Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper, we take a modern look at Julia's features and ecosystem, assess the current state of the language, and discuss its viability and pitfalls as a replacement for Python as the de-facto scientific machine learning language. We call for the community to address Julia's language-level issues that are preventing further adoption.

翻译：Julia 被誉为 Python 在科学机器学习和数值计算领域的潜在继任者，其宣称在人体工程学和性能方面均有改进。自 Julia 于 2012 年诞生并于 2017 年明确其语言目标以来，其生态系统和语言层面的特性已取得巨大发展。本文以现代视角审视 Julia 的特性与生态系统，评估该语言的当前状态，并讨论其作为事实上的科学机器学习语言替代 Python 的可行性及潜在缺陷。我们呼吁社区着手解决阻碍 Julia 进一步普及的语言层面问题。