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, that we believe to be cornerstones for the emergence of Intelligence, artificial or natural. While these two principles 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, that unifies and explains the evolution of modern deep networks and many artificial intelligence practices. While we mainly use modeling of visual data 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.

提出了“简约性”和“自洽性”两个基本原则，并采用可测量和可计算的方式表述，通过有效高效的计算框架“压缩闭环转录”实现模型的视觉数据建模。这将统一理解自主智能系统的广泛家族，并为理解大脑提供框架。

关于简约和自洽原则在智能出现中的应用