论智能起源中的简约与自洽原则

马毅; 曹颖; 沈向洋

doi:10.1631/FITEE.2200297

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论智能起源中的简约与自洽原则

视觉知识专栏 | Updated：2022-08-22

- 论智能起源中的简约与自洽原则
- On the principles of Parsimony and Self-consistency for the emergence of intelligence
- 信息与电子工程前沿（英文） 2022年23卷第9期页码：1298-1323
- Affiliations：
  
  1.Electrical Engineering and Computer Science Department, University of California, Berkeley, CA 94720, USA
  2.Department of Molecular & Cell Biology and Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA
  3.International Digital Economy Academy, Shenzhen 518045, China
- Author bio：
  
  ‡Corresponding author
  dortsao@berkeley.edu;
  hshum@idea.edu.cn
- Funds：
- DOI：10.1631/FITEE.2200297
  中图分类号： TP18
- 收稿：2022-07-10，
  
  修回：2022-07-29，
  
  录用：2022-07-24，
  
  网络出版：2022-08-12，
  
  纸质出版：2022-09
- Accepted：
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马毅, 曹颖, 沈向洋. 论智能起源中的简约与自洽原则[J]. 信息与电子工程前沿（英文）, 2022,23(9):1298-1323.

Yi MA, Doris TSAO, Heung-Yeung SHUM. On the principles of Parsimony and Self-consistency for the emergence of intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(9): 1298-1323.
马毅, 曹颖, 沈向洋. 论智能起源中的简约与自洽原则[J]. 信息与电子工程前沿（英文）, 2022,23(9):1298-1323. DOI： 10.1631/FITEE.2200297.

Yi MA, Doris TSAO, Heung-Yeung SHUM. On the principles of Parsimony and Self-consistency for the emergence of intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(9): 1298-1323. DOI： 10.1631/FITEE.2200297.

摘要

深度学习重振人工智能十年后的今天，我们提出一个理论框架来帮助理解深度神经网络在整个智能系统里面扮演的角色。我们引入两个基本原则：简约与自洽；分别解释智能系统要学习什么以及如何学习。我们认为这两个原则是人工智能和自然智能之所以产生和发展的基石。虽然这两个原则的雏形早已出现在前人的经典工作里，但是我们对这些原则的重新表述使得它们变得可以精准度量与计算。确切地说，简约与自洽这两个原则能自然地演绎出一个高效计算框架：压缩闭环转录。这个框架统一并解释了现代深度神经网络以及众多人工智能实践的演变和进化。尽管本文主要用视觉数据建模作为例子，我们相信这两个原则将会有助于统一对各种自动智能系统的理解，并且提供一个帮助理解大脑工作机理的框架。

Abstract

Ten years into the revival of deep networks and artificial intelligence

we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of intelligence in general. We introduce two fundamental principles

Parsimony

and

Self-consistency

which address two fundamental questions regarding intelligence: what to learn and how to learn

respectively. We believe the two principles serve as the cornerstone for the emergence of intelligence

artificial or natural. While they have rich classical roots

we argue that they can be stated anew in entirely measurable and computable ways. More specifically

the two principles lead to an effective and efficient computational framework

compressive closed-loop transcription

which unifies and explains the evolution of modern deep networks and most practices of artificial intelligence. While we use mainly visual data modeling as an example

we believe the two principles will unify understanding of broad families of autonomous intelligent systems and provide a framework for understanding the brain.

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