Criticism From Artificial Agents: Prior Interaction Reduces Negative Effects

The present study explored interaction between humans and artificial agents from a relational perspective. According to theories on interpersonal communication and relationships, criticism by known agents, as compared to unknown agents, could be less negative (the buffering effect of relationship) or more negative (the expectancy violation effect) to human-agent relationships. Using online survey tools, participants who had interacted with an agent were led to one of the same or different agent conditions, in which the same or a new agent gave negative comments (criticism) to participants. Participants evaluated the agent and the interaction with it before and after receiving the criticism. Results showed that a brief acquaintanceship with agents lessened the negative effects of criticism. Participants’ evaluations decreased to a lesser degree when the negative comments were given by the acquaintance agent rather than the stranger agent. Thus, the buffering effect of relationship was supported in human-agent relationships.     

How and where is artificial intelligence in the public sector going? A literature review and research agenda

To obtain benefits in the provision of public services, managers of public organizations have considerably increased the adoption of artificial intelligence (AI) systems. However, research on AI is still scarce, and the advance of this technology in the public sector, as well as the applications and results of this strategy, need to be systematized. With this goal in mind, this paper examines research related to AI as applied to the public sector. A review of the literature covering articles available in five research databases was completed using the PRISMA protocol for literature reviews. The search process yielded 59 articles within the scope of the study out of a total of 1682 studies. Results show a growing trend of interest in AI in the public sector, with India and the US as the most active countries. General public service, economic affairs, and environmental protection are the functions of government with the most studies related to AI. The Artificial Neural Networks (ANN) technique is the most recurrent in the investigated studies and was pointed out as a technique that provides positive results in several areas of its application. A research framework for AI solutions for the public sector is presented, where it is demonstrated that policies and ethical implications of the use of AI permeate all layers of application of this technology and the solutions can generate value for functions of government. However, for this, a prior debate with society about the use of AI in the public sector is recommended.     

国际科技情报竞争的反应模式:对两个案例的比较分析

本文通过对日本建立“中国情报中心”和美国制订“日本技术文献法”期间中国、日本两国不同反应模式的具体分析,探讨了目前我国国家技术竞争情报体系的薄弱环节,并分析了其原因。     

变中求存,存中思变-图书馆学、情报学与信息管理学等关系再思考

评述了国内现有的关于图书馆学、情报学、信息科学、信息管理学等学科关系的几种代表性观点,结合图书馆学、情报学、信息管理学等学科发展的实践,分析了各学科之间的关系,并对本科、研究生专业设置和课程体系提出了建议。     

企业竞争情报系统研究综述

文章对近年国内外企业竞争情报系统的发展现状、竞争情报系统的结构、系统建设模式、如何进行系统设置以及企业在竞争情报系统建设中所面临的问题和解决对策等的研究作了较全面的综述。     

美国国家情报体系人工智能技术发展现状分析

[目的/意义]分析美国国家情报体系人工智能技术的实施背景及意义,并根据美国中央情报局及美国国防情报局等国家情报机构的人工智能项目实施内容、方向以及模式,归纳美国国家情报体系人工智能技术的发展趋势,从而提出对我国国家情报体系AI技术应用的启示。[方法/过程]利用文献调研法和比较分析法进行研究,分析CIA以及DIA等机构的人工智能项目实施内容,以及美国国家情报体系AI技术发展的优势。[结果/结论]从项目数量、支持资金以及发展规划看,美国国家情报体系正以难以想象的速度发展AI技术,特别是In-Q-Tel公司的推动成为关键因素。我国国家情报体系应该借鉴美国的先进经验,加快AI技术的部署与应用,特别是借助我国互联网企业的力量。     

基于CNKI的我国竞争情报研究现状分析

论文以中国期刊全文数据库中收录的竞争情报文献为基础,从文献计量学的角度对20世纪90年代以来我国竞争情报发文进行统计。较全面地反映了我国竞争情报研究队伍、研究热点等状况,分析了我国不同阶段的竞争情报研究,并对比国外研究状况提出一些优化意见。     

组织:情报学研究中一个长期被忽视的维度——来自谢尔曼·肯特的启示

谢尔曼·肯特认为情报包含知识、行动、组织三个要素。本文分别从知识、行动、组织三个维度分析了情报学的研究领域、内容和相关范畴,最后指出组织维度是被长期忽视的一个维度,加强组织维度的研究,可成为情报学理论创新的一个突破口。     

面向对象的竞争情报分析方法体系建构研究

基于对现有竞争情报分析方法体系的分析,结合动态竞争发展对竞争情报分析提出的新要求,以竞争情报对象为着眼点,构建由逻辑思维分析方法和专业技术分析方法二个层次所构成的面向对象的竞争情报分析方法体系,并阐释此方法体系的结构特点,剖析其内在机理。     

情报生成理论

基于对生成性思维、生物化学变构酶调节机理和变构学习模型的认识,创建情报生成理论：提出并诠释情报生成概念;探讨情报生成的解读系统结构及其转化的变构调节机理、协同模型和动力学曲线;制定情报生成的策略;论述情报生成理论的意义。