The yin yang of AI: Exploring how commercial and non-commercial orientations shape machine learning innovation

IF 7.5 1区 管理学 Q1 MANAGEMENT
Edgar Brea
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Abstract

The scale of the potential implications of machine learning (ML) has prompted discussions on the issues of corporate control and technological openness. However, how commercial and non-commercially oriented organisations each contribute to ML progress remains an open question. This study uses the recombinant innovation perspective as a lens to explore recombinant patterns across projects in an open source software (OSS) environment – where a great deal of ML innovation occurs – and assess how commercial orientation influences such patterns. It builds on a unique dataset containing data on 28,443 OSS projects, their code dependencies and the organisations owning them. Exploratory analyses reveal that ML projects combine larger and more diverse components, and produce more atypical combinations in shorter timeframes than other OSS projects, and that both company and non-company owned ML projects contribute to such recombinant atypicality. Regression analyses indicate that company owned ML projects tend to rely more on distant combinations of technical knowledge, whereas non-company owned ML projects tend to produce more novel combinations of application ideas. By extending the theories of recombinant innovation and motivation in OS innovation into a new setting – ML technology, this study contributes to both literatures by confirming that the link between distant recombination and innovation still holds in contexts characterised by complex search spaces, and by suggesting complementarities between commercial and non-commercial orientations in OSS environments rich in knowledge diversity and recombinant activity.

人工智能的阴阳:探索商业和非商业取向如何塑造机器学习创新
机器学习(ML)潜在影响的规模引发了有关企业控制和技术开放问题的讨论。然而,商业机构和非商业机构各自如何促进 ML 的发展仍是一个未决问题。本研究以重组创新视角为视角,探讨开源软件(OSS)环境中各项目之间的重组模式(大量的 ML 创新都发生在该环境中),并评估商业导向如何影响这种模式。它以一个独特的数据集为基础,该数据集包含 28,443 个开放源码软件项目、其代码依赖关系以及拥有这些项目的组织的数据。探索性分析表明,与其他开放源码软件项目相比,ML 项目结合了更大和更多样化的组件,并在更短的时间内产生了更多非典型组合。回归分析表明,公司所有的 ML 项目往往更依赖于技术知识的远距离组合,而非公司所有的 ML 项目往往产生更多新颖的应用创意组合。通过将操作系统创新中的重组创新和动机理论扩展到一个新的环境--ML 技术中,本研究证实了远距离重组和创新之间的联系在以复杂搜索空间为特征的环境中仍然存在,并提出了在知识多样性和重组活动丰富的开放源码软件环境中商业和非商业取向之间的互补性,从而为这两方面的研究做出了贡献。
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来源期刊
Research Policy
Research Policy MANAGEMENT-
CiteScore
12.80
自引率
6.90%
发文量
182
期刊介绍: Research Policy (RP) articles explore the interaction between innovation, technology, or research, and economic, social, political, and organizational processes, both empirically and theoretically. All RP papers are expected to provide insights with implications for policy or management. Research Policy (RP) is a multidisciplinary journal focused on analyzing, understanding, and effectively addressing the challenges posed by innovation, technology, R&D, and science. This includes activities related to knowledge creation, diffusion, acquisition, and exploitation in the form of new or improved products, processes, or services, across economic, policy, management, organizational, and environmental dimensions.
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