General Intelligence (Paperback)

Robert D. Johnson

ISBN 13: 9798998599521

Published by Robert Johnson, 2025

About this Item

Paperback. In General Intelligence: Hierarchical Prioritized Neural Nodal Learning (HPNNL), decades of multidisciplinary research converge into one groundbreaking theory that redefines how artificial intelligence can evolve. Inspired by cognitive neuroscience, emotional processing, and human learning behavior, HPNNL introduces a dynamic, self-organizing framework where memories are weighted by priority, nodes are connected through meaningful association, and the system reorganizes itself in real time based on contextual understanding.From its foundational principles to its potential applications in education, healthcare, robotics, and affective computing, this book explores how HPNNL bridges the gap between biological and artificial learning. It tackles real-world challenges-such as catastrophic forgetting, emotional bias, and knowledge transfer-and presents a vision for future AI that is adaptive, intuitive, and human-aligned.Whether you're an AI researcher, neuroscientist, software engineer, or simply fascinated by the human cognition and intelligence, this book offers a rich, readable, and forward-thinking exploration of what's next. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798998599521

Bibliographic Details

• Title: General Intelligence (Paperback)
• Publisher: Robert Johnson
• Publication Date: 2025
• Language: English
• ISBN 13: 9798998599521
• Binding: Paperback
• Condition: new

About this title

Synopsis

In General Intelligence: Hierarchical Prioritized Neural Nodal Learning (HPNNL), decades of multidisciplinary research converge into one groundbreaking theory that redefines how artificial intelligence can evolve. Inspired by cognitive neuroscience, emotional processing, and human learning behavior, HPNNL introduces a dynamic, self-organizing framework where memories are weighted by priority, nodes are connected through meaningful association, and the system reorganizes itself in real time based on contextual understanding.

From its foundational principles to its potential applications in education, healthcare, robotics, and affective computing, this book explores how HPNNL bridges the gap between biological and artificial learning. It tackles real-world challenges—such as catastrophic forgetting, emotional bias, and knowledge transfer—and presents a vision for future AI that is adaptive, intuitive, and human-aligned.

Whether you're an AI researcher, neuroscientist, software engineer, or simply fascinated by the human cognition and intelligence, this book offers a rich, readable, and forward-thinking exploration of what’s next.