Designing autonomous AI: a guide for machine teaching
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 …
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- Full title :Designing autonomous AI : a guide for machine teaching / Kence Anderson.
Summary
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.
Read More ↓Edition Statements
- Publication Date:©2022
- Edition:First edition
Publication Statements
- Publisher(s):O'Reilly Media
- Place of Publication:Sebastopol, CA
- Language:eng
- Publication Date:©2022
Notes
Includes index.
The Physical Object
- Description:xl, 204 pages : : illustrations ; : 24 cm
Edition Identifiers
- ISBN:1098110757, 9781098110758
- oclc:on1286147194
- LC Classification:Q335
- Dewey Decimal:006.3
Table of Contents
Part 1. When automation doesn't work. Sometimes machines make bad decisions -- The quest for more human-like decision-making -- Part 2. What is machine teaching? How brains learn best: teaching humans and AI -- Building blocks for machine teaching -- Part 3. How do you teach a machine? Teaching your AI brain what to do -- Setting goals for your AI brain -- Teaching skills to your AI brain -- Giving your AI brain the information it needs to learn and decide -- Part 4. Tools for the machine teacher. Designing AI brains that someone can actually build.
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