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1.
Ensmenger N 《Social studies of science》2012,42(1):5-30
Since the mid 1960s, researchers in computer science have famously referred to chess as the 'drosophila' of artificial intelligence (AI). What they seem to mean by this is that chess, like the common fruit fly, is an accessible, familiar, and relatively simple experimental technology that nonetheless can be used productively to produce valid knowledge about other, more complex systems. But for historians of science and technology, the analogy between chess and drosophila assumes a larger significance. As Robert Kohler has ably described, the decision to adopt drosophila as the organism of choice for genetics research had far-reaching implications for the development of 20th century biology. In a similar manner, the decision to focus on chess as the measure of both human and computer intelligence had important and unintended consequences for AL research. This paper explores the emergence of chess as an experimental technology, its significance in the developing research practices of the AI community, and the unique ways in which the decision to focus on chess shaped the program of AI research in the decade of the 1970s. More broadly, it attempts to open up the virtual black box of computer software--and of computer games in particular--to the scrutiny of historical and sociological analysis. 相似文献
2.
AI has received increased attention from the information systems (IS) research community in recent years. There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously. This study addresses this concern, by conducting a systematic literature review of AI research in IS between 2005 and 2020. The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented. In doing so, this study makes important contributions, namely (i) an identification of the current reported business value and contributions of AI, (ii) research and practical implications on the use of AI and (iii) opportunities for future AI research in the form of a research agenda. 相似文献
3.
Mitchell Marcus 《Journal of The Franklin Institute》2004,341(3):215-221
Since the mid-1950s, John McCarthy has made seminal contributions to a remarkably diverse range of important areas in computer science. In this report, we examine several of these contributions: As one of the fathers of artificial intelligence, he originated the logic-based paradigm of artificial intelligence (AI) research, arguably both the most productive approach to AI problems to date and the most promising for the future. He invented the time shared use of computer systems for the interactive development of software, a technique that allowed a single computer of large capacity to appear to a large number of simultaneous users as if that machine were theirs alone. He invented the LISP programming language, creating a program language design for the first time that was based on mathematical foundations rather than a partial abstraction away from the underlying computer hardware. The practical impact of his work has been enormous. Functional programming languages, of which LISP was the first, remain widely used, and the programming language constructs he invented remain the basis of modern programming control structures. The notion of time sharing, which he invented, remains a principle paradigm for the use of large computers even today. McCarthy's use of logic was among the primary intellectual sources of logic programming and automated theorem proving, and of many of their important applications. 相似文献
4.
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。 相似文献
5.
美国政府举国家之力发展人工智能技术以保持在世界的领先地位，对此，通过文献梳理的方法，从美国自身发展及其对外政策的视角系统分析美国人工智能战略部署和相关举措。分析发现，美国在人工智能领域的战略部署分为积累和升级两个阶段，均以制定发布政策文件和成立相关机构进行部署，已上升为国家战略并演变为世界领导权的竞争，基本实现人工智能创新价值链各环节全覆盖，各部门积极深耕细作，并日益重视在国防领域的应用；其对华策略主要采取成立专门机构、全面封锁、出口管制、限制人才流动等措施。为此，建议中国从组织管理职能、人才、立法、舆论等方面采取应对策略，增强自身实力、主动赢取先机。 相似文献
6.
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts. 相似文献
7.
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。 相似文献
8.
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. 相似文献
9.
Artificial Intelligence (AI) is viewed as having great potential for the public sector to improve the management of internal activities and the delivery of public services. However, realizing its potential depends on the proper implementation of the technology, which is characterized by unique factors, that afford or constrain its use. What these factors are and how they affect AI implementation is still poorly understood, and scholars call for studies to add empirical evidence to the existing knowledge. This study relies on a case study methodology and, by adopting an abductive approach, applies a double theoretical perspective: the Technology-Organization-Environment (TOE) framework and the Technology Affordances and Constraints Theory (TACT). Drawing on these combined lenses, we develop a conceptual framework that extends previous studies by showing how AI implementation is the result of a combination of contextual factors that are deeply interrelated and, specifically, how AI-related factors bring new affordances and constraints to the application domain. 相似文献
10.
基于深度学习算法的进步,人工智能逐渐有能力独立进行发明创造和文艺作品创作。本文主要探讨现行专利及著作权制度中规定的保护对象、权利人资格、专利及著作权的权属、侵权判定、侵权责任主体等对人工智能技术快速发展的适应及协调程度,研究指出：现有的专利和版权制度应当对人工智能的发明和作品持鼓励的态度,在排除不适宜作为专利或著作权保护对象的同时,人工智能的发明或作品的权利授予标准应当与人类的有所区分;相关权利人仍须对应自然人或法人,而非人工智能本身;相关专利侵权行为应包括间接侵权,同时应对人工智能作品安排“登记-授权”的著作权制度、参考临摹作品为人工智能绘画作品提供相应的授权使用制度等。本文还探讨了当前的专利法及著作权法在人工智能时代符合公平原则的程度,并提出解决方案：在“强人工智能时代”将人工智能的发明创造或作品作为公共财产,授予相应的开发者“数据处理权”作为一种新的邻接权,赋予人工智能创造物新的特别权利（Sui Generis）,修改专利法与著作权法中关于主要权利的相关规定等。 相似文献
11.
AI systems offer organizations great benefits causing decision-makers to invest more in these systems. The advantages of AI cannot be achieved without successful implementation. Thus, it is crucial to recognize the factors impacting the successful implementation of AI. It is also important to assess and rank these factors by their importance to assist decision-makers in implementing these systems and increasing the success rate. Due to its importance, scholars called for studies to expand our knowledge in this critical area. This paper identifies, extracts, and assesses the most critical factors that influence the implementation of AI systems. This study identifies nineteen factors and categorizes them into four categories: organization, technology, process, and environment. The analytical hierarchy process is used to evaluate the factors and the categories. The analysis offers two types of results, at the category level and the level of the factors. The results indicate that technology is the most significant of the four categories. The results also suggest that ethics is the most crucial factor among all nineteen factors. The order of all factors and discussions of the implications of the findings for practice and research are presented in the paper. 相似文献
12.
《Research Policy》2023,52(8):104828
With the rise of artificial intelligence (AI), professional services firms (PSFs) need to innovate their services to adapt to AI. However, traditional ad hoc innovations driven by individual professionals have limitations in incorporating new technology outside their expertise. Although service R&D—an organizational function for centralized coordination of service innovations in strategically targeted areas—is potentially effective, studies on service R&D have still been scarce. This case study aims to fill the gap by examining how PSFs can establish and utilize service R&D to innovate services, overcoming the challenges of AI adoption. An in-depth qualitative study was conducted on the process by which the Big Four audit firms incorporated AI into their external audit service in Japan in the 2010s. The analysis shows the detailed process of how newly created service R&D organizations advanced AI adoption in the case firms. This study contributes to the literature on innovations in services and PSFs by (1) demonstrating the neglected but critical role of service R&D as an innovation enabler beyond the existing expertise of service firms, (2) constructing a three-phase model of the evolution of the service R&D function, and (3) suggesting the significance of innovation process design for the legitimation of innovations. This study also expands our knowledge of AI adoption, presenting a process tailored to address the challenges inherent in AI adoption for PSFs. 相似文献
13.
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. 相似文献
14.
Despite heightened interest, integrating artificial intelligence (AI) into businesses remains challenging. Recent surveys show that up to 85 % of AI initiatives ultimately fail to deliver on their promises. Studies on successful AI applications that could provide invaluable lessons for organizations embarking on their AI journey are still lacking. Therefore, this study aims to understand how AI technology, people, and processes should be managed to successfully create value. Building on the resource orchestration perspective, this study analyzes the successful applications of AI at Alibaba's e-commerce fulfillment center. The findings indicate that the key AI resources include data, AI algorithms, and robots. These resources must be orchestrated (e.g., coordinated, leveraged, deployed) to work with other related resources, such as warehouse facilities and existing information systems, to generate strong AI capabilities. The key AI capabilities generated include forecasting, planning, and learning. More importantly, AI capabilities are not independent – they interact and coevolve with human capabilities to create business value in terms of efficiency (e.g., space optimization, labor productivity) and effectiveness (e.g., error reduction). The implications of understanding these social informatics of AI for research and practice are discussed. 相似文献
15.
《Research Policy》2022,51(7):104555
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. 相似文献
16.
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。 相似文献
17.
Organizations face increasing pressure to implement artificial intelligence (AI) within a variety of business processes and functions. Many perceived benefits surround AI, but a considerable amount of trepidation also exists because of the potential of AI to replace human employees and dehumanize work. Questions regarding the future of work in the age of AI are particularly salient in pre-adoption organizations, before employees have the opportunity to gain direct experience with AI. To cope with this potentially stressful situation, employees engage in cognitive appraisal processes based on their own knowledge and personal use of AI. These pre-adoptive appraisals of AI influence both affective and cognitive attitudes, which in turn trigger behavioral responses that influence an organization’s ability to leverage AI successfully. Our survey of 363 Taiwanese employees shows that perceptions of AI’s operational and cognitive capabilities are positively related to affective and cognitive attitudes toward AI, while concerns regarding AI have a negative relationship with affective attitude only. Interaction effects of employee knowledge and affective attitude are also observed. This work’s main contribution lies in the development of an empirically-tested model of the potential impact of AI on organizations from an employee perspective in the pre-adoption phase. These results have practical implications for how organizations prepare for the arrival of this transformative technology. 相似文献
18.
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。 相似文献
19.