How Can Artificial Intelligence Deep Learning Platform Achieve Open Source Innovation
Open source innovation is becoming the mainstream trend for artificial intelligence(AI)deep learning platforms,particularly in emerging economies where open source practices have started relatively later.It is worth exploring how AI deep learning platforms can achieve open source innovation under such circumstances.Existing literature on open source innovation has formed three main schools:process-based view,product-based view,and structure-based view.However,these studies have not focused on constructing process theories specific to open source innovation in AI deep learning platforms.Moreover,the implicit assumptions in current research do not apply to the Chinese context.The reason is that existing research,primarily based on developed economies,assumes that open source innovation is a spontaneous,uncontrolled,and non-preset activity driven by developers.These underlying assumptions fail to explain the success of another type of open source innovation in China's practices,namely,the"preset open source innovation"initiated by AI enterprises.This study focuses on achieving open source innovation in AI deep learning platforms in China.Based on a longitudinal single case study of the open source leader—Baidu PaddlePaddle deep learning platform,this study follows the process research paradigm and constructs an integrated logical framework of"driving logic-action mode-open source mechanism-open source output".The findings are as follows.First,the open source innovation process of AI deep learning platforms is seen as an evolutionary process consisting of three stages:the momentum accumulation stage,the intense change stage,and the mature integration stage.They undergo a dynamic change of three driving logics:"competitive advantage","efficient iteration"and"value optimization".Second,driven by different driving logics,the actors involved in open source innovation exhibit three distinct action patterns sequentially:"instrumental mode","spiral mode"and"ecological mode".Third,the open source innovation mechanisms in the above process include both the mechanisms within the stage("linkage mechanisms")and between the stages("evolution mechanisms").The linkage mechanisms include the"routine transformation mechanism","self-adaptation fusion mechanism"and"polymerization symbiosis mechanism",while the evolution mechanisms include the"communitization evolution mechanism"and"commercialization evolution mechanism".Fourth,the above process results in three separate open source results:"open source process collaboration","open source paradigm shift"and"open source systems co-creation",realizing the evolution of open source capabilities from low to high levels.Then,this study compares the differences between preset open source innovation and non-preset open source innovation.The theoretical contributions of this study mainly include the following two aspects.First,it constructs a process theory model for open source innovation in AI deep learning platforms from a dynamic perspective.It focuses on the progressive evolution of open source innovation in AI deep learning platforms over an extended time span,revealing the evolutionary patterns and underlying mechanisms of open source innovation based on a process-oriented view.Second,this study uncovers an alternative form of open source innovation,preset open source innovation,under the latecomer open source context of emerging economies.This broadens the fundamental assumptions and theoretical boundaries of open source innovation and offers practical insights for the open source development of AI deep learning platforms.
open source innovationAI deep learning platformprocess theorization
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