How much information should we drop to become intelligent?

被引：0

作者：

Nave, Ophir ^{[1
]}

Neuman, Yair ^{[2
]}

Perlovsky, Leonid ^{[3
]}

Howard, Newton ^{[4
]}

机构：

[1] Ben Gurion Univ Negev, Dept Math, Lev Acad Ctr JCT, IL-84105 Beer Sheva, Israel

[2] Ben Gurion Univ Negev, Dept Educ, IL-84105 Beer Sheva, Israel

[3] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA

[4] MIT, Synthet Intelligence Lab, Cambridge, MA 02139 USA

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 245卷

关键词：

Cognition and physics; Set partition; Entropy; Interdisciplinary research;

D O I：

10.1016/j.amc.2014.07.029

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Cognitive processing by intelligent systems involves the deletion of information in favor of higher level abstractions. This process can be addressed through the physics of computation but a formal model that explains this process has not been proposed yet. In this short paper, we propose a model that through physical constraints only generates optimal solution to the collapse of n objects into n sets. A numerical simulation of the model results in a logarithmic function of information loss and condensation that perfectly fits our knowledge of cognitive processes. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：261 / 264

页数：4

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