摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-研究人员详细介绍了机器学习的新数据。根据NewsRx Ed Itors在澳大利亚圣卢西亚的新闻报道，这项研究指出，“机器学习(ML)潜在影响的规模促使人们讨论公司控制和技术开放问题。然而，商业和非商业导向的组织结构如何各自促进ML进步仍然是一个悬而未决的问题。”我们的新闻记者从Queensland大学的研究中获得了一句话：“本研究以重组创新视角为视角，探索开放源代码软件(OSS)环境中跨项目的重组模式，并评估商业导向如何影响这些模式。它建立在一个独特的数据集上，该数据集包含28443上的数据。”探索性分析显示，与其他OSS项目相比，ML项目结合了更大和更多的三维组件，在更短的时间内产生了更多的非典型组合，公司和非公司拥有的ML项目都有助于这种重组非典型性。回归分析表明，公司拥有的ML项目倾向于更依赖技术知识的远程组合。而非公司拥有的ML项目往往会产生更新颖的应用思想组合。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of St. Lucia, Australia, by NewsRx ed itors, the research stated, “The scale of the potential implications of machine learning (ML) has prompted discussions on the issues of corporate control and te chnological openness. However, how commercial and non -commercially oriented org anisations each contribute to ML progress remains an open question.” Our news journalists obtained a quote from the research from the University of Q ueensland, “This study uses the recombinant innovation perspective as a lens to explore recombinant patterns across projects in an open source software (OSS) en vironment - where a great deal of ML innovation occurs - and assess how commerci al orientation influences such patterns. It builds on a unique dataset containin g data on 28,443 OSS projects, their code dependencies and the organisations own ing them. Exploratory analyses reveal that ML projects combine larger and more d iverse components, and produce more atypical combinations in shorter timeframes than other OSS projects, and that both company and non -company owned ML project s 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 co mbinations of application ideas.”
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