Cognitive semantics and artificial intelligence

This paper discusses the basic concepts of cognitive semantics that were described in [14]. These use the thesis that human cognitive structures and mechanisms substantially depend on sensory mechanisms, as well as physical and social experience. The possibilities of the application of these concepts for solving the problems of artificial intelligence are considered.     

Orchestrating artificial intelligence for urban sustainability

Artificial intelligence (AI) is regarded as the next digital frontier in government, with many potential applications for economic development as well as sustainable urbanization. Governments have started experimenting with AI, but empirical research on how to leverage and implement AI remains limited. This study analyzed two cases of AI implementation in a large city and identified various AI capabilities useful for government. More importantly, purposeful orchestration of AI-related resources such as data, knowledge, algorithms, and information systems is necessary for developing strong AI capabilities. The findings indicate two different types of orchestration: policy-driven orchestration focuses on the integration of resources, while innovation-driven orchestration focuses on triangulation. This study contributes to the growing body of knowledge on AI in government by revealing and conceptualizing different paths and approaches to AI implementation. They also serve to inform practitioners' planning of AI implementation.     

再论“人工智能新闻生产体”的主体性

"智能新闻"是相对"人工新闻"而言的概念。主体性是属人的,不是属物的。人工新闻的生产主体是人主体,主体性自然是指人的主体性;智能新闻的直接生产体是人工智能体(人工智能网络),现象上表现出"拟主体性"。拟人主体性不等于完全的人主体性。人类智能创造了人工智能,人主体创造了人工智能体,这是不可颠倒的客观逻辑。我们可以把"人主体"与"人工技术体"之间的基本演进关系大致描述为:手工技术、机械技术,它们仅仅是人作为人主体的工具;弱人工智能技术,开始具有类人的一些功能属性,形成一些初步的拟人主体性的特征(拟主体);强人工智能技术,可能形成与人主体相平行的新主体类型(平行主体),生成强人工智能主体的主体性;超强人工智能技术,可能形成超越人主体的全新主体(超主体),但这是人主体无法理解的超人主体性。就当前情况看,新闻生产传播处于人机交互、人机协同的开启阶段,是人们所说的人类智能与弱人工智能共同协作的阶段。这是一个人主体依然占据绝对主导支配地位的时代,是人主体依然必须在新闻生产传播中充当各种责任终极主体的时代。     

新世纪国际人工智能研究领域可视化分析

目的：全面了解新世纪国际人工智能领域的研究现状与研究热点。方法：运用TDA软件,利用文献统计分析、关键词共现分析的方法揭示研究热点。结果：国际人工智能领域文献量呈上升趋势,其中美国发文量排名第一,中国位居第六。人工智能的主研究领域包括计算机科学、工程学、自动化与控制系统3个学科。研究热点为遗传算法、神经网络和机器学习。结论：新世纪国际人工智能研究涉及多个学科,研究热点集中在知识获取、知识表示和问题求解3个宏观层面,神经网络、遗传算法和机器学习是研究者们关注的核心。     

Using artificial intelligence to identify administrative errors in unemployment insurance

Administrative errors in unemployment insurance (UI) decisions give rise to a public values conflict between efficiency and efficacy. We analyze whether artificial intelligence (AI) – in particular, methods in machine learning (ML) – can be used to detect administrative errors in UI claims decisions, both in terms of accuracy and normative tradeoffs. We use 16 years of US Department of Labor audit and policy data on UI claims to analyze the accuracy of 7 different random forest and deep learning models. We further test weighting schemas and synthetic data approaches to correcting imbalances in the training data. A random forest model using gradient descent boosting is more accurate, along several measures, and preferable in terms of public values, than every deep learning model tested. Adjusting model weights produces significant recall improvements for low-n outcomes, at the expense of precision. Synthetic data produces attenuated improvements and drawbacks relative to weights.     

人工智能在档案资源开发利用中的应用分析

人工智能的应用为档案资源开发利用带来机遇,但目前在档案领域的应用尚处于探索时期。一方面,智能语音技术、光学字符识别技术、人脸识别技术等能够促进档案资源的文本化、数字化与检索智能化;另一方面,档案资源类型复杂、指导理念与技术脱节、用户信息泄露、算法伦理等问题制约着其发挥作用。本文通过分析人工智能在档案资源开发利用中的现状,探讨其在资源层面、理念层面与技术层面遇到的困境,提出通过技术聚焦、理念更新、用户信息加密等策略全面优化人工智能在档案资源开发利用中的应用,为充分挖掘与实现档案价值赋能。     

Mining patterns of author orders in scientific publications

The author order of multi-authored papers can reveal subtle patterns of scientific collaboration and provide insights on the nature of credit assignment among coauthors. This article proposes a sequence-based perspective on scientific collaboration. Using frequently occurring sequences as the unit of analysis, this study explores (1) what types of sequence patterns are most common in the scientific collaboration at the level of authors, institutions, U.S. states, and nations in Library and Information Science (LIS); and (2) the productivity (measured by number of papers) and influence (measured by citation counts) of different types of sequence patterns. Results show that (1) the productivity and influence approximately follow the power law for frequent sequences in the four levels of analysis; (2) the productivity and influence present a significant positive correlation among frequent sequences, and the strength of the correlation increases with the level of integration; (3) for author-level, institution-level, and state-level frequent sequences, short geographical distances between the authors usually co-present with high productivities, while long distances tend to co-occur with large citation counts; (4) for author-level frequent sequences, the pattern of “the more productive and prestigious authors ranking ahead” is the one with the highest productivity and the highest influence; however, in the rest of the levels of analysis, the pattern with the highest productivity and the highest influence is the one with “the less productive and prestigious institutions/states/nations ranking ahead.”     

Regulating artificial intelligence: Proposal for a global solution

With increasing ubiquity of artificial intelligence (AI) in modern societies, individual countries and the international community are working hard to create an innovation-friendly, yet safe, regulatory environment. Adequate regulation is key to maximize the benefits and minimize the risks stemming from AI technologies. Developing regulatory frameworks is, however, challenging due to AI's global reach, agency problems present in regulation, and the existence of widespread misconceptions about the very notion of regulation. This paper makes three claims: (1) Based on interdisciplinary insights, we show that AI-related challenges cannot be tackled effectively without sincere international coordination supported by robust, consistent domestic, regional, and international governance arrangements. (2) Against this backdrop, we propose the establishment of an international AI governance framework to spearhead initiatives to create a consistent, global enabling regulatory environment, which is necessary for the successful and responsible adoption of AI technologies. To facilitate the practical implementation of our recommendation, we provide a simplified impact assessment on regulatory architecture and governance design options, appropriate to the scope of the paper. (3) We draw attention to communication challenges, which we believe are underestimated barriers hindering contemporary efforts to develop AI regulatory regimes. We argue that a fundamental change of mindset regarding the nature of regulation is necessary to remove these, and put forward some recommendations on how to achieve this.     

Assessing behavioral data science privacy issues in government artificial intelligence deployment

In today's global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens' behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens' privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens' concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens' privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens' privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.     

人工智能背景下科技论文摘要的机器翻译与译后编辑

神经网络机器翻译的发展为作者英译科技论文摘要提供了便利,也丰富了科技期刊英文编辑的工作内涵。通过借鉴(GB/T 40036—2021)《翻译服务 机器翻译结果的译后编辑要求》中对机器翻译译后编辑类型、任务及目标等相关问题的界定来探讨机译英文摘要的编辑加工方法不失为一次全新而有益的尝试。本文辨析了作者、编辑进行机译英文摘要译后编辑的不同类型,阐述了编辑层面的译后编辑任务分解流程,归纳了机译英文摘要的常见错误类型,探讨了与摘要内容相适应的译后编辑策略,并认为编辑应坚守人机互动翻译模式中的主导地位。     

The moon,the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms

Computational algorithms and automated decision making systems that include them offer potential to improve public policy and organizations. But computational algorithms based on biased data encode those biases into algorithms, models and their outputs. Systemic racism is institutionalized bias with respect to race, ethnicity and related attributes. Such bias is located in data that encode the results and outputs of decisions that have been discriminatory, in procedures and processes that may intentionally or unintentionally disadvantage people based on race, and in policies that may discriminate by race. Computational algorithms may exacerbate systemic racism if they are not designed, developed, and used–that is, enacted–with attention to identifying and remedying bias specific to race. Advancing social equity in digital governance requires systematic, ongoing efforts to assure that automated decision making systems, and their enactment in complex public organizational arrangements, are free from bias.     

科技期刊读者阅读过程的心理定势

作者结合实例分析了科技期刊读者阅读时的四种心理定势:①读者与作者同步,保持一致,读后有充实感;②读者与作者倾向同步,基本一致,但又互相补充;③读者与作者互逆,读者虽然不同意文章蕴含的思想方法和客观发展规律,但能接受;④读者与作者完全相悖,持相反意见。     

论人工智能应用给学术期刊编辑带来的机遇与挑战

从编辑实践角度出发,探讨人工智能应用对学术期刊编辑工作的影响并分析了学术期刊编辑的转型路径。人工智能带来的机遇有：优化学术出版工作流程并提高加工质量、强化编辑把关能力和效果、AI生成内容给编辑从事全媒体传播活动提供了机遇、提升学术出版资源配置和编辑价值创造力。但人工智能也带来如何与人工智能技术协调、面对人工智能生产内容、人工智能生成的新媒体形态等挑战。期刊编辑需要锻造数据分析和价值发现能力、提高智能环境下的把关能力、形成与技术协同的全媒体编辑能力和以人文价值驾驭新技术的新能力应对上述挑战。     

Signatures of capacity development through research collaborations in artificial intelligence and machine learning

Extant studies suggest that the proximity between the researchers and their structural positioning in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observations ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distribution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with domestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research.     

Governance of artificial intelligence: A risk and guideline-based integrative framework

This study addresses the growing challenge of governing artificial intelligence (AI) arising from the risks that it increasingly poses to the public sector and society. Based on an in-depth literature analysis, we first identify AI risks and guidelines and classify them into six categories, including technological, data, and analytical risks and guidelines, informational and communicational risks and guidelines, economic risks and guidelines, social risks and guidelines, ethical risks and guidelines, as well as legal and regulatory risks and guidelines. These risks and guidelines are then elaborated and transferred into a four-layered conceptual framework for AI governance. The framework interrelates AI risks and AI guidelines by means of a risk management and guidance process, resulting in an AI governance layer depicting the process for implementation of customised risk mitigation guidelines. The framework constitutes a comprehensive reference point for developing and implementing AI governance strategies and measures in the public sector.     

Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States

This study aims to evaluate the perceptions of librarians with regard to artificial intelligence in academic libraries. An online survey of 24 questions was distributed through library distribution lists in Canada and the United States at the end of the summer in 2019. Findings suggest that librarians do not agree on a definition of artificial intelligence which is in keeping with this emerging field. The survey responses highlight the fact that academic librarians require more training with regard to artificial intelligence and its potential applications in libraries. Other important implications include a recognition that library patrons are interested in AI and that little to no programming about it has been offered in academic libraries. Very few studies have focused on academic librarians' perceptions of AI. This article highlights some useful practical implications for AI technologies in libraries and how AI could help improve library services and workflows.     

关于人工智能背景下医学期刊应对医学伦理问题的思考

随着人工智能在医学领域的应用,越来越多的患者受益。但同时,人工智能也带来了一系列医学伦理问题,如机器训练伦理、机器准确性伦理、患者相关伦理、医师相关伦理、责任和监管。本文简要分析了上述伦理问题,阐述了医学期刊在促进人工智能医学伦理发展中的作用和责任,并提出了医学期刊应对人工智能医学伦理问题的建议,包括编辑培训、制定相关伦理声明、建立医学期刊行业规范、构建审查团队等。期望医学期刊可以为解决人工智能在医学领域中应用的伦理问题做出贡献。     

Eleventh national conference on artificial intelligence with international participation (CAI-2008)

Program and General Chair of CAI-2008 provides a brief survey of each of a hundred papers included into the program of the conference. Being one of the series of biannual conferences, the conference CAI-2008, was organized and held by the Russian Association for Artificial Intelligence (RAAI) in the city Dubna (Moscow region) from September 28th to October 3d, 2008. The purpose of the present survey is to give a general idea of each paper by a brief exposition of its contents. The full texts of the original papers may be found in the first two volumes of the Proceedings of this conference (Moscow: URSS, 2008).