Introduction

Since I began studying artificial intelligence in 2019, I have developed a strong interest in the mechanisms of AI and the meaning and implications of intelligence. This interest encompasses not only the engineering development of computer AI and the knowledge related to program execution, but also an understanding of the relationship between individuals and collectives in the construction mechanisms of AI networks, human cognition, social structures, and organizational evolution.

In the process of constructing a complex organization from a single neuron, we can observe intelligence gradually becoming richer and deeper, with the logic and form of this process being remarkably intricate. While learning, developing, and building a knowledge map related to AI, I began to rethink the origins of human perception and cognition and to explore the relationship between the evolution of our society and organizations and intelligence.

Reflecting on some phenomena and subsequent insights from investment decision-making and business operations, I vaguely sensed a critical relationship between organizational development and intelligence. When I decided to include this chapter in the book, I was firmly convinced that understanding intelligence would play a pivotal role in the fields of enterprise development and organizational restructuring, which I am deeply invested in.

Considering that the framework of Intelligenism I propose is built by referencing the architecture of deep learning networks and drawing on the characteristics of the relationship between individuals and collectives in deep learning networks to restructure or transform organizational frameworks, it is necessary to briefly introduce the history of machine intelligence and the operating mechanisms of deep learning in this chapter. This will help readers unfamiliar with the mechanisms of machine learning and deep learning better comprehend and understand the book’s content.

In introducing the mechanisms of deep learning, I will avoid all formulaic derivations and computational processes as much as possible, as the application of deep learning in computer engineering is not the focus of this book, and such computations are not directly applicable to constructing human social organizations. For readers interested in the application of deep learning in computer engineering, I recommend consulting professional texts on machine learning and deep learning for further study.