中国人工智能产品的设计评价现状与发展趋势研究综述

目的 针对中国的设计现状,通过探索现有产品的设计评价体系是否适切于人工智能产品,能够为人工智能产品的设计及评价研究提供一个更完整的视角。方法 以对比分析为主要研究手段。探析产品和人工智能产品的核心理念,分析两者之间的区别,为研究主体定性;针对产品和人工智能产品的设计评价展开数据计量分析,了解2个领域的研究热点与发展趋势;剖析人工智能、人工智能产品和人工智能产品设计评价的现存问题和发展演进,从历史角度梳理阶段性研究成果并展开热点评述,进一步定义人工智能产品的设计评价热点前沿与发展趋势;综合所有分析结果共同定位现有产品的设计评价体系对人工智能产品设计评价的适切度。结果 论述了人工智能产品设计评价的重要作用,梳理了现存理论与方法,定位了发展程度,预测了未来的研究重点和发展方向,为构建更加科学、客观且适切的人工智能产品的设计评价体系提供了理论依据。有助于把控生成产品的质量,提高产品应用落地率,使之与研发投入保持相互平衡的状态,同时可反向助力于优化人工智能的算法和技术。结论 综合基本逻辑的区别探析、数据计量分析和发展演进研究的结果,论证了中国现有产品的设计评价体系与人工智能产品的设计评价体系是部分适切的关系,因此人工智能产品的设计评价体系研究与构建不能完全移植产品设计评价的研究成果,需要在部分借鉴与参考现有成果的基础之上展开以人工智能产品特殊性为核心的针对性研究,进而构建成熟的人工智能产品的设计评价体系。     

Factors influencing the use of artificial intelligence in government: Evidence from China

Some studies have discussed the potential and challenges related to the use of artificial intelligence (AI) in government. However, there are few empirical studies that have examined factors that influence the use of AI in government. By collecting policy documents and empirical data from the government, IT enterprises, and the public in China, we identified the influencing factors in the three stages of government adoption, implementation, and decision-making. The research results show that the influencing factors of government application of AI are different at different stages and with different stakeholders’ backgrounds.     

Governing AI during a pandemic crisis: Initiatives at the EU level

After the outbreak of Covid-19, the European Commission (EC) promptly took the initiative to lead and coordinate a common European response. The actions unfolded in several directions, paving the way to the uptake of AI-related solutions and placing hope in these tools to face crises, namely of a public health and global nature. In this article, we focus on initiatives for the uptake of AI-related solutions from the experimental level towards implementation. The Repository of AI and Robotics solutions, launched in 2020, is an example of an initiative put forth to leverage and disseminate knowledge on AI, expanding the fields of application and fostering the development and adaptation of cutting-edge technologies to explore how they can assist in tackling specific tasks during a public health crisis. Using this database, the article outlines the promise of AI as a hope for handling specific needs and tasks and how the uptake of such technologies was accelerated during the Covid-19 pandemic. In extension, we frame initiatives for the uptake of AI-enabled solutions from a governance perspective, focusing on the establishment of frameworks for ethical and trustworthy AI by defining principles and standards that aim to protect the underlying values deemed fundamental.     

Towards a deliberative framework for responsible innovation in artificial intelligence

The rapid innovation in artificial intelligence (AI) is raising concerns regarding human autonomy, agency, fairness, and justice. While responsible stewardship of innovation calls for public engagement, inclusiveness, and informed discourse, AI seemingly challenges such informed discourse by way of its opacity (poor transparency, explainability, and accountability). We apply a deliberative approach to propose a framework for responsible innovation in AI. This framework foregrounds discourse principles geared to help offset these opacity challenges. To support better public governance, we consider the mutual roles and dependencies of organizations that develop and apply AI, as well as civil society actors, and investigative media in exploring pathways for responsible AI innovation.     

Artificial intelligence and sustainable development goals nexus via four vantage points

Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets to extract meaningful information for policymakers and researchers alike - shedding light on how AI is being used and can potentially be employed in the future to achieve the SDGs. To this end, we devised a methodology using keyword-matching and keyword-similarity to compute the relevance of the SDGs for a given document. SDG metadata and AI4SDG Projects (Oxford initiative on AI4SDGs) were used to validate our methodology. We find an imbalance of coverage with SDG 9 (Industry Innovation and Infrastructure) having the highest representation (with 50.3% of our data containing references to it) compared to SDGs 5, 6, 14, and 15, which have the lowest representation (5% of observed data). Findings from this study suggest that the development of AI technology is focused on improving the current economic growth, but it might neglect important societal and environmental issues.     

人工智能产品与服务体系研究综述

目的在新一代人工智能发展背景下,分析并明确人工智能产品及其服务体系的特征与价值,指出未来发展趋势,为相关设计、技术与应用研究提供参考。方法从人工智能的概念出发,给出人工智能产品及其服务体系的定义;收集并分析典型的人工智能产品和相关研究,总结整理人工智能产品的关键特征和支撑技术;探索人工智能产品的典型服务场景,对相关研究现状进行综述;基于前文分析对未来发展趋势及挑战进行预测。结论指明了人工智能产品具有情境感知、自适应学习、自主决策、主动交互与协同的典型特征;描绘了以数据和计算能力为基础、算法为核心、多种底层技术与通用技术为支持的场景应用的人工智能产品支撑技术框架;分析了人工智能产品的服务体系在不同场景中可以被赋予的价值;预测了由技术驱动向设计驱动转化、由单品视角向服务体系视角转变的未来发展趋势。     

Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory

With the rapid development of artificial intelligence (AI), AI anxiety has emerged and is receiving widespread attention, but research on this topic is not comprehensive. Therefore, we investigated the dimensions of AI anxiety using the theoretical model of integrated fear acquisition and a questionnaire survey. A total of 494 valid questionnaires were recovered. Through a first-order confirmatory factor analysis (CFA), a factor model of AI anxiety was constructed, and eight factors of AI anxiety were verified. Then, a second-order CFA was applied to verify the adaptation of the factor structure of AI anxiety to fear acquisition. We identified four dimensions of AI anxiety and proposed a theory of AI anxiety acquisition that illustrates four pathways of AI anxiety acquisition. Each pathway includes two factors that cause AI anxiety. We conclude by analyzing the limitations of current AI anxiety research and proposing a broader research agenda for AI anxiety.     

Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation

Artificial intelligence (AI) technologies and their fields of application are among the most debated developments of recent times. Although being widely discussed academically, publicly and in policy debates, certain aspects of their research, development and application are completely ignored, namely the impact AI has on animals. Animals are affected by the research on and development of this technology since it partially relies on animal testing. In addition, AI is also being applied to improve monitoring and marketing of animals in an agricultural context. We argue that it is insufficient to exclude these aspects from debates around AI. In addition to the surveillance-applications on animals, which can be evaluated as impacting them negatively, AI applications, from which individual animals can benefit, do exist. These can primarily be found in nature and wildlife conservation, as we point out at the end of the paper. By providing an overview on how these technologies are applied to animals and how this affects them, this paper aims to fill a previously existing research gap.     

Multi-Agent Deep Reinforcement Learning-Based Resource Allocation in HPC/AI Converged Cluster

As the complexity of deep learning (DL) networks and training data grows enormously, methods that scale with computation are becoming the future of artificial intelligence (AI) development. In this regard, the interplay between machine learning (ML) and high-performance computing (HPC) is an innovative paradigm to speed up the efficiency of AI research and development. However, building and operating an HPC/AI converged system require broad knowledge to leverage the latest computing, networking, and storage technologies. Moreover, an HPC-based AI computing environment needs an appropriate resource allocation and monitoring strategy to efficiently utilize the system resources. In this regard, we introduce a technique for building and operating a high-performance AI-computing environment with the latest technologies. Specifically, an HPC/AI converged system is configured inside Gwangju Institute of Science and Technology (GIST), called GIST AI-X computing cluster, which is built by leveraging the latest Nvidia DGX servers, high-performance storage and networking devices, and various open source tools. Therefore, it can be a good reference for building a small or middle-sized HPC/AI converged system for research and educational institutes. In addition, we propose a resource allocation method for DL jobs to efficiently utilize the computing resources with multi-agent deep reinforcement learning (mDRL). Through extensive simulations and experiments, we validate that the proposed mDRL algorithm can help the HPC/AI converged cluster to achieve both system utilization and power consumption improvement. By deploying the proposed resource allocation method to the system, total job completion time is reduced by around 20% and inefficient power consumption is reduced by around 40%.     

AI设计下的文本视觉问答技术

目的 分析基于AI设计的文本视觉问答模型的有效性,旨在利用AI设计更好地指导当前AI模型的构建,提升模型效果和用户体验.方法 以传统文本视觉问答框架为基础,结合AI设计改进当前模型.具体包括加强基于场景设计原则的关系挖掘,根据不同理解层次需求的答案关键词预测,并对模型被投入应用所将面临的问题的分析.结果 基于AI设计完善模型可进一步提升模型效果;同时,通过AI设计对不同年龄认知差异的建模可指导回复生成,提升整体用户体验.结论 通过理论分析和实验对比,可以得出AI设计是AI技术投入到应用的一个重要步骤.基于AI设计对模型进行重构,可提高当前模型的效果,解决AI技术落地中将面临的用户体验问题,满足不同人群的需求.     

认知科学与设计研究

目的阐述设计师应如何看待认知科学。方法结合高端重大装备设计领域的学识和经验,详细解读认知科学。结论认知科学是人工智能的基础,设计师必须具备理解任务内容和解决问题的智慧,也就是“心智”,即人类学习、理解、产生决策的心智能力。人工智能尝试用计算来表达人的理解和心智。心智是认知过程产生结果的“集合”,因此设计师首先要懂得认知科学。     

The Theory of Innovation and Innovative Development. AI Scenarios in Russia

This article explores different scenarios of implementing artificial intelligence (AI) in Russia. The relevance of the research and the range of problems addressed in it consist in the fact that Russia is not fully realizing the potential of innovative economic development. AI can accelerate the innovative development of Russia's Economy, but it entails the risks of its destabilization and therefore requires further research. This article contributes to the theory of innovation, thereby specifying the logic of the optimization of innovative development and the optimal scale in a strategy of risk management. The effectiveness of the digital modernization of Russia's Economy based on AI will be remarkably high. The index of global competitiveness achieves maximum value (7 points), increasing by 1.52 times. The innovation index of the economy grows 1.15 times. The unemployment level will rise 1.62 times, and the level of cybersecurity will decrease 6.52 times. Both advantages from the implementation of AI and risks of digital modernization of the Russian Economy are determined. The significance of the research consists in the application of the offered method for determining the optimal intervals of AI usage in Russia. The key conclusion that can be drawn from the research is the justification of the need to promote the modernization of the Russian Economy on the basis of AI with a view to accelerating its innovative development until 2024. The consequences of the research are involved with the improvement of practices of public regulation of the innovative development of the Russian Economy by promoting the dissemination of AI according to recommendations proposed by the authors.     

基于IPA分析人工智能生成的包装设计满意度及策略研究

目的 从消费者感知评价角度出发,对人工智能（AI）生成化妆品包装设计的关键因素进行定量研究,分析其与消费者满意度之间的关系,提供基于AI的包装设计相关开发策略。方法 通过文献研究及用户访谈,综合专家意见构建出包含5个维度共18项因子指标的评价体系;依据评价指标对AI生成的包装设计进行受访者问卷调查;采用IPA模型分析AI生成化妆品包装设计的各项因子指标的重要程度与满意度。结果 研究发现消费者对AI生成包装设计的信息传达度和创意吸引力维度等方面表现出较高的认可度和满意度,而对包装的功能可用性和用户体验性方面则普遍倾向于不满意。结论 增强AI生成包装的实用性和用户体验是提升消费者满意度的关键。可通过现有成熟的包装公模训练AI模型、强化多模态学习丰富数据集、优化AI设计生成过程的可解释性及可控性、构建多元利益相关者参与的创生设计平台等策略提升AI设计的有效性及消费者满意度。     

Drivers,barriers and social considerations for AI adoption in SCM

This study explored the challenges and recommendations for implementing Artificial Intelligence (AI) in SC Management (SCM). The experts identified several drivers for AI adoption in SCM, including increased efficiency, improved decision-making, and reduced costs. However, they highlighted several barriers to AI adoption, such as data quality and management issues, resistance to change, and lack of understanding and trust in AI. To overcome these barriers and ensure successful AI implementation, companies should involve all stakeholders, focus on data quality and management, and ensure the AI solution integrates with existing processes and workflows. In addition, companies should also avoid common mistakes when implementing AI, such as neglecting the importance of explainability and transparency in AI decision-making, underestimating the importance of involving all stakeholders, and rushing into large-scale implementation without conducting small-scale pilot projects. By following the recommendations and avoiding common mistakes, companies can harness the benefits of AI in SCM while minimizing risks and challenges.     

人工智能辅助技术在包装设计中的应用研究

目的 研究人工智能辅助技术介入包装设计流程的多元方式,分析人工智能技术应用现状,推导出更适应当下包装设计市场需求的技术应用策略。方法 通过分析包装设计流程中人工智能技术实际应用案例,总结智能技术介入背景下包装设计的转变,探讨技术的创新及应用的层次。结合设计实践进一步研究技术的应用策略,展望人工智能技术在包装设计中的应用前景。结论 人工智能辅助技术可从用户数据分析、个性化设计延展、方案智能优化等多个应用层面优化包装设计,带来更大的生产效益。合理应用人工智能技术有助于转变设计思维、拓展包装设计形式。在包装设计中根据不同应用场景,使用智能技术辅助设计,可以提升设计效率,满足当前数字化经济时代发展的需求。     

非家庭式智能养老的伦理研究和治理设计

目的非家庭式智能养老的伦理研究和治理设计旨在为中国养老提供解决对策。方法提出非家庭式智能养老、数字劳动力、养老治理的定义,从政治、教育、哲学、社会学、经济学、管理、人口、法学、计算机科学与技术(大数据、人工智能、人机交互)、设计等多维视角综合研究,进行对抗性设计。综合运用各种方法采集、分析、研究数据,包括实地调研,问卷调研,运用网络爬虫、舆情分析软件、CiteSpace等大数据采集和分析软件,利用争胜性信息设计方法,揭示养老各因素的关联模式。利用多层次数据提取,使养老、伦理、治理、AI等形成一个完整的养老循环系统。结论养老的治理需要放在一个大的、复杂的循环系统中解决。非家庭式智能养老的伦理是治理复杂养老民生问题的基础,通过构建和设计多边社会契约和广泛的民主,重塑和设计AI时代下公共行动者的义务、责任、利益,非家庭式智能养老行动的观念、法律、信仰和路线,养老体系中工作流程和岗位、养老保障、智能平台、智能产品、资源、服务,以居家为主,其它少量辅助的养老方式等,开展公共行动,从而形成完整的养老体系和治理对策,并能够让其自我成长。     

基于5G与AI技术的家庭服务机器人交互设计研究

目的 针对家庭服务机器人的多功能、多智能、重交互等迭代需求,探究5G与AI技术在家庭服务机器人的多功能联动、智能交互的设计。方法 以家庭服务机器人的交互设计为研究对象,使用5G技术,协同智能语音、智能感知、计算机视觉等AI核心技术,对居家养老机器人的多智能体现及交互设计进行研究。结果 在5G与AI技术融合下,完成了以“智慧养老”为核心的居家养老机器人设计,输出了居家养老机器人的智能交互造型、交互方式及界面设计表达。结论 将5G互联和AI技术相结合,不仅为家庭服务机器人的功能、智能提供了新模式,而且增加了用户与家庭服务机器人的互动体验。     

Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude

Despite having one of the most advanced healthcare systems in the world, Japan is expected to experience a shortage of nearly half a million healthcare workers by 2025 due to its rapidly aging population. In response, government authorities plan to implement a wide range of AI-driven healthcare solutions. These include care robots that assist the physically handicapped or elderly, chatbots that provide anonymous online mental health consultation, and diagnostic software utilizing machine learning. Yet one of the most popular smart technologies to augment the nation's already overstrained and undermanned healthcare system is a little known but emerging emotional AI technologies, i.e., deep learning systems trained to read, classify, and respond to human emotions. These technologies are being sold on a commercial level not only to the public but also to rehabilitation centers, local hospitals, and senior citizen residences. Although the augmentation of healthcare services to intelligent machines may seem like a logical step in a country well-known for its long-standing affection toward robots, Japanese society is also known for its adherence to established social relations and traditional institutional practices, especially, in the realm of medical care. In order to gauge Japanese acceptance of emotion-sensing technology, we analyze a dataset of 245 visitors to clinics and hospitals in a typical suburban area in Japan using multiple linear regression. The results show that in general, senior and male patients perceive the emotional AI technology more negatively. For behavioral variables, patients' level of familiarity has positive correlations with attitudes toward emotional AI-based applications in private setting (β_{Familiarity_AttitudePri}=0.346, p<0.001) and public setting (β_{Familiarity_PublicAttitude}=0.297, p<0.001); while concern for losing control to AI has negative correlations with the attitudes' variables: private setting (β_{LosingControl_AttitudePri}=−0.262,p=0.002) and public setting (β _{LosingControl_AttitudePub}=-0.188, p=0.044). Interestingly, concerns over violation of privacy and discrimination are non-significant correlates, which contradict the emerging literature on this subject. We further contextualize the findings with insights afforded by an understanding of Japanese culture as well as the relevant literature on care robots in Japan. Finally, policy and education implications to promote emotional AI acceptance to the general and senior members of the society are provided.     

Intermediaries in regional innovation systems: An historical event-based analysis applied to AI industry in Montreal

This article combines intermediaries and regional innovation system literatures to develop an analytical approach to study the role of intermediaries using the case of the artificial intelligence (AI) industry in Montreal as a testing ground. More specifically, it explores what kinds of intermediaries dominate the Montreal AI innovation system, the functions and types of activities they perform in fostering and supporting the development of the AI industry, as well as their ability to link different actors both within and outside this configuration. Based on a historical event analysis, we found that there is a great diversity of intermediaries present in Montreal's AI innovation system and there is an association between the type of intermediary and the functions and activities they perform. We also find that while intermediaries are instrumental in both facilitating local and non-local linkages, their priority is to orchestrate local interactions.     

Embrace artificial intelligence technologies for advanced analytics and management of intellectual properties

This short communication describes the background, objectives, and publications of World Patent Information's special issue on Artificial Intelligence for Intellectual Property (AI for IP). The report serves as the editorial for the WPI's special issue on AI for IP. We look forward to receiving future contributions in research articles, literature/book reviews, conference reports and short communications in the subject areas.