AI applications of data sharing in agriculture 4.0: A framework for role-based data access control

Industry 4.0 and the associated IoT and data applications are evolving rapidly and expand in various fields. Industry 4.0 also manifests in the farming sector, where the wave of Agriculture 4.0 provides multiple opportunities for farmers, consumers and the associated stakeholders. Our study presents the concept of Data Sharing Agreements (DSAs) as an essential path and a template for AI applications of data management among various actors. The approach we introduce adopts design science principles and develops role-based access control based on AI techniques. The application is presented through a smart farm scenario while we incrementally explore the data sharing challenges in Agriculture 4.0. Data management and sharing practices should enforce defined contextual policies for access control. The approach could inform policymaking decisions for role-based data management, specifically the data-sharing agreements in the context of Industry 4.0 in broad terms and Agriculture 4.0 in specific.     

Governments as partners: The role of alliances in U.S. cleantech startup innovation

Accelerating innovation in clean energy technologies is a policy priority for governments around the world aiming to mitigate climate change and to provide affordable energy. Most research has focused on the role of governments financing R&D and steering market demand, but there is a more limited understanding of the role of direct government interactions with startups across all sectors. We propose and evaluate the value-creation mechanisms of network resources from different types of partners for startups, highlighting the unique resources of government partners for cleantech startups. We develop and analyze a novel dataset of 657 U.S. cleantech startups and 2,015 alliances with governments, firms, research organizations, and not-for-profit organizations from 2008 to 2012 and analyze short-term firm outcomes from the different alliances. Our findings highlight the importance of governmental partners in technology development alliances to catalyze cleantech startup innovation (the patenting activity of cleantech startups increases by 73.7 percent with every additional governmental technology alliance when compared to those startups that did not engage in such alliances) and as quality signals to private sector investors for licensing alliances (private financing deals increase by 155 percent for every additional license from a government organization). Overall, these findings extend the alliance perspectives on innovation, contribute to the emerging research on entrepreneurial ecosystems, and underline the need to develop empirical evidence in different sectors.     

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。     

The role of big data in smart city

The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data.     

Putting explainable AI in context: institutional explanations for medical AI

Ethics and Information Technology - This paper approaches the interaction of a health professional with an AI system for diagnostic purposes as a hybrid decision making process and conceptualizes...     

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.     

The possibility of deliberate norm-adherence in AI

Ethics and Information Technology - Moral agency status is often given to those individuals or entities which act intentionally within a society or environment. In the past, moral agency has...     

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.     

Healthcare big data processing mechanisms: The role of cloud computing

Recently, patient safety and healthcare have gained high attention in professional and health policy-makers. This rapid growth causes generating a high amount of data, which is known as big data. Therefore, handling and processing of this data are attracted great attention. Cloud computing is one of the main choices for handling and processing of this type of data. But, as far as we know, the detailed review and deep discussion in this filed are very rare. Therefore, this paper reviews and discusses the recently introduced mechanisms in this field as well as providing a deep analysis of their applied mechanisms. Moreover, the drawbacks and benefits of the reviewed mechanisms have been discussed and the main challenges of these mechanisms are highlighted for developing more efficient healthcare big data processing techniques over cloud computing in the future.     

面向AI算法的外观专利图像数据集

作为一种工业产权的外观专利在产业振兴中起着重要作用.外观专利采用的洛迦诺分类标准不支持图像分类,而AI算法直接在洛迦诺分类标准图像数据集并不能有效提升分类精度.因此,对外观专利审查员来说,外观专利的分类检索具有相当大的挑战.为此,本文提出先领域、再功能、后视觉的四级外观专利图像分类新标准,在此分类标准基础上构建了Pat...     

人工智能发展综述

概要的阐述了人工智能的概念、发展历史、当前研究热点和实际应用以及未来的发展趋势     

人工智能在拼石头游戏中的应用

游戏的人工智能研究是人工智能的主要研究领域之一,它涉及人工智能中的搜索算法和决策规划等。介绍了一款＂拼石头＂游戏,它是第18届日本全国高专编程竞赛竞技组的竞赛题目。针对此游戏规则,给出了参赛程序中使用的制胜策略、搜索算法以及决策规划方法等,最后分析了程序存在的不足并提出了改进思路。     

从责任的视角看人工智能系统的开发

为了最大限度地规避人工智能系统的决策和行为带来的潜在风险,人类应当对人工智能系统的行为和决策承担起责任.因此需要构建一种对所有人负责的人工智能系统开发路径.这意味着,人工智能系统的设计应当以人类的基本价值观和道德原则作为核心指导思想,遵循问责原则、责任原则和透明性原则;人工智能系统应当具备根据人类的基本价值观和道德原则来进行道德推理的能力;人工智能系统的开发应当保持多样性和包容性.     

Oracle数据库启动过程研究与实践

分析了Oracle数据库启动过程,阐述了导致Oracle数据库启动故障的不同原因,模拟了各种故障,提出了故障的处理过程,使读者对Oracle数据库启动过程有更深入的了解。     

智能化科研（AI4R）：第五科研范式

文章将“智能化科研”（AI4R）称为第五科研范式,概括它的一系列特征包括：（1）人工智能（AI）全面融入科学、技术和工程研究,知识自动化,科研全过程的智能化;（2）人机智能融合,机器涌现的智能成为科研的组成部分;（3）有效应对计算复杂性非常高的组合爆炸问题;（4）面向非确定性问题,概率统计模型在科研中发挥更大的作用;（5）跨学科合作成为主流科研方式,实现前4种科研范式的融合;（6）科研更加依靠以大模型为特征的科研大平台等。文章指出科研的智能化是一场科技上的革命,它带来的机遇和挑战将深刻影响中国科技发展的前途,呼吁各行业的科学家本身实现智能化转型。     

Nurturing a FinTech ecosystem: The case of a youth microloan startup in China

Financial technology, or FinTech, involves the design and delivery of financial products and services through technology. It impacts financial institutions, regulators, customers, and merchants across a wide range of industries. Pervasive digital technologies are challenging the fundamentals of the highly regulated financial sector, leading to the emergence of non-traditional payment systems, peer-to-peer money exchanges and increased turbulence in currency markets. This case study explores the development of a FinTech company in China that offers microloans to college students. Five lessons learned are presented for organizations to better manage the challenges and to leverage the opportunities amidst the disruption of financial sector. Our findings also shed light on how digital technology 1) offers the strategic capability for a firm to occupy a market niche in financial sector, 2) enables the generation of alternative credit scores based on non-traditional data, and 3) improves the financial inclusion of previously excluded market segments.     

The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage

The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors.     

Recommendations for the Development of the AI Industry in China

<正>The dramatic advancement of artificial intelligence(AI)technology in recent years has captured the attention of governments in various countries,many interested in its prospects for industrialization.With the support of the CAS Academic Divisions(CASAD),a task force headed by Prof.ZHANG Bo,a CAS Member from Tsinghua University.carried out studies on the development strategy of the academic discipline of AI and its industrialization with a focus on subjects such as state-of-the-art advances of AI,its industrial possibilities,and ways to develop the AI industry in China.     

Critically engaging the ethics of AI for a global audience

Ethics and Information Technology - This article introduces readers to the special issue on Selected Issues in the Ethics of Artificial Intelligence. In this paper, I make a case for a wider...