Inspired,but not mimicking: a conversation between artificial intelligence and human intelligenceOA

<正>How intelligent is artificial intelligence(AI)? How intelligent will it become in the future? What is the relationship between AI and human intelligence(HI)? These questions have been a hot topic of discussion in recent years, but no consensus has yet been reached. To discuss these issues, we should first understand the concept of intelligence as well as the underlying mechanisms for both HI and AI. In this NSR Forum, experts from both disciplines gathered to discuss these issues; in pa...     

人工情感与人工智能应用研究

生活世界中人工情感研发得到快速发展,但还不能比及人类情感。语境残破和缺身涉入是人工情感不及人类情感真挚、真切的两个重要原因。建构语境与虚拟身体的协同创新有助于增强人工情感的逼真度。人工情感在教育、客服、营销、制造、安防等领域的应用,将有助于提高人机交互的有效性、体验性和真实性。随着人工情感的增强,我们既要积极支持,同时也要做好应对因人工情感所带来各种风险的防控。     

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.     

Fashion analysis and understanding with artificial intelligence

As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges for computer scientists, fashion studies have received increasing attention in computer vision, machine learning and multimedia communities in the past few years. In this paper, introduce the progress in fashion research and provide a taxonomy of these fashion studies that include low-level fashion recognition, middle-level fashion understanding and high-level fashion applications. Finally, we discuss the challenges that when the fashion industry faces AI technologies.     

Human-aligned artificial intelligence is a multiobjective problem

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents.     

Consumer engagement via interactive artificial intelligence and mixed reality

The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.     

Business Innovation based on artificial intelligence and Blockchain technology

The growing business evolution and the latest Artificial Intelligence (AI) make the different business practices to be enhanced by the ability to create new means of collaboration. Such growing technology helps to deliver brand services and even some new kinds of corporate interactions with customers and staff. AI digitization simultaneously emphasized businesses to focus on the existing strategies and regularly and early pursue new market opportunities. While digital technology research in the framework of business innovation is gaining greater interest and the privacy of data can be maintained by Blockchain technology. Therefore in this paper, Business Innovation based on artificial intelligence and Blockchain technology (BI-AIBT) has been proposed to enhance the business practices and maintain the secured interaction among the various clients. The collection of qualitative empirical data is made up of few primary respondents from two distinct business sectors. BI-AIBT has been evaluated by undertaking and exploring the difference and similarities between digitalization's impact on value development, proposal, and business capture. Besides, organizational capacities and staff skills interaction issues can be improved by BT. The experimental result suggests that digital transformation is usually regarded as essential and improves business innovation strategies. The numerical result proposed BI-AIBT improves the demand prediction ratio (97.1%), product quality ratio (98.3%), Business development ratio (98.9%), customer behavior analysis ratio (96.3%), and customer satisfaction ratio (97.2%).     

人工智能提升企业劳动收入份额了吗

随着人工智能时代的到来，产业体系与生产方式发生着转变，对劳动力市场和国民收入分配格局产生冲击。在此背景下，基于微观企业面板数据，分析人工智能应用对企业劳动收入份额的影响。利用双重差分倾向匹配得分法（PSM-DID）的研究结论表明，人工智能应用显著提升企业劳动收入份额，平均而言可以提升1.4-1.7个百分点，且这一效果在不同类型企业之间存在异质性，出口参与行为抑制了人工智能对劳动收入份额的促进作用；私营企业强于非私营企业；对劳动密集型企业的积极影响最为突出。利用因果中介分析（CMA）模型的机制验证表明，人工智能应用提升企业劳动收入份额的作用机制以劳动增进效应为主，企业全要素生产率起到部分中介效应。研究结论对我国在“人工智能革命”中的收入分配改革具有启示意义。     

以色列人工智能发展现状分析及政策举措

目前,世界上许多国家都在竞相发展人工智能,以创新创业见长的以色列在人工智能方面也作出了重要部署,并有突出的表现.本文基于以色列创新署发布的《2019年创新报告》等报告及数据,着重分析了以色列近年来在人工智能方面取得的成就和当前发展所面临的障碍,以及以色列为保持现有优势并进一步推动人工智能发展的政策举措,旨在为我国人工智能的发展提供参考借鉴.     

Market power and artificial intelligence work on online labour markets

We investigate three alternative but complementary indicators of market power on one of the largest online labour markets (OLMs) in Europe: (1) the elasticity of labour demand, (2) the elasticity of labour supply, and (3) the concentration of market shares. We explore how these indicators relate to an exogenous change in platform policy. In the middle of the observation period, the platform made it mandatory for employers to signal the rates they were willing to pay as given by the level of experience required to perform a project, i.e., entry, intermediate or expert level. We find a positive labour supply elasticity ranging between 0.06 and 0.15, which is higher for expert-level projects. We also find that the labour demand elasticity increased while the labour supply elasticity decreased after the policy change. Based on this, we argue that market-designing platform providers can influence the labour demand and supply elasticities on OLMs with the terms and conditions they set for the platform. We also explore the demand for and supply of AI-related labour on the OLM under study. We provide evidence for a significantly higher demand for AI-related labour (ranging from +1.4% to +4.1%) and a significantly lower supply of AI-related labour (ranging from -6.8% to -1.6%) than for other types of labour. We also find that workers on AI projects receive 3.0%-3.2% higher wages than workers on non-AI projects.     

人工智能伦理的国际软法之治：现状、挑战与对策

人工智能技术不仅能快速赋能经济社会发展,也可能引发诸多与人工智能技术本身特征和发展高度相关的伦理问题。国际软法因具有灵活高效、适用成本低,能填补硬法空白,以及方便区分治理、分层应对伦理问题的优势,其在人工智能伦理治理领域的勃兴几乎是必然的。在该领域国际软法发达、硬法落后的现状下,面对国际软法主体间合作不稳定、有时得不到有效实施的治理挑战,治理模式逐渐向软硬兼备、软法“硬化”转变,以提高软法约束力与执行可能性。建议构造国际软硬法混合治理的“中心—外围”模式、构建间接执行机制,以完善人工智能伦理的国际软法治理对策。     

Applying artificial intelligence technique to predict knowledge hiding behavior

Drawing on psychological ownership and social exchange theories, this study suggests theoretical arguments and empirical evidence for understanding employee reactions to distributive, procedural, and interactional (in)justice — three crucial bases of employees’ feelings of social self-worth. Utilizing field data and artificial intelligence technique, this paper reveals that distributive, procedural, and interactional (in)justice contribute to higher levels of knowledge hiding behavior among employees and that this impact is non-linear (asymmetric). By reuniting the discourses of organizational justice and knowledge management, this study indicates that feelings of psychological ownership of knowledge and the degree of social interaction are mechanisms that work with organizational (in)justice to influence knowledge hiding behavior. The current research may inform contemporary theories of business research and provide normative guidance for managers.     

An explainable artificial intelligence approach for financial distress prediction

External stakeholders require accurate and explainable financial distress prediction (FDP) models. Complex machine learning algorithms offer high accuracy, but most of them lack explanatory power, resulting in external stakeholders being cautious in adopting them. Therefore, an explainable artificial intelligence approach including a whole process ensemble method and an explainable frame for FDP is here proposed. The ensemble algorithm from feature selection to predictor construction can achieve high accuracy according to the actual case, and the interpretation framework can meet the needs of external users by generating local explanations and global explanations. First, a two-stage scheme integrated with a filter and wrapper technique is designed for feature selection. Second, multiple ensemble models are explored and they are evaluated according to the actual case. Finally, Shapley additive explanations, counterfactual explanations and partial dependence plots are employed to enhance model interpretability. Taking financial data of Chinese listed companies from 2007 to 2020 as a dataset, the highest AUC is ensured by LightGBM with a value of 0.92. Local explanations help individual enterprises identify the key features which lead to their financial distress, and counterfactual explanations are produced to provide improvement strategies. By analyzing the features importance and the impact of feature interaction on the results, global explanations can improve the transparency and credibility of ‘black box’ models.     

PTZ-Surveillance coverage based on artificial intelligence for smart cities

Surveillance cameras have a plethora of usages in newly born cities including smart traffic, healthcare, monitoring, and meeting security needs. One of the most famous new cites is the Egypt's new administration capital “New Cairo”. The new administration capital of Egypt mainly characterizes with the green life style via the "Green River ". In this paper, a new enhanced Artificial Intelligence (AI) algorithm is introduced for adjusting the orientation of Pan–Tilt–Zoom (PTZ) surveillance cameras in new Cairo. In other words, the new proposed algorithm is used for improving the field of view (FOV) coverage of PTZ cameras network. For validating the proposed algorithm, it is tested on many scenarios with different criterions. After that, the proposed algorithm is applied to adjust the PTZ monitoring cameras in the green river which locates on new administrative capital as an equivalent to the river Nile. In addition, it compared with several other AI algorithms through the appropriate statistical analysis. The overall experimental results indicate the prosperity of the proposed algorithm for increasing the coverage of the PTZ surveillance system.     

人工智能计算机的发展困境

上个世纪八十年代,第五代计算机,也就是人工智能计算机的研究曾热极一时,然而时至今日,已经鲜有人提起第五代计算机了。本文对第五代计算机的主要特征进行了描述,从第五代计算机的核心技术…人工智能入手,对第五代计算机研究陷于困境的原因进行了探讨。在此基础上,对于人工智能的发展前景做出了自己的判断。     

医学人工智能发展态势分析及问题浅析

为探究医学人工智能发展态势以及我国医学人工智能存在的问题，从医学人工智能领域论文、临床试验、医疗器械产品3个维度进行态势分析，量化分析结果显示我国和全球医学人工智能发表论文数量均呈迅速增长趋势，发文数量及高被引论文方面我国处于国际前列；人工智能临床试验开展数量和面向疾病种类较多，但临床试验注册质量有待提高；我国人工智能医疗器械获批数量逐年增加，医疗人工智能产品认证稳步推进。医学人工智能发展的同时在理论层面、技术层面、伦理法律层面以及应用层面依旧面临着诸多问题和挑战。     

基于文献计量学的人工智能领域研究现状及热点分析

基于Scopus数据库和SciVal科研评价工具,从学术产出、被引情况、学术影响力等计量指标分析2014至2019年人工智能领域发展状况和研究热点.研究发现:全球人工智能领域关注度和学术产出持续增加,相关研究进入高速发展阶段,其中美国高校和企业均处于领先地位,德国在研究机构方面最具影响力,而中国高校和研究所虽然学术产出全球第一但成果质量和影响力还有待提高,不过中国企业表现突出,已进入世界第一梯队;研究热点主要集中在基础研究和共性关键技术这两个方面,尤其是模型、算法等基础理论的研究在人工智能领域得到持续关注和重视.最后依据研究结果以及中国人工智能发展战略举措,为促进中国人工智能发展提出建议:引导并激励研究人员发表"三高"论文、开展合作共研,以及聚焦基础理论和关键共性技术研究,促进产学研合作及成果转化.     

国内人工智能研究的知识图谱分析

为全面分析国内人工智能的研究现状和研究热点,运用Vosviewer与Citespace软件对CNKI数据库中1992-2020年国内人工智能研究的相关文献进行可视化分析,绘制作者共现图谱、关键词共现标签视图、关键词时区图谱.在此基础,对人工智能在过去二十多年间的研究主题演化、内容分支、跨学科融合等问题进行介绍;换言之,...