论文标题
当不使用机器学习时：潜在和局限性的观点
When not to use machine learning: a perspective on potential and limitations
论文作者
论文摘要
人工智能（AI）在技术领域的无与伦比的成功促进了科学界的大量研究。事实证明，它是一种强大的工具，但是与任何快速发展的领域一样，大量信息可能会令人困惑，令人困惑，有时是误导性的。这可以使在历史上稀缺的资金和被称为AI冬季的耗尽期望时期结束的相同炒作周期中迷失。此外，虽然创新，高风险研究的重要性不能被夸大，但也必须了解可用技术的基本限制，尤其是在年轻领域，这些规则似乎不断地被不断地重写，并且随着应用到高风险场景的可能性增加。从这个角度来看，我们重点介绍了数据驱动建模的指导原理，这些原则如何以几乎具有神奇的预测能力赋予模型，以及它们如何对他们可以解决的问题范围施加限制。特别是，了解何时不使用数据驱动技术（例如机器学习）并不是通常探索的东西，而是与知道如何正确应用技术一样重要。我们希望关注的讨论能为整个科学的研究人员提供更好的了解，以更好地理解该技术是合适的何时，要注意的陷阱，最重要的是，有信心利用他们可以提供的力量。
The unparalleled success of artificial intelligence (AI) in the technology sector has catalyzed an enormous amount of research in the scientific community. It has proven to be a powerful tool, but as with any rapidly developing field, the deluge of information can be overwhelming, confusing and sometimes misleading. This can make it easy to become lost in the same hype cycles that have historically ended in the periods of scarce funding and depleted expectations known as AI Winters. Furthermore, while the importance of innovative, high-risk research cannot be overstated, it is also imperative to understand the fundamental limits of available techniques, especially in young fields where the rules appear to be constantly rewritten and as the likelihood of application to high-stakes scenarios increases. In this perspective, we highlight the guiding principles of data-driven modeling, how these principles imbue models with almost magical predictive power, and how they also impose limitations on the scope of problems they can address. Particularly, understanding when not to use data-driven techniques, such as machine learning, is not something commonly explored, but is just as important as knowing how to apply the techniques properly. We hope that the discussion to follow provides researchers throughout the sciences with a better understanding of when said techniques are appropriate, the pitfalls to watch for, and most importantly, the confidence to leverage the power they can provide.
