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Frontiers of Information Technology & Electronic Engineering >> 2022, Volume 23, Issue 9 doi: 10.1631/FITEE.2200297

On the principles of Parsimony and Self-consistency for the emergence of intelligence

Affiliation(s): Electrical Engineering and Computer Science Department, University of California, Berkeley, CA 94720, USA; Department of Molecular & Cell Biology and Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA; International Digital Economy Academy, Shenzhen 518045, China; less

Received: 2022-07-10 Accepted: 2022-09-21 Available online: 2022-09-21

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Abstract

Ten years into the revival of and artificial , we propose a theoretical framework that sheds light on understanding within a bigger picture of in general. We introduce two fundamental principles, and , which address two fundamental questions regarding : what to learn and how to learn, respectively. We believe the two principles serve as the cornerstone for the emergence of , 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 , which unifies and explains the evolution of modern and most practices of artificial . 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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