CLC number: TP391; C8
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-01-28
Cited: 0
Clicked: 7962
Bin Yu, Karl Kumbier. Artificial intelligence and statistics[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 6-9.
@article{title="Artificial intelligence and statistics",
author="Bin Yu, Karl Kumbier",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="1",
pages="6-9",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700813"
}
%0 Journal Article
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%A Bin Yu
%A Karl Kumbier
%J Frontiers of Information Technology & Electronic Engineering
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%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700813
TY - JOUR
T1 - Artificial intelligence and statistics
A1 - Bin Yu
A1 - Karl Kumbier
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700813
Abstract: 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.
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