Designing Autonomous AI - Helion

ebook

Autor: Kence Anderson
ISBN: 9781098110703
stron: 248, Format: ebook
Data wydania: 2022-06-22
Księgarnia: Helion

Cena książki: 203,15 zł (poprzednio: 236,22 zł)
Oszczędzasz: 14% (-33,07 zł)

Osoby, które kupiły tę książkę, wybierały także »

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

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Spis treści

Designing Autonomous AI eBook -- spis treści

Foreword
Preface
- What Is Autonomous AI?
- Who Should Read This Book?
  - Process Experts
  - Data Scientists and Software Engineers
  - Innovation Leaders
  - Teachers
  - Problem Solvers
- What Can You Expect to Learn from This Book?
- Conventions Used in This Book
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
Introduction: The Right Brain in the Right Place (Why We Need Autonomous AI)
- The Changing World Requires Adapting Skills
  - Problems Need Solutions, Not AI
  - What Can AI Do for Me in Real Life?
  - AI Decision-Making Is Becoming More Autonomous
  - Beware of Data Science Colonialism
- The Changing Workforce Demands Transferred Skills
  - Expertise Is Hard to Acquire
  - Expertise Is Hard to Maintain
  - Expertise Is Simple to Teach, but Requires Practice
- Pressing Problems Demand Completely New Skills
  - AI Is a Tool; Use It for Good
I. When Automation Doesnt Work
1. Sometimes Machines Make Bad Decisions
- Math, Menus, and Manuals: How Machines Make Automated Decisions
  - Control Theory Uses Math to Calculate Decisions
  - Optimization Algorithms Use Menus of Options to Evaluate Decisions
    - Solutions Are like Points on a Map
    - Solving the Game of Checkers
    - Reconnaissance
    - What About Uncertainty?
  - Expert Systems Recall Stored Expertise
2. The Quest for More Human-Like Decision-Making
- Augmenting Human Intelligence
- How Humans Make Decisions and Acquire Skills
  - Humans Act on What They Perceive
  - Humans Build Complex Correlations into Their Intuition with Practice
  - Humans Abstract to Strategy for Complex Tasks
    - Beginner
    - Advanced beginner
    - Competent
    - Proficient
    - Expert
  - Theres a New Kind of AI in Town
    - Reinforcement learning acquires skill through practice
    - Neural networks can correlate any relationship between variables
- The Superpowers of Autonomous AI
  - Autonomous AI Makes More Human-Like Decisions
  - Autonomous AI Perceives, Then Acts
  - The Difference Between Perception and Action in AI
  - Autonomous AI Learns and Adapts When Things Change
  - Autonomous AI Can Spot Patterns
  - Autonomous AI Infers from Experience
  - Autonomous AI Improvises and Strategizes
  - Autonomous AI Can Plan for the Long-Term Future
  - Autonomous AI Brings Together the Best of All Decision-Making Technologies
- When Should You Use Autonomous AI?
- Autonomous AI Is like a Brilliant, Curious Toddler That Needs to Be Taught
II. What Is Machine Teaching?
3. How Brains Learn Best: Teaching Humans and AI
- Learning Multiple Skills Simultaneously Is Hard for Humans and AI
- Teaching Skills and Strategies Explicitly
- Teaching Allows Us to Trust AI
- The Mindset of a Machine Teacher
  - Teacher More Than Programmer
  - Learner More Than Expert
- What Is a Brain Design?
  - How Decision-Making Works
    - Exploring without a map
    - Learning systems navigate better with skills as landmarks than rote instructions
    - So, what is a skill?
    - Why cant you just give me a map?!
    - Skills let your learner learn
  - Acquiring Skill Is like Learning to Navigate by Exploring
    - Zones of proximal development
    - Scaffolding
  - A Brain Design Is a Mental Map That Guides Exploration with Landmarks
4. Building Blocks for Machine Teaching
- Case Study: Learning to Walk Is Hard to Evolve, Easier to Teach
  - So, Why Do We Walk?
  - Strategy Versus Evolution
  - Teaching Walking as Three Skills
    - Defining skills
    - Setting goals for each skill
    - Organizing the skills
- Concepts Capture Knowledge
- Skills Are Specialized Concepts
- Brains Are Built from Skills
  - Building Skills
  - Expert Rules Inflate into Skills
    - Teach expert rules, and let the learner inflate the concepts through practice
  - Perceptive Concepts Discern or Recognize
    - See and hear
    - Predict
    - Detect
    - Classify
    - Filter
  - Directive Concepts Decide and Act
  - Selective Concepts Supervise and Assign
    - Programmed concepts
    - Learned concepts
- Brains Are Organized by Functions and Strategies
  - Sequences or Parallel Execution for Functional Skills
    - Sequences live in the selector
    - Fixed order sequences
    - Parallel execution of functional skills
    - Variable order sequences
  - Hierarchies for Strategies
    - Discovering strategies
    - Strategies wax and wane in effectiveness over time
    - Strategies capture trade-offs
    - Selective concepts navigate strategy hierarchies
- Visual Language of Brain Design
III. How Do You Teach a Machine?
- Understanding the Process
- Meet with Experts
- Ask the Right Questions
- Case Study: Lets Design a Smart Thermostat
5. Teaching Your AI Brain What to Do
- Determining Which Actions the Brain Will Take
  - Perception Is Required, but Its Not All We Need
  - Sequential Decisions
- Triggering the Action in Your AI Brain
- Setting the Decision Frequency
- Handling Delayed Consequences for Brain Actions
- Actions for Smart Thermostat
6. Setting Goals for Your AI Brain
- Theres Always a Trade-off
  - Throughput Versus Efficiency
  - Supervisors Have Different Goals Than Crews Do
  - Dont Prioritize Goals; Balance Them Instead
  - Watch Out for Expert Rules Disguised as Goals
  - Ideal Versus Available
- Setting Goals
  - Step 1: Identify Scenarios
  - Step 2: Match Goals to Scenarios
  - Step 3: Teach Strategies for Each Scenario
- Goal Objectives
  - Maximize
  - Minimize
  - Reach, like the Finish Line for a Race
  - Drive, like the Temperature for a Thermostat
  - Avoid, like Dangerous Conditions
  - Standardize, like the Heat in an Oven
  - Smooth, like a Line
- Expanding Task Algebra to Include Goal Objectives
- Setting Goals for a Smart Thermostat
7. Teaching Skills to Your AI Brain
- Teaching Focuses and Guides Practice (Exploration)
- Skills Can Evolve and Transform
- Skills Adapt to the Scenario
- Levels of Teaching Sophistication
  - The Introductory Teacher Conveys the Facts and Goals
  - The Coach Sequences Skills to Practice
  - The Mentor Teaches Strategy
  - The Maestro Democratizes New Paradigms
- How Maestros Democratize Technology
- Levels of Autonomous AI Architecture
  - Machine Learning Adds Perception
  - Monolithic Brains Are Advanced Beginners
  - Concept Networks Are Competent Learners
  - Massive Concept Networks Are Proficient Learners
- Pursuing Expert Skill Acquisition in Autonomous AI
  - Brains That Come with Hardwired Skills
  - Brains That Define Skills as They Learn
  - Brains That Assemble Themselves
  - Brains with Skills That Coordinate
- Steps to Architect an AI Brain
  - Step 1: Identify the Skills That You Want to Teach
  - Step 2: Orchestrate How the Skills Work Together
  - Step 3: Select Which Technology Should Perform Each Skill
- Pitfalls to Avoid When Teaching Skills
  - Pitfall 1: Confusing the solution for the problem
  - Pitfall 2: Losing the forest for the trees
- Example of Teaching Skills to an AI Brain: Rubber Factory
- Brain Design for Our Smart Thermostat
8. Giving Your AI Brain the Information It Needs to Learn and Decide
- Sensors: The Five Senses for Your AI Brain
  - Variables
  - Proxy Variables
  - Trends
- Simulators: A Gym for Your Autonomous AI to Practice In
  - Simulating Reality Using Physics and Chemistry
  - Simulating Reality Using Statistics and Events
  - Simulating Reality Using Machine Learning
  - Simulating Reality Using Expert Rules
- Sensor Variables for Smart Thermostat
IV. Tools for the Machine Teacher
9. Designing AI Brains That Someone Can Actually Build
- Designers and Builders Working Together in Harmony (Mostly)
  - The Autonomous AI Design Fallacy Designs but Wont Iterate
  - The Autonomous AI Implementation Fallacy Skips Design Altogether
- Specification for Documenting AI Brain Designs
- Platform for Machine Teaching
- Platform for Wiring Multiple Skills Together as Modules
  - Platform for mixing math, menus, and manuals with AI
- What Difference Will You Make with Machine Teaching?
Glossary
Index