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.     

The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users

There is an exponential growth of the use of AI applications in organisations. Due to the machine learning capability of artificial intelligence (AI) applications, it is critical that such systems are used continuously in order to generate rich use data that allow them to learn, evolve and mature into a better fit for their user and organisational context. This research focuses on the actual use of conversational AI, in particular AI chatbot, as one type of workplace AI application to answer the research question: how do employees experience the use of an AI chatbot in their day-to-day work? Through a qualitative case study of a large international organisation and by performing an inductive analysis, the research uncovers the different ways in which users appropriate the AI chatbot and identifies two key dimensions that determine their type of use: the dominant mode of interaction and the understanding of the AI chatbot technology. Based on these dimensions, a taxonomy of users is presented, which classifies users of AI chatbots into four types: early quitters, pragmatics, progressives, and persistents. The findings contribute to the understanding of how conversational AI, particularly AI chatbots, is used in organisations and pave the way for further research in this regard. The implications for practice are also discussed.     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture

This study addresses two critical research gaps in human-robot interaction (HRI): the limited systematic research on the role of trust in customers’ acceptance of artificially intelligent (AI) robots; and the lack of understanding of robot acceptance under different cultural backgrounds. Drawing on the AIDUA framework, this study examines the impacts of trust and moderating effects of both national (the U.S. and China) and individual culture on customers’ intentions to use AI robots in hospitality services by developing a theoretical model. The model is tested on data collected using online data collection platforms from 491 U.S. and 495 Chinese respondents. PLS-SEM and the bootstrapping method were used to test the hypothesized relationships and analyze the moderating effects of culture, respectively. The findings suggest that trust in interaction with AI robots is a significant higher-order construct that influences the intention of use. Furthermore, uncertainty avoidance, long-term orientation, and power distance have been found to exhibit significant moderation effects. The results of this study extend the theoretical frameworks in HRI and provide detailed guidance to promote AI robot applications across different cultures.     

替代或互补：人工智能应用管理对创新的影响

数字经济背景下,人工智能（AI）技术的应用正在深入地影响着企业管理变革、业务边界的扩展和管理模式的改变。结合互补资产的观点和组织学习理论,本文提出了一个基于AI应用能力和AI管理能力的分析框架,强调人工智能与人类智慧结合的必要性,阐述了两种能力的功能和作用及其协同对企业效率和创新成本的影响。本文提出,企业必须具备管理AI的能力才能有效应对大数据、数字技术、AI的不断革新及技术带来的组织内部结构和外部环境变化以及风险;企业AI应用与管理能力的有效结合,有利于控制AI应用带来的成本和风险,增强企业在人工人力、协调沟通、和数据搜寻方面的效率,同时降低AI应用带来的数字基建、道德情感、数据安全、组织结构变革方面的成本,进而促进企业的组织学习、对内外部数字技术使能资源的获取和管理以及互补资产的形成,对企业创新绩效发挥正向作用。最后,本文为企业的数字化创新战略提供了新的发展思路。     

The determinants of AI innovation across European firms

Using patent data for a panel sample of European companies between 1995 and 2016 we explore whether the inventive success in Artificial Intelligence (AI) is related to earlier firms’ innovation in the area of Information and Communication Technology (ICT), and identify which company characteristics and external factors shape this performance. We show that AI innovation presents strong dynamic returns (learning effects) and benefits from complementaries with knowledge earlier developed in the area of network and communication technologies, high-speed computing and data analysis, and more recently cognition and imaging. AI patent productivity increases with the scale of firm innovation, and is lower for companies with narrow technological competences. There is evidence of knowledge spillovers from ICT innovators to AI innovators, but this effect is confined to the frontier firms of the new technological field. Our findings suggest that, with the take-off of the new technology, the technological lead of top AI innovators has increased due to the accumulation of internal competences and the expanding knowledge base. These trends help explain the concentration process of the world’s data market.     

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges,opportunities, and agenda for research,practice and policy

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.     

Transformative computing approaches for advanced management solutions and cognitive processing

This paper will describe Transformative Computing technologies as a new area of modern information sciences, which plays crucial role in development future IT technologies. Transformative computing allow to join communication technologies, and data processing techniques with advanced AI solutions, which allow to analytically process and manage acquired data. It enhances possibilities of efficient data analysis, and thanks to the application of AI, opens a new areas of data exploration towards planning, decision supporting, and advanced secure information management in distributed systems. In this paper we'll focus on new possible areas of transformative computing applications, especially for semantic information processing, as well as cognitive data reasoning.     

基于文献计量的中外人工智能政策研究现状及启示

[目的/意义]人工智能已成为推动新一轮科技革命和产业变革的重要技术力量,世界各国加紧出台了相关政策。通过对当前研究进行及时梳理,可为今后国内人工智能政策的理论推进及政策出台和完善等提供指导。[方法/过程]以国外SSCI和国内CSSCI期刊数据库收录的395篇研究论文为样本,采用文献计量分析和比较研究法对中外人工智能政策研究的高共被引文献、热点主题及演进趋势等进行深入探索。[结果/结论]与国外相比,国内研究起步较晚但势头迅猛;高共被引文献反映了人工智能领域存在的问题和风险、应用前景、技术革新及对社会的影响;国外研究热点涵盖了知识管理等十二类主题,而国内研究热点则包括国家治理背景下人工智能政策发展路径等三类主题;国外研究的演进特征体现在三个方面,而国内研究则体现在两个方面。最后,从加快形成和构建人工智能政策研究的理论框架体系等三个方面提出对国内研究的启示。     

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。     

人工智能在21世纪水与环境领域应用的问题及对策

水,是维系人类经济社会与自然生态系统可持续发展的重要资源。近半个世纪以来,因人口增长、人类活动加剧与气候变化等系列因素驱动,水安全已成为全球重要议题。其中,为所有人提供水和环境卫生并对其进行可持续管理,已被列为联合国面向2030年的全球可持续发展目标之一;但是,如何构建行之有效的实施路径和解决方案依旧面临诸多挑战。人工智能技术的飞速发展,为实现这一宏伟目标提供了新的思路与方法。文章结合联合国全球可持续发展总体目标6"清洁饮水和卫生设施"的核心内涵与进程难点,分析和总结人工智能在水与环境领域的应用现状及效应,探讨利用人工智能落实水与环境可持续发展过程中待解决的核心关键问题,并对水与环境领域和人工智能领域的融合创新和协同发展方向进行展望。     

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.     

AI for Science：智能化科学设施变革基础研究

近年来，人工智能（AI）在前沿科技领域取得了诸如AlphaFold2、核聚变智能控制、新冠药物设计等诸多令人瞩目成果，表明AI for Science正在成为一种新的研究范式。实现智能时代的基础科学源头创新及其下游重大技术创新，需破解2个方面的核心问题：（1）如何利用新一代AI（特别是生成式AI及大模型）的通用性和创造性推动新范式的形成；（2）如何利用AI实现对传统科学设施的赋能与改造。文章提出一种智能化科学设施的建设构想，兼顾“高度智能化的科学新设施”和“AI赋能已有科学大设施”2个层面的需求，构筑AI for Science的科学设施体系，形成科学领域大模型、生成式模拟与反演、自主智能无人实验及大规模可信科研协作等创新功能，加速重大科学发现、变革性物质合成，以及重大工程技术应用。     

Artificial intelligence for digital sustainability: An insight into domain-specific research and future directions

Recent research call for action on digital sustainability research could potentially contribute to achieving United Nations (UN) Sustainable Development Goals (SDG). In this opinion piece, we specifically focus on artificial intelligence (AI) as a technology that could help achieve digital sustainability. We identify six dimensions related to AI grounded in past literature: sensemaking, relationships among actors in the supply chain, green creativity skills, metrics, strategies, and AI tool improvement. We conceptualize several propositions for these six dimensions, highlighting the nuances associated with AI for digital sustainability to provide clear directions for future research.     

AI for Technology：技术智能在高技术领域的应用实践与未来展望

以智能化科研（AI for Science）为核心的第五科研范式已经在多个自然科学和高技术领域得到了广泛应用。与人工智能（AI）在自然科学领域的应用强调发现新原理、新机理和新规律不同,高技术领域更强调用AI技术来发明创造新方案、新工具和新产品,以解决特定的领域问题。文章总结了AI在高技术领域的应用——“技术智能”（AI for Technology）的典型特征和科学问题,并以CPU芯片全自动设计为例介绍过往的成功案例。最后,文章指出技术智能的目标不仅是加速创新流程并减少人工投入,同时也希望其具备更强的创造能力,最终超过人类的水平。     

反网络洗钱技术研究

针对当前洗钱犯罪活动迅速发展并呈现网络化的问题,从洗钱与反洗钱行为的概念出发,概述了网络洗钱的特点、形式、发展以及与传统洗钱行为的差异;在对国内外反网络洗钱中采用的数据挖掘、人工智能等技术以及国际先进的反洗钱应用系统进行归纳与分析后,展望了反网络洗钱技术的发展趋势。     

强人工智能和超级智能:技术合理性及其批判

强人工智能(以下简称强AI:Strong Artificial Intelligence)由美国哲学家约翰·塞尔上世纪70年代在其论文《心灵、大脑与程序》中提出,主要是指对人工智能(以下简称AI)持有的这样一种哲学立场:基于心智的计算模型,以通用数字计算机为载体的AI程序可以象人类一样认知和思考,达到或者超过人类智能水平。这种立场与弱人工智能(以下简称弱AI:Weak AI)或应用人工智能相对立,后者认为AI只是帮助人类完成某些任务的工具或助理。随着最近20年来互联网、神经科学、基因工程等技术的飞速发展,强AI从塞尔时代的一种哲学立场逐步向工程实践转变和演进,未来学家甚至设想和描述了强AI的更极端版本:超级智能,这些在IBM、谷歌、Facebook、微软等产业巨头和库兹韦尔、马克拉姆等乐观的技术实践者的双重推动下,藉由大众科学传播的放大作用,渗透到人们的日常生活中构成了对其技术合理性的辩护,但AI本身对人类主体和社会的影响不是价值中立的,它一方面难以吸收和提升人类的创新本质,另一方面其技术合理性带来的后果与其初衷有时相互背离,并在商业行为的推动下,构成对作为文化产物和自我解释的理性人类的单向压制和挑战。     

Exploring developments of the AI field from the perspective of methods,datasets, and metrics

The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets, and metrics, are extracted and used as artificial intelligence (AI) markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the named entity recognition model is used to extract AI markers from large-scale AI literature. A multi-stage self-paced learning strategy (MSPL) is proposed to address the negative influence of hard and noisy samples on the model training. Secondly, original papers are traced for AI markers. Statistical and propagation analyses are performed based on the tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters is explored. The above-mentioned mining based on AI markers yields many significant findings. For example, the propagation rate of the datasets gradually increases. The methods proposed by China in recent years have an increasing influence on other countries.     

Artificial intelligence and industrial innovation: Evidence from German firm-level data

This paper analyses the link between the use of Artificial Intelligence (AI) and innovation performance in firms. Based on firm-level data from the German part of the Community Innovation Survey (CIS) 2018, we examine the role of different AI methods and application areas in innovation. The results show that 5.8% of firms in Germany were actively using AI in their business operations or products and services in 2019. We find that the use of AI is associated with annual sales with world-first product innovations in these firms of about €16 billion (i.e. 18% of total annual sales of world-first innovations). In addition, AI technologies have been used in process innovation that contributed to about 6% of total annual cost savings of the German business sector. Firms that apply AI broadly (using different methods for different applications areas) and that have already several years of experience in using AI obtain significantly higher innovation results. These positive findings on the role of AI for innovation have to be interpreted with caution as they refer to a specific country (Germany) in a situation where AI started to diffuse rapidly.     

智能科学家——科技信息创新引领的下一代科研范式

科技创新是国家发展与民族复兴的强大引擎。提高科技创新能力必须透彻理解科研活动本身,包括科学研究发展规律、科技竞争形式特点、科研人员行为方式、科研成果传播影响等。科技信息是大量科研活动信息的承载和记录,科技信息的智能挖掘服务可以有效支撑科研创新能力研究。文章提出"智能科学家"的理念,首先分析了科研范式的演变与发展趋势,然后探讨了科技信息引领下的辅助科研创新、协助科研创新、自主科研创新三阶段构想,最终实现"智能科学家"的目标,最后介绍了"智能科学家"需要依托的若干关键技术方向。