Thinking Fast and Slow in AI

被引:0
|
作者
Booch, G. [1 ]
Fabiano, F. [2 ]
Horesh, L. [1 ]
Kate, K. [1 ]
Lenchner, J. [1 ]
Linck, N. [1 ]
Loreggia, A. [3 ]
Murugesan, K. [1 ]
Mattei, N. [4 ]
Rossi, F. [1 ]
Srivastava, B. [5 ]
机构
[1] IBM Corp, Armonk, NY 10504 USA
[2] Univ Udine, Udine, Italy
[3] European Univ Inst, Fiesole, Italy
[4] Tulane Univ, New Orleans, LA 70118 USA
[5] Univ South Carolina, Columbia, SC 29208 USA
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暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.
引用
收藏
页码:15042 / 15046
页数:5
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