflow practices. While the combination of these elements enables Consult to be effective in delivering AI projects to their customers, our analysis reveals that managing AI projects in this way draw upon three core logics, that is, commonly shared norms, values, and prescribed behaviours which influence actors' understanding of how work should be done. We identify that the simultaneous presence of these three logics—a traditional project management logic, an agile logic, and an AI workflow logic—gives rise to conflicts and issues in managing AI projects at Consult, and successfully managing these AI projects involves resolving conflicts that arise between them. From our case findings, we derive four strategies to help organisations better manage their AI projects.     

人工智能在计算机网络技术中的应用分析

随着信息技术的快速发展,人工智能已成为引领新一轮科技革命和产业变革的战略性技术。现阶段,各个国家都在争先布局和发展人工智能,以期能在未来科技革命中抢占高点和先机。人工智能是一种模拟人脑工作的技术形式,它包含系统推荐、人工神经网络、语言处理、机器学习等方面的内容。将人工智能应用于计算机网络技术,可以节省人力资源、提升效率,可较好地弥补当前计算机网络技术在运用过程中存在的不足,进一步提升计算机网络技术水平。     

The piping of thought and the need for a permanent monitoring of the cultural effects of artificial intelligence

Over the years, AI has undergone a transformation from its original aim of producing an intelligent machine to that of producing pragmatic solutions of problems of the market place. In doing so, AI has made a significant contribution to the debate on whether the computer is an instrument or an interlocutor. This paper discusses issues of problem solving and creativity underlying this transformation, and attempts to clarify the distinction between theresolutive intelligence andproblematic intelligence. It points out that the advance of intelligent technology, with its failure to make a clear distinction betweenresolutive andcreative intelligence, could contribute to the further cultural marginalisation of human activities not connected with production. A further danger is that AI products may suffer a further loss of social reputation and prestige for those activities for which it is not possible to build artificial devices.     

Trends in distributed artificial intelligence

Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.     

Managing the risks of artificial general intelligence: A human factors and ergonomics perspective

Artificial General Intelligence (AGI) is the next and forthcoming evolution of Artificial Intelligence (AI). Though there could be significant benefits to society, there are also concerns that AGI could pose an existential threat. The critical role of Human Factors and Ergonomics (HFE) in the design of safe, ethical, and usable AGI has been emphasized; however, there is little evidence to suggest that HFE is currently influencing development programs. Further, given the broad spectrum of HFE application areas, it is not clear what activities are required to fulfill this role. This article presents the perspectives of 10 researchers working in AI safety on the potential risks associated with AGI, the HFE concepts that require consideration during AGI design, and the activities required for HFE to fulfill its critical role in what could be humanity's final invention. Though a diverse set of perspectives is presented, there is broad agreement that AGI potentially poses an existential threat, and that many HFE concepts should be considered during AGI design and operation. A range of critical activities are proposed, including collaboration with AGI developers, dissemination of HFE work in other relevant disciplines, the embedment of HFE throughout the AGI lifecycle, and the application of systems HFE methods to help identify and manage risks.     

Biological and artificial intelligence

The paper discusses the characteristics of Biological Intelligence (BI) and its differences with artificial intelligence. In particular the plasticity of the nervous system is considered in the different forms with special attention to deterministic and localizationist views of the brain vs holistic approaches. When memory and learning are considered the localizationist views do not offer a possible solution to a number of problems while memory may be better conceptualized in terms of categorization procedures and generalizing strategies. Finally, the problem of individual variability, an important feature in terms of BI, is considered. The legitimacy of analogies between BI and AI is discussed and the necessity for an innovative approach to the field of AI is stressed.     

Advancing artificial intelligence research and dissemination through conference series: Benchmark,scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained.     

基于神经网络的多媒体通信控制机制研究

在Internet网络的多媒体通信系统中需要解决QoS控制问题,如视频、音频等多媒体数据的同步、网络拥塞控制、多媒体数据传输的QoS协商控制、视频平滑,以及连续多媒体系统的CPU调度等.为了解决好这些控制问题,提出一种基于神经网络的多媒体通信控制机制,把人工智能与多媒体通信技术紧密结合起来,并在Internet网络环境下开发了实用的多媒体通信系统.运行结果表明,该系统效果优越.