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。     

浅析ChatGPT:历史沿革、应用现状及前景展望

近年来,人工智能技术接连取得突破,尤其是在强化学习、大规模语言模型和人工智能生成内容技术等方面,正逐步成为各个行业的创新驱动力。OpenAI于2022年11月30日发布的ChatGPT由于具有惊人的自然语言理解和生成能力,引起全社会大范围的关注,成为全球热议的话题,并被广泛应用于各个行业。仅两个月后,ChatGPT的月活跃用户数便达1亿,成为史上用户数增长最快的消费者应用。鉴于ChatGPT目前造成的影响,对其进行全面的分析较为必要。本文从历史沿革、应用现状和前景展望这3个角度对ChatGPT进行剖析,探究其对社会的影响、技术的原理和挑战以及未来发展的可能性,并从模型能力的角度简要介绍GPT-4相对于ChatGPT的改进。作为一个现象级技术产品,从技术角度而言ChatGPT对相关领域具有里程碑式的重要意义,从应用角度而言其可能会给人类社会带来巨大的影响。ChatGPT有潜力成为计算机领域最伟大的成就之一。但就目前而言,ChatGPT仍然存在一些局限,尚未达到强人工智能的水平。在当前阶段,研究人员需要对人工智能技术持有自信和谦虚学习的态度,继续发展相关的技术研究和应用。     

多媒体智能：当多媒体遇到人工智能

过去10年中涌现出大量新兴的多媒体应用和服务,带来了很多可以用于多媒体前沿研究的多媒体数据。多媒体研究在图像/视频内容分析、多媒体搜索和推荐、流媒体服务和多媒体内容分发等方向均取得了重要进展。与此同时,由于在深度学习领域所取得的重大突破,人工智能(artificial intelligence,AI)在20世纪50年代被正式视为一门学科之后,迎来了一次“新”的发展浪潮。因此,一个问题就自然而然地出现了：当多媒体遇到人工智能时会带来什么？为了回答这个问题,本文通过研究多媒体和人工智能之间的相互影响引入了多媒体智能的概念。从两个方面探讨多媒体与人工智能之间的相互影响：一是多媒体促使人工智能向着更具可解释性的方向发展;二是人工智能反过来为多媒体研究注入了新的思维方式。这两个方面形成了一个良性循环,多媒体和人工智能在其中不断促进彼此发展。本文对相关研究及进展进行了讨论,并围绕值得进一步探索的研究方向分享见解。希望可以对多媒体智能的未来发展带来新的研究思路。     

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。     

基于人工智能的计算机网络异常数据挖掘方法

针对现有网络异常数据检测方法存在异常数据挖掘精准度较低的问题,本文设计基于人工智能（Artificial Intelligence,AI）的计算机网络异常数据挖掘方法。确定出网络异常数据属性集,全方位分析计算机网络数据;在属性集中提取出计算机网络异常因子,分析异常数据的特点;利用AI技术构建异常数据挖掘模型,在缩短数据挖掘时间的基础上,提高数据挖掘精准度,进而得到更加有效的计算机网络异常数据。采用对比实验的方式,验证了该方法的数据挖掘精准度更高,网络运行可靠性更强,极具推广价值。     

人工智能技术下计算机网络安全防护系统的设计和实现

互联网时代,计算机网络实现了信息的高效交互与通信,为人们带来了巨大便利。但是,随之而来的安全问题不容小觑,各种漏洞、木马病毒等因素不时影响着网络安全,威胁着人们信息安全和财产安全,严重影响着计算机网络深度应用。因此,文章基于人工智能（Artificial Intelligence,AI）技术赋能下,采用分层架构设计计算机网络安全防护系统,以期提升计算机网络安全防护能力。     

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。     

人工智能技术在天线设计中的应用综述

精确计算各个尺寸参数数值是天线设计过程中的一个关键问题,需要丰富的理论和经验来确定等效场路模型,研究采用人工智能(AI)技术进行天线参数的有效设计,避免复杂的电磁计算过程,获得天线电磁特性与物理尺寸之间的非线性映射关系,是天线智能化设计的一个发展动向。通过总结近年来人工神经网络(ANN)模型应用于天线设计的技术方案,分析了人工智能化天线结构设计的基本方法,阐述了人工智能化天线参数预测研究中存在的问题,讨论了未来人工智能化天线设计的研究方向。     

CISOC—化学信息和人工智能系统：十年回顾与展望

本文从 CISOC 概念,图论和化学结构处理,数据库,结构-活性关系研究系统和推理机器等方面回顾了 CISOC 系统十年来的进展,同时展望了 CISOC 的未来发展。     

基于AI识别和物联网的住宅安防系统

采用住宅安防系统是构建安全、舒适居住环境的重要策略。近年来,随着住宅智能安防系统需求的增多,以及人工智能（Artificial Intelligence,AI）技术、物联网技术等相关技术的深入应用,开发基于AI识别和物联网的住宅安防系统已成为当前的研究重点。笔者首先分析了住宅安防系统开发与应用的关键技术,其次分析了基于AI识别和物联网的住宅安防系统的设计思路,以期为相关研究提供借鉴。     

医学影像人工智能辅助诊断的样本增广方法

针对不同领域人工智能（AI）应用研究所面临的采用常规手段获取大量样本时耗时耗力耗财的问题,许多AI研究领域提出了各种各样的样本增广方法。首先,对样本增广的研究背景与意义进行介绍;其次,归纳了几种公知领域（包括自然图像识别、字符识别、语义分析）的样本增广方法,并在此基础上详细论述了医学影像辅助诊断方面的样本获取或增广方法,包括X光片、计算机断层成像（CT）图像、磁共振成像（MRI）图像的样本增广方法;最后,对AI应用领域数据增广方法存在的关键问题进行总结,并对未来的发展趋势进行展望。经归纳总结可知,获取足够数量且具有广泛代表性的训练样本是所有领域AI研发的关键环节。无论是公知领域还是专业领域都进行样本增广,且不同领域甚至同一领域的不同研究方向,其样本获取或增广方法均不相同。此外,样本增广并不是简单地增加样本数量,而是尽可能再现小样本量无法完全覆盖的真实样本存在,进而提高样本多样性,增强AI系统性能。     

An application of artificial intelligence to medical robotics

In this paper an application of Artificial Intelligence (AI) to Medical Robotics is described. Namely, a specific AI technique is employed to generate a sequence of operations understandable by the control system of a robot which is to perform a semi-automatic surgical task. According to this technique, a planner is implemented to translate the “natural” language of the surgeon into the robotic sequence that should be executed by the robot. A robotic simulator has been implemented in order to test the planned sequence in a virtual environment. The planned sequence is then to be input to the medical robotic system, which will execute the surgical operation. The work described in this paper features a high level of originality, since no similar applications of AI to medical robotics could be found in the scientific literature.     

新型冠状病毒肺炎(COVID-19)医学影像AI诊断研究进展

2020年3月,世界卫生组织（World Health Organization,WHO）宣布新型冠状病毒肺炎（corona virus disease 2019,COVID-19）为世界大流行病,疫情的爆发给世界各地医疗系统带来巨大压力。现有的COVID-19诊断标准是核酸检测阳性,然而核酸检测假阴性率高达17%~25.5%,为避免漏诊,需要采用基于影像学的AI诊断方法筛查大量疑似病例,扼制疾病传播。本综述将回顾疫情爆发数月以来,基于医学影像的新冠肺炎AI辅助诊断的研究成果。首先介绍CT（computed tomography）和X光片的优缺点,以及COVID-19的放射学特征,然后对数据准备、图像分割和分类识别等AI诊断的关键步骤分别进行阐述,最后介绍COVID-19的跟踪和预后（预先对疾病后续发展过程及结果的判断和估计）。本文还整理了部分公开的COVID-19相关数据集,并对数据标注不足的问题提供了弱监督学习和迁移学习等解决方案。实验验证,AI系统诊断COVID-19的敏感性达到97.4%,特异性达到92.2%,优于放射科医生的诊断结果。其中表现尤为突出的是基于语义分割网络检测COVID-19感染区域,由此可以定量分析感染率。AI系统可以辅助医生诊断和治疗COVID-19,提高放射科医生阅读X光片和CT的效率。     

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.     

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.     

人工智能范式的革命与通用智能理论的创生

人工智能的研究取得了不少可喜的进展,也面临着许多严峻的挑战.为了应对这些挑战,学术界提出了各种各样的研究思路.笔者相信,每种思路都有其合理之处,都有可能获得一定的成效.不过,根据笔者的理解,人工智能面临的最深刻最严峻的挑战,是学科和时代的大转变所带来的大阵痛:人工智能范式的张冠李戴.因此,必须对人工智能的范式实施"正冠...     

Connecting the Dots in Self-Supervised Learning: A Brief Survey for Beginners

The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the "dots" of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.     

深度学习在医学影像智能处理中的应用与挑战

利用深度学习方法对医学影像数据进行处理分析,极大地促进了精准医疗和个性化医疗的快速发展。深度学习在医学图像领域的应用较为广泛,具有多病种、多模态、多组学和多功能的特点。为便于对深度学习在医学图像处理领域的应用进行更深入有效的探索,本文系统综述了相关研究进展。首先,从深度学习在影像基因组学中的应用出发,理清了深度学习在医学影像领域应用的一般思路和现状,将医学影像领域分为智能诊断、疗效评估和预测预后等3个模块,并对模块内的各病种进行总结,展示了深度学习各算法的优缺点及面临的问题和挑战。其次,对深度学习中出现的新思路、新方法以及对传统方法的改进进行了阐述。最后,总结了该领域现阶段面临的问题,并对未来的研究方向做出了展望。基于深度学习的医学图像智能处理与分析虽然取得了一些有价值的研究成果,但还需要根据临床的实际需求,将深度学习与经典的机器学习算法及无创并且高效的多组学数据结合起来,对深度学习的理论和方法进行深入研究。     

Conversational artificial intelligence in the AEC industry: A review of present status,challenges and opportunities

The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry.