【前沿追踪】人工智能顶刊文献分享

人工智能顶刊文献分享

人工智能（AI）是一种利用计算机系统模拟人类智力过程的技术，包括学习（通过数据和算法获取信息和规则）、推理（使用规则到达近似或确定结论）和自我修正。特点包括其能力在处理大量数据时表现出的高速度、高效率和自动化处理。

本次收集的四篇关于人工智能前沿的文献涵盖了人工智能对产业升级、创新团队增强、创新管理改革和技术预见的不同应用：Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship探讨人工智能对创业和可持续发展中的作用。通过分析482篇人工智能领域的研究文献，结果显示人工智能算法和模型可以帮助实现可持续发展目标，特别是在环境保护方面。Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models讨论人工智能在支持创新团队中的应用。分析了基于转换的语言模型如何在新产品开发中被用来增强创新效率，探讨了这些技术的优势与局限性。How AI revolutionizes innovation management-Perceptions and implementation preferences of AI-based innovators研究人工智能如何改变创新管理实践。通过对150名AI精通的创新管理者的探索性研究，揭示了不同群体如何利用AI在组织中推动创新，并讨论了AI的潜在影响和实施挑战。Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies探讨AI在提前发现技术趋势和支持创新管理中的应用。描述了一个AI数据挖掘模型帮助企业在高度自动化的环境中发现新兴趋势，该模型通过自动更新以适应新数据，显示出其作为预警系统的有效性。这些文献共同体现了人工智能如何在不同层面和领域内推动创新与发展，展示了AI在技术创新、产业升级、创新团队支持和预见性管理中的广泛应用和重要性。通过这些研究，AI的多面性和适应性得以突显，为未来的发展和应用提供了理论和实践的参考。

PART.1

Analysis of artificial intelligence-based technologies and approaches on

sustainable entrepreneurship

基于人工智能的可持续创业技术和方法分析

Keywords:Sustainable development;Business entrepreneurship;Artificial intelligence;

Sustainable development goals

关键词:可持续发展;企业创业;人工智能;可持续发展目标

ABSTRACT：

Research in the field of entrepreneurship has grown dramatically in the last decade and currently covers a wide variety of topics and concerns. Development in the field of artificial intelligence has a positive and negative impact on sustainable development. The current debate on sustainable development emphasizes the relevance of the environment, behavioral goals, and economic models to achieve sustainable goals, particularly in impoverished nations. However, recently, researchers have proposed the use of algorithms and models based on artificial intelligence (AI) to achieve sustainable development goals. In this context, this study intends to shed

light on AI’s crucial role in aiding sustainable development. To accomplish this goal, we collected data from Scopus (1994–2022). This includes a total of 482 research articles. The investigation shows the most essential patterns of study, enabling the visual mapping of Thematic Maps to suggest several new research paths. The results in the paper indicates that there is a positive relation between AI and environment development on sustainable entrepreneurship.

摘要：

创业领域的研究在过去十年中飞速增长，目前涵盖了各种各样的主题和关注点。人工智能领域的发展对可持续发展有积极和消极的影响。目前关于可持续发展的辩论强调环境、行为目标和经济模式与实现可持续目标的相关性，特别是在贫困国家。然而，最近研究人员提出使用基于人工智能的算法和模型来实现可持续发展目标。在这种背景下，本研究旨在阐明人工智能在帮助可持续发展方面的关键作用。为了实现这一目标，我们从Scopus（1994-2022）收集了数据。包括总共482篇研究文章。调查显示了最基本的研究模式，使文献专题的可视化能够提出几种新的研究途径。研究结果表明，在可持续创业中，人工智能与环境发展之间存在着正相关关系。

文献来源：

Gupta BB,Gaurav A,Panigrahi PK,et al.Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship[J].Technological forecasting and social change,2023,186:122152.

PART.2

Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models

用人工智能增强人类创新团队：探索基于转换的语言模型

Keyword:artificial intelligence;GPT-3;hybrid intelligence;innovation teams;prompt engineering;transformer-based language models

关键词:人工智能; GPT-3;混合智能;创新团队;快速工程;基于转换的语言模型

ABSTRACT：

The use of transformer-based language models in artificial intelligence(AI) has increased adoption in various industries and led to significant productivity advancements in business operations. This article explores how these models can be used to augment human innovation teams in the new product development process, allowing for larger problem and solution spaces to be explored and ultimately leading to higher innovation performance. The article proposes the use of the AI-augmented double diamond framework to structure the exploration of how these models can assist in new product development(NPD) tasks, such as text summarization, sentiment analysis, and idea generation. It also discusses the limitations of the technology and the potential impact of AI on established practices in NPD. The article establishes a research agenda for exploring the use of language models in this area and the role of

humans in hybrid innovation teams. (Note: Following the idea of this article,GPT-3 alone generated this abstract. Only minor formatting edits were performed by humans.)

摘要：

在人工智能（AI）中使用基于转换的语言模型，增加了各个行业对人工智能的采用率，并显著提高了业务运营的生产力。本文探讨了如何在新产品开发过程中使用这些模型来赋能创新团队，从而发现更多的问题以及得到更好的解决方案，并最终实现更高的创新绩效。本文使用人工智能赋能的双菱形框架来构建这些模型，展示人工智能如何帮助新产品开发（NPD）任务的探索，如文本摘要、情感分析和想法生成。研究还讨论了该技术的局限性以及人工智能对NPD既定实践的潜在影响。本文建立了一个研究流程，以探索语言模型在这一领域的使用以及人类在混合创新团队中的作用。（注意：根据本文的猜想，GPT-3单独生成了此摘要。只有少量的格式编辑是由人类完成。）

文献来源：

Bouschery SG,Blazevic V,Piller FT.Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models[J].Journal of product innovation management,2022,40(02):139-153.

PART.3

How AI revolutionizes innovation management-Perceptions and implementation preferences of AI-based innovators

人工智能如何彻底改变创新管理——基于人工智能创新者的认知和执行偏好

Keywords:AI-based innovation management;Innovation process;Organizational setup;Organizational context;Cluster analysis

关键词:基于人工智能的创新管理;创新过程;组织设置;组织背景;聚类分析

ABSTRACT：

The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations. Our exploratory study among 150 AI-savvy innovation managers reveals four different clusters in terms of how organizations may use and implement AI in their innovation management

ranging from (1) AI-Frontrunners, (2) AI-Practitioners, and (3) AI-Occasional innovators to (4) Non-AI innovators. The different groups vary not only in their strategy, organizational structure, and skill-building but also in their perceived potential, understanding of the required changes, encountered challenges, and organizational contexts. Our study contributes to a better understanding of the current state of AI-based innovation management, its impact on future innovation practice, and differences in organizations’ AI ambitions and chosen implementation approaches.

摘要：

人工智能的应用有望为创新管理带来新的机遇，重塑组织的创新实践。我们对150名精通人工智能的创新管理者进行了探索性研究，揭示了组织如何在创新管理中使用和实践人工智能，分为四个不同集群，包括（1）人工智能前沿者、（2）人工智能从业者（3）人工智能偶然创新者（4）非人工智能创新者。不同的群体不仅在战略、组织结构和技能建设方面存在差异，而且在感知潜力、对所需变革的理解、遇到的挑战和组织环境方面也存在差异。我们的研究有助于更好地了解基于人工智能的创新管理现状，人工智能对未来创新实践的影响，以及组织对人工智能的态度和选择实施方法的差异。

文献来源：

Füller J, Hutter K, Wahl J,et al.How AI revolutionizes innovation management-Perceptions and implementation preferences of AI-based innovators[J].Technological forecasting and social change,2022,178:121598.

PART.4

Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies

创新中的人工智能：如何发现新兴趋势和技术

Keywords：Artificial intelligence (AI), computer-aided foresight, corporate foresight, innovation management, machine learning, strategic decision making, strategic foresight, technology management, trend detection.

关键词：人工智能、电脑辅助预测、企业前瞻、创新管理、机器学习、战略决策、策略预测、科技管理、趋势预测

ABSTRACT：

Firms apply strategic foresight in technology and innovation management to detect discontinuous changes early, to assess their expected consequences, and to develop a future course of action enabling superior company performance. For this purpose,

an ever-increasing amount of data has to be collected, analyzed,and interpreted. Still, a major part of these activities is performed manually, which requires high investments in various resources.To support these processes more efficiently, this article presents an artificial-intelligence-based data mining model that helps firms

spot emerging topics and trends at a higher level of automation than before. Its modular structure consists of components for query generation, data collection, data preprocessing, topic modeling,topic analysis, and visualization, combined in such a way that only a minimum amount of manual effort is required during its initial set up. The approach also incorporates self-adaptive capabilities,allowing the model to automatically update itself once new data has become available. The model parameterization is based on latest research in this area, and its threshold parameter is learnt during supervised training using a training data set. We have applied our model to an independent test data set to verify its effectiveness as an early warning system. By means of a retrospective analysis, we show in three case studies that our model is able to identify emerging technologies prior to their first publication in the Gartner Hype Cycle for Emerging Technologies. Based on our findings, we derive both theoretical and practical implications for the technology and innovation management of firms, and we suggest future research opportunities to further advance this field.

摘要：

企业应用技术和创新管理的战略预见，以及早发现不规律的变化，评估其预期后果，并制定未来的行动方针，使公司业绩优异。为此，需要收集、分析和解释越来越多的数据。尽管如此，这些活动的主要部分仍然是手动执行的，这需要在各种资源上投入大量资金。为了更有效地支持这些过程，本文提出了一个基于人工智能的数据挖掘模型，帮助企业在比以前更高的自动化水平上发现新兴主题和趋势。它的模块化结构由查询生成、数据收集、数据预处理、主题建模、主题分析和可视化等组件组成，以这样一种方式组合在一起，即在初始设置过程中只需要最少的手工工作量。该方法还结合了自适应能力，允许模型在新数据可用时自动更新自身。该模型的参数量化是基于该领域的最新研究，其阈值参数是在监督训练期间通过训练数据集获得的。我们将模型应用于一个独立的测试数据集，以验证其作为预警系统的有效性。通过回顾性分析，我们在三个案例研究中表明，我们的模型能够在新兴技术首次发表在Gartner Hype Cycle for Emerging Technologies之前识别出该技术。基于我们的研究结果，我们得出了理论和实践意义，并应用于企业技术开发和创新管理，我们提出该领域的未来研究建议，以进一步推动这一领域的发展。

文献来源：

Muhlroth C, Grottke M.Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies[J].IEEE transactions on engineering management,2020,69(02):493-510.

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图文：张明昊

编辑：李发珍

审核：宁靓、姜忠辉