Artificial intelligence and statistics
Artificial intelligence and statistics
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DOI：
10.1631/fitee.1700813
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发表时间：
2018-01-01
影响因子：
3
通讯作者：
Kumbier, Karl
中科院分区：
文献类型：
--
作者：
Yu, Bin;Kumbier, Karl
人工智能（AI）本质上是数据驱动的。它要求在数据生成、算法开发和结果评估过程中通过人机协作应用统计概念。本文讨论了如何通过人口、兴趣问题、训练数据的代表性和结果审查（PQRS）的统计概念来实现这种人机协作。PQRS工作流为将统计思想与人工输入集成到人工智能产品和研究中提供了一个概念框架。这些思想包括随机化和局部控制的实验设计原则，以及稳定性原则，以获得算法和数据结果的可重复性和可解释性。我们讨论了这些原则在自动驾驶汽车、自动医疗诊断以及作者合作研究中的例子中的应用。
Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors' collaborative research.