评估Julia在科学机器学习中的现状，探讨其替代Python的潜力与挑战。
The State of Julia for Scientific Machine Learning
- 系统梳理Julia的语言特性与生态发展
- 指出其性能优势但存在采纳障碍
- 呼吁社区解决阻碍推广的语言问题
自2012年发布以来，Julia被寄望成为科学机器学习和数值计算领域对Python的潜在替代者，具备更优的语法设计与运行性能。自2017年明确语言目标以来，其生态系统和语言特性已显著发展。本文从当前视角审视Julia的功能与生态，评估其作为事实标准科学机器学习语言的可行性与局限性，并呼吁社区关注并解决阻碍进一步采用的语言层面问题。
原文摘要 · Abstract (English)
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.
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