Designing Autonomous AI: A Guide for Machine Teaching

Early rules-based AI lacked perception and learning capabilities, but modern AI with machine learning and deep reinforcement learning can perform superhuman decision-making for specific tasks. This book combines early AI with deep learning and industrial control technologies to make robust decisions in the real world.

Format: Paperback / softback
Length: 250 pages
Publication date: 24 June 2022
Publisher: O'Reilly Media

Early rules-based artificial intelligence exhibited intriguing decision-making capabilities, yet it lacked perception and failed to learn. Today's AI, equipped with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book aims to bridge the practicality of early AI with deep learning capabilities and industrial control technologies to enable robust decision-making in the real world. Author Kence Anderson, through concrete examples, minimal theory, and a proven architectural framework, demonstrates how to teach autonomous AI explicit skills and strategies. Readers will learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. This book is valuable for students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes. It examines the differences between automated, autonomous, and human decision-making, highlights the unique advantages of autonomous AI for real-time decision-making, and provides a step-by-step guide to designing an autonomous AI from modular components. The book also includes documentation of design decisions to ensure transparency and maintainability. By leveraging the methodology outlined in this book, readers can unlock the full potential of autonomous AI and make informed decisions that drive success in their industries.

Weight: 434g
Dimension: 177 x 232 x 16 (mm)
ISBN-13: 9781098110758