Jung-Chun Liu 劉容均

Introduction

Before delving into this book, it's essential to understand the era in which it was published. Released in 2001, two decades ago, the field of Artificial Intelligence was a vibrant arena of diverse methodologies. Common approaches of that time included Bayesian inference, genetic algorithms, and symbolic theory. Neural networks, due to limited computational power, experienced a period of relative obscurity. The neural networks discussed in this book are traditional in nature, focusing on their architecture and the significance of inter-node connections. Nevertheless, the author introduces neural networks from a connectionist perspective, asserting that this structure mirrors human mental representation and thought processes, somewhat anticipating the potential of neural networks.

In the past decade, research on neural networks has come to dominate the field of Artificial Intelligence, with neural networks nearly synonymous with AI. Over these twenty years, continuous new studies have emerged, technology has evolved rapidly, and the world and human society have undergone dramatic changes. Therefore, this report will also extend and explore the current technological and societal developments.

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