Artificial intelligence, Statistics, Human-machine collaboration," /> Artificial intelligence, Statistics, Human-machine collaboration,"/> Artificial intelligence and statistics
Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2018, Vol. 19 Issue (1): 6-9    
    
Artificial intelligence and statistics
Bin YU, Karl KUMBIER
Department of Statistics, University of California, Berkeley, CA 94720, USA
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
Artificial intelligence and statistics
Bin YU, Karl KUMBIER
Department of Statistics, University of California, Berkeley, CA 94720, USA
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
 全文: PDF 
摘要： 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.
关键词： Artificial intelligence')" href="#">Artificial intelligence;  Statistics;  ')" href="#"> Human-machine collaboration    
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.
Key words: Artificial intelligence    Statistics    Human-machine collaboration
收稿日期: 2017-12-07 出版日期: 2019-06-06
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Bin YU
Karl KUMBIER

引用本文:

Bin YU, Karl KUMBIER. Artificial intelligence and statistics. Front. Inform. Technol. Electron. Eng., 2018, 19(1): 6-9.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2018/V19/I1/6

No related articles found!