
Sách keo gáy, bìa mềm
Early rules-based artificial intelligence
demonstrated intriguing decision-making capabilities but lacked
perception and didn't learn. AI today, primed with machine learning
perception and deep reinforcement learning capabilities, can perform
superhuman decision-making for specific tasks. This book shows you how
to combine the practicality of early AI with deep learning capabilities
and industrial control technologies to make robust decisions in the real
world.
Using concrete examples, minimal theory, and a proven
architectural framework, author Kence Anderson demonstrates how to teach
autonomous AI explicit skills and strategies. You'll 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. Students, process operators, data
scientists, machine learning algorithm experts, and engineers who own
and manage industrial processes can use the methodology in this book to
design autonomous AI.
This book examines:
• Differences between and limitations of automated, autonomous, and human decision-making
• Unique advantages of autonomous AI for real-time decision-making, with use cases
• How to design an autonomous AI from modular components and document your designs
Thể loại:Computers - Cybernetics
Năm:2022
In lần thứ:1
Ngôn ngữ:english
Trang:248