book

Designing Autonomous AI

Name: Designing Autonomous AI
Author: Kence Anderson
ISBN: 9781098110758

by Kence Anderson

June 2022

Intermediate to advanced

245 pages

6h 55m

English

O'Reilly Media, Inc.

Read now

Unlock full access

Foreword
Preface
What Is Autonomous AI?Who Should Read This Book?Process ExpertsData Scientists and Software EngineersInnovation LeadersTeachersProblem SolversWhat Can You Expect to Learn from This Book?Conventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
Introduction: The Right Brain in the Right Place (Why We Need Autonomous AI)
The Changing World Requires Adapting SkillsProblems Need Solutions, Not AIWhat Can AI Do for Me in Real Life?AI Decision-Making Is Becoming More AutonomousBeware of Data Science ColonialismThe Changing Workforce Demands Transferred SkillsExpertise Is Hard to AcquireExpertise Is Hard to MaintainExpertise Is Simple to Teach, but Requires PracticePressing Problems Demand Completely New SkillsAI Is a Tool; Use It for Good
I. When Automation Doesn’t Work
1. Sometimes Machines Make Bad Decisions
Math, Menus, and Manuals: How Machines Make Automated DecisionsControl Theory Uses Math to Calculate DecisionsOptimization Algorithms Use Menus of Options to Evaluate DecisionsExpert Systems Recall Stored Expertise
2. The Quest for More Human-Like Decision-Making
Augmenting Human IntelligenceHow Humans Make Decisions and Acquire SkillsHumans Act on What They PerceiveHumans Build Complex Correlations into Their Intuition with PracticeHumans Abstract to Strategy for Complex TasksThere’s a New Kind of AI in TownThe Superpowers of Autonomous AIAutonomous AI Makes More Human-Like DecisionsAutonomous AI Perceives, Then ActsThe Difference Between Perception and Action in AIAutonomous AI Learns and Adapts When Things ChangeAutonomous AI Can Spot PatternsAutonomous AI Infers from ExperienceAutonomous AI Improvises and StrategizesAutonomous AI Can Plan for the Long-Term FutureAutonomous AI Brings Together the Best of All Decision-Making TechnologiesWhen 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 AITeaching Skills and Strategies ExplicitlyTeaching Allows Us to Trust AIThe Mindset of a Machine TeacherTeacher More Than ProgrammerLearner More Than ExpertWhat Is a Brain Design?How Decision-Making WorksAcquiring Skill Is like Learning to Navigate by ExploringA 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 TeachSo, Why Do We Walk?Strategy Versus EvolutionTeaching Walking as Three SkillsConcepts Capture KnowledgeSkills Are Specialized ConceptsBrains Are Built from SkillsBuilding SkillsExpert Rules Inflate into SkillsPerceptive Concepts Discern or RecognizeDirective Concepts Decide and ActSelective Concepts Supervise and AssignBrains Are Organized by Functions and StrategiesSequences or Parallel Execution for Functional SkillsHierarchies for StrategiesVisual Language of Brain Design
III. How Do You Teach a Machine?
Understanding the ProcessMeet with ExpertsAsk the Right QuestionsCase Study: Let’s Design a Smart Thermostat

5. Teaching Your AI Brain What to Do
Determining Which Actions the Brain Will TakePerception Is Required, but It’s Not All We NeedSequential DecisionsTriggering the Action in Your AI BrainSetting the Decision FrequencyHandling Delayed Consequences for Brain ActionsActions for Smart Thermostat
6. Setting Goals for Your AI Brain
There’s Always a Trade-offThroughput Versus EfficiencySupervisors Have Different Goals Than Crews DoDon’t Prioritize Goals; Balance Them InsteadWatch Out for Expert Rules Disguised as GoalsIdeal Versus AvailableSetting GoalsStep 1: Identify ScenariosStep 2: Match Goals to ScenariosStep 3: Teach Strategies for Each ScenarioGoal ObjectivesMaximizeMinimizeReach, like the Finish Line for a RaceDrive, like the Temperature for a ThermostatAvoid, like Dangerous ConditionsStandardize, like the Heat in an OvenSmooth, like a LineExpanding Task Algebra to Include Goal ObjectivesSetting Goals for a Smart Thermostat
7. Teaching Skills to Your AI Brain
Teaching Focuses and Guides Practice (Exploration)Skills Can Evolve and TransformSkills Adapt to the ScenarioLevels of Teaching SophisticationThe Introductory Teacher Conveys the Facts and GoalsThe Coach Sequences Skills to PracticeThe Mentor Teaches StrategyThe Maestro Democratizes New ParadigmsHow Maestros Democratize TechnologyLevels of Autonomous AI ArchitectureMachine Learning Adds PerceptionMonolithic Brains Are Advanced BeginnersConcept Networks Are Competent LearnersMassive Concept Networks Are Proficient LearnersPursuing Expert Skill Acquisition in Autonomous AIBrains That Come with Hardwired SkillsBrains That Define Skills as They LearnBrains That Assemble ThemselvesBrains with Skills That CoordinateSteps to Architect an AI BrainStep 1: Identify the Skills That You Want to TeachStep 2: Orchestrate How the Skills Work TogetherStep 3: Select Which Technology Should Perform Each SkillPitfalls to Avoid When Teaching SkillsPitfall 1: Confusing the solution for the problemPitfall 2: Losing the forest for the treesExample of Teaching Skills to an AI Brain: Rubber FactoryBrain 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 BrainVariablesProxy VariablesTrendsSimulators: A Gym for Your Autonomous AI to Practice InSimulating Reality Using Physics and ChemistrySimulating Reality Using Statistics and EventsSimulating Reality Using Machine LearningSimulating Reality Using Expert RulesSensor 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 Won’t IterateThe Autonomous AI Implementation Fallacy Skips Design AltogetherSpecification for Documenting AI Brain DesignsPlatform for Machine TeachingPlatform for Wiring Multiple Skills Together as ModulesWhat Difference Will You Make with Machine Teaching?
Glossary
Index
About the Author

Content preview from Designing Autonomous AI

Glossary

artificial intelligence: Computer systems that can perform tasks that normally require human intelligence.
automation: Intelligence applied to industrial tasks, usually using mathematical calculations, optimization, and rule systems.
autonomous AI: AI-powered automation that optimizes equipment and processes by sensing its environment and responding in real time to maximize the chance of achieving its goals.
machine teaching: A framework for using human teaching methods to help machines learn concepts and acquire skills.
expert rule: Direction for acting based on previous experience.
concept: A notion or idea that comprises a composable unit of knowledge.
skill: A unit of competence for a specific task that provides guidance for practicing, exploring, and successfully completing that task.
scenario: A segment of an environment that helps decision-makers organize a vast decision-making space.
strategy: A labeled course of action for completing a task.
heuristic strategy: A strategy acquired by previous experience.
goal: A high-level specification of what you want the AI to learn; the goal represents success at the task.
objective: One of the components of a goal.
simulation: A virtual environment where an autonomous AI can practice; the AI accepts an action and responds by predicting what state of the environment will result from the action.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781098110741Errata Page

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Designing Autonomous AI

by Kence Anderson

Glossary

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.