语义Web运作机制的认知语义学基础

人工智能与人类智能本质上的不同在于对语义的理解上。人工智能尚不完全具备人类智能对语义理解的意向性或语义性,更欠缺人类理解语义时的思维推理能力。本文从讨论语义Web的运作机制入手,探讨其运作机制的认知语义学基础,旨在阐释人类对语义理解的认知过程,以期能将此认知过程应用到人工智能语义Web的运作设计中,最终使其更加具有人类的意向性。     

类脑智能研究现状与发展思考

近年来人工智能研究的许多重要进展反映了一个趋势:来自脑科学的启发,即使是局部的借鉴都能够有效地提升现有人工智能模型与系统的智能水平。然而,想要真正逼近乃至超越人类水平的人工智能,还需要对脑信息处理机制更为深入的研究和借鉴。类脑智能研究的目标就是通过借鉴脑神经结构及信息处理机制,实现机制类脑、行为类人的下一代人工智能系统。文章从受脑启发的新一代人工神经网络、基于记忆、注意和推理的认知功能模型、基于生物脉冲神经网络的多脑区协同认知计算模型等角度,并结合研究团队在类脑智能领域的研究进展,论述类脑智能的研究进展、发展方向和对未来发展的思考。     

Considerations for development and use of AI in response to COVID-19

Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions.     

The ethical use of artificial intelligence in human resource management: a decision-making framework

Ethics and Information Technology - Artificial intelligence (AI) is increasingly inputting into various human resource management (HRM) functions, such as sourcing job applicants and selecting...     

全球视角下中国人工智能研究可视化分析

人工智能已经成为下一轮工业革命的核心内容,各国争先对其进行部署和发展。为了更好地了解我国人工智能领域的现状和发展,本文基于知识图谱的研究方法,以Web of Science和CNKI中人工智能相关文献作为样本数据,运用CiteSpace软件,通过双图叠加分析、共词分析和突现词分析等功能,从全球的视角对我国人工智能研究近十年来的发展面貌、特点和演化历程做出了可视化的分析。研究发现：人工智能的研究已经呈现出了多学科和学科融合发展的态势;我国在人工智能领域的发展已经取得成果,但距离顶尖还有差距;我国对人工智能应用的研究更为丰富,这与我国的丰富的数据资源和应用场景有关;未来我们应更注重人才的培养,对基础技术的长期支持和投入,同时期待人工智能技术能够带来更为深刻的变革。     

政策工具视角的中国人工智能产业政策量化分析

人工智能是全球第四次工业革命的关键动力,将可能成为未来产业转型的核心。本文以2012年以来新一轮人工智能技术革命为背景,以中国国家层面的21项人工智能相关产业政策为样本,基于政策工具和创新过程的二维分析框架,采用内容分析法,对当前中国人工智能产业政策制定的现状与存在问题进行计量与分析。研究发现：中国人工智能产业政策需增加需求侧政策工具,并随着产业的成熟制定更多面向人工智能的商业化阶段的政策,尽早开展面向人工智能产业的政策布局。     

The role of data for AI startup growth

Artificial intelligence (AI)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find a positive correlation between having proprietary training data and obtaining future venture capital funding. Moreover, this correlation is greater for startups in markets where data is a major advantage and for startups using more sophisticated algorithms, such as neural networks and ensemble learning.     

A perspective on natural and artificial intelligence

Abstract

Intelligence is an attribute that has, since time immemorial, drawn the line of distinction between man and machine. Artificial Intelligence (AI) refers to the endeavor of the former to introduce some of this special faculty into the latter. Just as natural intelligence has undergone major changes as regards its definitions and understanding, so has the field of AI. In order to encompass the gamut of this fundamental capability and know its origins, AI researchers have often had to deal with psychological and philosophical viewpoints on the issue. From the point of view of cognitive psychology, the Information Processing (IP) paradigm and IP systems are of special interest, and we present a brief overview of these topics. While the AI community claims to have some understanding of the concept of knowledge, the idea of consciousness, which we consider of finer grain than any other, has received little practical attention. These related terms are discussed at length in the article. Further, of late there has been a movement toward incorporating a background of common‐sense reasoning in AI systems. We emphasize the importance of this trend, especially in distributed AI. The basics of adaptability and learning are also discussed. We sum up the ideas presented and link them to the current progress in AI research with specifics aimed at making it more dynamic.     

人工智能对就业影响研究的源起、热点与趋势——基于知识图谱文献计量方法

人工智能已成为新一轮产业变革的核心驱动力、经济发展的新引擎和社会发展加速器,必将对就业产生深远影响。近年来,人工智能对就业的影响受到国内外学者的广泛关注。本文应用Citespace软件,对CNKI数据库和WOS核心集合中关于人工智能影响就业主题的文献进行可视化分析,发现核心研究国家,对比国内外的研究机构分布和关键词共现状况,分析关键节点文献,追溯研究源起、梳理研究现状、热点及发展趋势。研究发现,当前各国、各研究机构间的联系较为松散,缺乏多学科领域合作。就业极化和收入极化成为重要研究视角,技术进步、数字化转型、工作时长和常规性结构成为重要的研究领域,人工智能对就业影响的“情景化”、 “异质化”研究、 “重塑效应”、人机协同情景下的人机“共生关系”、基于人类价值的就业质量提升、劳动者的知识技能转化、人工智能下的就业心理、就业领域的伦理道德、基于产业与就业动态匹配的应对政策以及人工智能与就业关系的跨学科交叉研究将成为未来的十大研究趋势。     

人工智能伦理问题的现状分析与对策

人工智能（AI）是第四次产业革命的核心，但也为伦理道德规范和社会治理带来了挑战。文章在阐释当前人工智能伦理风险的基础上，分析了当前对人工智能伦理准则、治理原则和治理进路的一些共识，提出了以“共建共治共享”为指导理论，逐渐建设形成包含教育改革、伦理规范、技术支撑、法律规制、国际合作在内的多维度伦理治理体系等对策建议。     

基于文本相似度计算的我国人工智能政策比较研究

[目的/意义]随着2019年政府工作报告中首次提出“智能+”概念,我国各地区将加快推动人工智能与实体经济等产业深度融合,而政策是政府调控现代市场经济和社会管理的重要手段,因此对人工智能的政策研究受到了社会广泛的关注。[方法/过程]基于文本相似度计算对国务院发布的《新一代人工智能发展规划》和我国20个地区发布的人工智能政策文本进行比较研究。[结果/结论]数据表明:新兴产业、产业升级、人才队伍、智能服务、社会治理等内容在各地区人工智能政策制定层面整体关注最高,呈现出政策制定的相似性。安徽省、辽宁省所制定的政策比较完整和全面,甘肃省、黑龙江省在政策制定中部分内容较为突出,且逐渐形成人工智能产业发展区域特色,呈现出政策制定的差异性。最后从可操作性角度为我国各地区人工智能产业发展提出政策建议。     

Artificial intelligence for decision making in the era of Big Data – evolution,challenges and research agenda

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.     

双循环战略提升中国人工智能产业竞争力途径

摘 要：文章以中国、美国、英国、德国、印度等主要人工智能大国为研究对象，通过钻石模型构建评价指标体系，运用主成分分析法对五国人工智能产业国际竞争力进行分析。总结借鉴其他国家人工智能产业发展过程中的优劣势与形成原因，从人工智能经历的 “概念化——商业化——产业化”发展三阶段周期入手，明确不同发展阶段起着关键作用的有利要素类型，提出了三种不同实现路径下的分类政策思路。最后根据国际政治经济形势，提出在“双循环”战略下提升中国人工智能产业国际竞争力的依据与政策建议。     

“政策属性—政策结构”框架下人工智能产业政策区域比较研究

[目的/意义]由于我国不同区域产业结构不同导致产业政策存在差异。对人工智能产业政策进行比较,可以清晰识别政策的布局,为进一步优化产业政策奠定基础。[方法/过程]以京津冀、珠三角和长三角区域2015—2019年出台的人工智能产业政策为研究对象,构建"政策属性—政策结构"分析框架,运用社会网络分析、自然语言处理和主题识别等方法,对比分析《新一代人工智能发展规划》发布前后阶段各区域人工智能产业政策发展态势。[结果/结论]政策属性方面,《新一代人工智能发展规划》发布后各区域政策发文数量明显增加,并趋向强管控态势,但文种"缺位"明显。政策结构方面,珠三角和长三角区域的主体合作发文逐渐增加,京津冀区域则呈下降趋势。京津冀区域侧重基础研发和打造产业集群,长三角区域侧重智能应用和智慧城市建设,珠三角区域依托国际市场环境,侧重人工智能合作发展。     

基于人工智能专利图谱的技术热点发现以及演化分析

人工智能在近几年快速发展并成为最热门的技术之一,如能快速了解人工智能技术热点与发展态势,对抢抓人工智能发展的重大战略机遇与构筑先发优势具有重要意义。本文提出了一种基于专利可视化图谱发现技术热点的方法,即在连续时间窗口上绘制图谱,在此基础上运用密度分布变化来识别该领域技术热点。为了提升专利图谱的准确性,本文使用海量专利文本训练了基于深度学习的doc2vec模型,形成了专利文本特征抽取模型。经过实验对比发现该模型在测试数据集中表现远超经典的词袋模型与主题模型。在实例分析中使用了2012—2019年10457件三方人工智能专利进行热点发现,共发现研究热点7个,并对7个热点中关键概念词、专利申请人所属国家进行深入分析。     

利用地理信息系统与知识推论模式分析崩坍地潜势

人工智能技术适合处理复杂性较高的非线性地理问题,本研究整合地理信息系统、遗传演算、模糊逻辑与类神经网络,建立数据探索型态之知识库分析模式,同时设计新的GIS空间分析工具,让研究者得以具体应用在地理学的研究上,并适当地补强GIS在数据撷取与知识探索的不足。 研究让当前大多停留在理论探讨阶段的人工智能在地理学之应用研究,有了更具体的研究方法、可操作的研究工具、和更具说服力的研究案例,具体之成果包含：①整合人工智能中遗传演算与模糊逻辑的相关技术,建立GIS之空间分析架构;②在这个分析架构下,结合现有的商用GIS软件ArcView,发展人工智能空间信息分析师(Artificial intelligent Spatial Information Analyst; ASIA),方便领域专家的直接应用;③以实际崩坍潜势分析案例,证明前述空间分析架构之合理性与正确性。     

Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification

State-of-the-art artificial intelligence (AI) methods are progressively strengthened in Traditional Chinese Medicine (TCM) pulse palpation, aiding physicians to make comprehensive preliminary clinical decisions through non-invasive diagnostics. One of the well-known proven examinations i.e., hesitant pulse wave diagnosis, is a sign that the blood circulation of a person is sluggish. This examination provides a preliminary diagnosis for physiological problems. Modern AI methods such as artificial neural networks achieve better performance than traditional methods; however, the final decision of such examination lacks of interpretability. In clinical situations, patients need an easy-to-understand diagnosis to be provided for selecting appropriate clinical treatment. Therefore, this study presents feature extraction and clinical decision support systems based on Pulse-Line Intersection (PLI) and eXplainability AI (XAI) methods. The pulses were recorded from 46 patients in six different measurement points for six seconds. In addition, a comparison of several AI methods was provided to classify hesitant and normal pulse. The contribution of each feature in the classification process was analyzed by unboxing each predictive intelligence model. The results revealed that all models performed comparably, evaluated using performance matric on the testing data with average F1-score of Logistic Regression, Support Vector Machine, Random Forest, XGBoost, Multi-Layer Perceptron, and Long Short-Term Memory were 0.74, 0.74, 0.74, 0.78, 0.73, and 0.80, respectively. This work suggests that modern AI methods can provide more comprehensive explainability and higher accuracy than traditional method rankings.     

Artificial intelligence for sustainability: Challenges,opportunities, and a research agenda

Artificial intelligence (AI) will transform business practices and industries and has the potential to address major societal problems, including sustainability. Degradation of the natural environment and the climate crisis are exceedingly complex phenomena requiring the most advanced and innovative solutions. Aiming to spur groundbreaking research and practical solutions of AI for environmental sustainability, we argue that AI can support the derivation of culturally appropriate organizational processes and individual practices to reduce the natural resource and energy intensity of human activities. The true value of AI will not be in how it enables society to reduce its energy, water, and land use intensities, but rather, at a higher level, how it facilitates and fosters environmental governance. A comprehensive review of the literature indicates that research regarding AI for sustainability is challenged by (1) overreliance on historical data in machine learning models, (2) uncertain human behavioral responses to AI-based interventions, (3) increased cybersecurity risks, (4) adverse impacts of AI applications, and (5) difficulties in measuring effects of intervention strategies. The review indicates that future studies of AI for sustainability should incorporate (1) multilevel views, (2) systems dynamics approaches, (3) design thinking, (4) psychological and sociological considerations, and (5) economic value considerations to show how AI can deliver immediate solutions without introducing long-term threats to environmental sustainability.     

数字科学家与平行科学：AI4S和S4AI的本源与目标

围绕人工智能（AI）大模型技术的最新进展，从AI4S （人工智能驱动的科学研究）到S4AI （面向人工智能的科学研究），讨论人工与自然平行的智能科技与数字人科学家的作用及其对科研范式和社会形态变革的可能冲击；认为范式与形态的变革刻不容缓，必须积极应对。     

Understanding public opinions on social media for financial sentiment analysis using AI-based techniques

The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (～ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding.