Beyond Algorithms: Delivering AI for Business

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Authors
Acknowledgements
PROLOGUE
Chapter 1 Why This Book?
	AI IS EVERYWHERE
	ENTERPRISE APPLICATIONS
	AI WINTERS
	WHAT IS DIFFERENT NOW?
	PROCEED WITH CAUTION!
	DELIVERING AI SOLUTIONS
	BETTER UNDERSTANDING OF AI IS CRITICAL FOR SOCIETY
	TARGET AUDIENCE FOR THE BOOK
	AN OUTLINE OF THE BOOK
	REFERENCES
Chapter 2 Building Applications
	WHAT’S DIFFERENT ABOUT AI WHEN BUILDING AN APPLICATION?
	PROMINENT AI APPLICATIONS OF THE LAST SEVEN DECADES
	AI OR NO AI?
	THE PRESENT – THE DOMINANCE OF THE WEB
	THE FUTURE – THE ENTERPRISE STRIKES BACK
	EXAMPLES OF REAL ENTERPRISE APPLICATIONS
	WHERE DO YOU INTRODUCE AI?
	ACTIVITIES IN CREATING AN AI APPLICATION
	COMPLEXITY OF AI APPLICATIONS
	ARCHITECTURAL AND ENGINEERING CONSIDERATIONS
	THREE STAGES OF AN ENTERPRISE AI APPLICATION
	ENABLING ENTERPRISE SOLUTIONS AT SCALE
	IN SUMMARY – ARE YOU READY TO START BUILDING APPLICATIONS?
	REFERENCES
Chapter 3 It’s Not Just the Algorithms, Really!
	INTRODUCING ALGORITHMS
	ALGORITHMS IN AI
	ALGORITHM ADDICTION
	APPLICATIONS VERSUS THE UNDERLYING TECHNOLOGY
	ALGORITHMS AND MODELS
	OBJECT DROPPING PROBLEM
	UNDERSTANDING THE OBJECT DROPPING DATA
	FOUR MODELS TO PREDICT OBJECT BREAKAGE
	COMPARING THE TWO ML APPROACHES
	COMPARING PHYSICS MODEL WITH ML
	WHAT ARE THE ML ALGORITHMS ACTUALLY LEARNING?
	FEATURE DEFINITION AND EXTRACTION
	REVENGE OF THE ARTIFICIAL NEURAL NETWORKS
	HUMAN INTERPRETATION OF ARTIFICIAL NEURAL NETWORKS
	SO WHICH ALGORITHM IS BEST?
	TRANSFER LEARNING
	REINFORCEMENT LEARNING
	BRAIN VERSUS ARTIFICIAL NEURAL NETWORKS
	FUNDAMENTAL PRINCIPLES AND FUNDAMENTAL MISTAKES
	SO … IT REALLY ISN’T ALL ABOUT THE ALGORITHM
	IN SUMMARY – THERE REALLY IS SO MUCH MORE TO AI THAN THE ALGORITHMS
	REFERENCES
Chapter 4 Know Where to Start – Select the Right Project
	THE DOABILITY METHOD
	INNOVATION AND EMERGING TECHNOLOGIES
	A PORTFOLIO-BASED APPROACH
	DOABILITY METHOD STEP 1 – TO AI OR NOT AI
	THREE RECOMMENDATIONS FROM DOABILITY METHOD STEP 1
	DOABILITY METHOD STEP 1 – WORKED EXAMPLES
	DOABILITY METHOD STEP 2 – PRIORITISING AI PROJECTS IN THE PORTFOLIO
	IN SUMMARY – SUCCESS OR FAILURE WILL DEPEND ON SELECTING THE RIGHT PROJECT
	REFERENCES
Chapter 5 Business Value and Impact
	WHAT IS DIFFERENT ABOUT AI APPLICATIONS?
	BUILDING BUSINESS CASES
	STAKEHOLDERS
	MEASURABILITY AND UNDERSTANDABILITY
	IMPORTANCE OF ETHICS IN AI DEVELOPMENT
	DELIVERING TRUSTWORTHY AI
	FAIRNESS AND BIAS
	EXPLAINABILITY
	TRANSPARENCY
	TACKLING THE WEAKNESS OF ML SYSTEMS
	IN SUMMARY – THERE’S MORE TO VALUE THAN MONETARY RETURN
	REFERENCES
Chapter 6 Ensuring It Works – How Do You Know?
	MANAGING QUALITY OF TRADITIONAL SOFTWARE
	MANAGING QUALITY OF AI APPLICATIONS
	STATISTICAL ACCURACY
	COST FUNCTIONS
	MULTIPLE OUTCOMES
	QUALITY METRICS FOR NATURAL LANGUAGE UNDERSTANDING
	WHAT DOES THIS MEAN IN PRACTICE?
	HOW ACCURATE DOES IT NEED TO BE?
	WHERE DO YOU ASSESS ACCURACY AND BUSINESS IMPACT?
	OPERATING WITHIN LIMITS
	QUALITY ATTRIBUTES OF TRUSTWORTHY AI SYSTEMS
	IN SUMMARY – IF THE AI ISN’T TRUSTWORTHY, PEOPLE WON’T TRUST IT
	REFERENCES
Chapter 7 It’s All about the Data
	DATA TSUNAMI
	DATA TYPES
	DATA SOURCES FOR AI
	DATA FOR THE ENTERPRISE
	ENTERPRISE REALITY
	HUMANS VERSUS AI – LEARNING AND DECISION-MAKING
	DATA WRANGLING
	HOW MUCH DATA DO WE NEED?
	SO, WHAT FEATURES DO WE NEED?
	ENABLING EXPANDING FEATURE SPACES
	WHAT HAPPENS IN THE REAL WORLD?
	COPING WITH MISSING DATA
	USE OF SYNTHETIC DATA
	MANAGING THE DATA WORKFLOW
	IMPROVING DATA QUALITY
	IN SUMMARY – IT REALLY IS ALL ABOUT THE DATA!
	REFERENCES
Chapter 8 How Hard Can It Be?
	DEMONSTRATIONS VERSUS BUSINESS APPLICATIONS
	SETTING EXPECTATIONS … YOURS AND OTHERS!
	DO WE NEED AN INVENTION?
	CURRENT STATE OF AI
	THE IMPORTANCE OF DOMAIN SPECIALISTS
	BUSINESS CHANGE AND AI
	AI IS SOFTWARE
	THE GREAT REUSE CHALLENGE
	THE AI FACTORY
	IN SUMMARY – IT CAN BE AS HARD AS YOU MAKE IT
	REFERENCES
Chapter 9 Getting Your Priorities Right
	AI PROJECT ASSESSMENT CHECKLIST
	USING THE DOABILITY MATRIX
	IN SUMMARY – NEVER TAKE OFF WITHOUT COMPLETING YOUR CHECKLIST
	REFERENCE
Chapter 10 Some (Not So) Boring Stuff
	TRADITIONAL ENGINEERING
	WHY IS ENGINEERING AI DIFFERENT?
	FOUR PHASES OF AN AI PROJECT
	DEVELOPING AN ENTERPRISE AI APPLICATION
	AI MODEL LIFECYCLE
	APPLICATION LIFECYCLE
	APPLICATION INTEGRATION AND DEPLOYMENT
	PROJECT MANAGEMENT
	AUDITABILITY AND EXPLAINABILITY
	SECURITY
	IN SUMMARY – THE BORING STUFF ISN’T REALLY BORING
	REFERENCES
Chapter 11 The Future
	IT’S ALL ABOUT THE DATA – TRENDS IN THE ENTERPRISE
	EFFICIENT COMPUTING FOR AI WORKLOADS – NEW PARADIGMS
	ADVANCES IN ALGORITHMS – TARGETING DATA CHALLENGES AND NEURO-SYMBOLIC AI
	AI ENGINEERING – EMERGENCE OF A NEW DISCIPLINE
	HUMAN–MACHINE TEAMING
	IN SUMMARY – SOME FINAL THOUGHTS
	REFERENCES
EPILOGUE
INDEX