Enterprise Artificial Intelligence Transformation

Cover
Title Page
Copyright Page
Contents
Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks
Prologue: A Guide to This Book
PART I A Brief Introduction to Artificial Intelligence
	Chapter 1 A Revolution in the Making
		The Impact of the Four Revolutions
		AI Myths and Reality
		The Data and Algorithms Virtuous Cycle
		The Ongoing Revolution – Why Now?
		AI: Your Competitive Advantage
	Chapter 2 What Is AI and How Does It Work?
		The Development of Narrow AI
		The First Neural Network
		Machine Learning
			Types of Uses for Machine Learning
			Types of Machine Learning Algorithms
		Supervised, Unsupervised, and Semisupervised Learning
		Making Data More Useful
		Semantic Reasoning
		Applications of AI
PART II Artificial Intelligence in the Enterprise
	Chapter 3 AI in E-Commerce and Retail
		Digital Advertising
		Marketing and Customer Acquisition
		Cross-Selling, Up-Selling, and Loyalty
		Business-to-Business Customer Intelligence
		Dynamic Pricing and Supply Chain Optimization
		Digital Assistants and Customer Engagement
	Chapter 4 AI in Financial Services
		Anti-Money Laundering
		Loans and Credit Risk
		Predictive Services and Advice
		Algorithmic and Autonomous Trading
		Investment Research and Market Insights
		Automated Business Operations
	Chapter 5 AI in Manufacturing and Energy
		Optimized Plant Operations and Assets Maintenance
		Automated Production Lifecycles
		Supply Chain Optimization
		Inventory Management and Distribution Logistics
		Electric Power Forecasting and Demand Response
		Oil Production
		Energy Trading
	Chapter 6 AI in Healthcare
		Pharmaceutical Drug Discovery
		Clinical Trials
		Disease Diagnosis
		Preparation for Palliative Care
		Hospital Care
PART III Building Your Enterprise AI Capability
	Chapter 7 Developing an AI Strategy
		Goals of Connected Intelligence Systems
		The Challenges of Implementing AI
		AI Strategy Components
		Steps to Develop an AI Strategy
		Some Assembly Required
			Creating an AI Center of Excellence
			Building an AI Platform
			Defining a Data Strategy
		Moving Ahead
	Chapter 8 The AI Lifecycle
		Defining Use Cases
		Collecting, Assessing, and Remediating Data
			Data Instrumentation
			Data Cleansing
			Data Labeling
		Feature Engineering
		Selecting and Training a Model
		Managing Models
		Testing, Deploying, and Activating Models
			Testing
			Governing Model Risk
			Deploying the Model
			Activating the Model
			Production Monitoring
		Conclusion
	Chapter 9 Building the Perfect AI Engine
		AI Platforms versus AI Applications
		What AI Platform Architectures Should Do
		Some Important Considerations
			Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two?
			Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace?
			Should a Business Use Batch or Real-Time Processing?
			Should a Business Use Monolithic or Microservices Architecture?
		AI Platform Architecture
			Data Minder
			Model Maker
			Inference Activator
			Performance Manager
	Chapter 10 Managing Model Risk
		When Algorithms Go Wrong
		Mitigating Model Risk
			Before Modeling
			During Modeling
			After Modeling
		Model Risk Office
	Chapter 11 Activating Organizational Capability
		Aligning Stakeholders
		Organizing for Scale
		AI Center of Excellence
			Standards and Project Governance
			Community, Knowledge, and Training
			Platform and AI Ecosystem
		Structuring Teams for Project Execution
		Managing Talent and Hiring
		Data Literacy, Experimentation, and Data-Driven Decisions
		Conclusion
PART IV Delving Deeper into AI Architecture and Modeling
	Chapter 12 Architecture and Technical Patterns
		AI Platform Architecture
			Data Minder
			Model Maker
			Inference Activator
			Performance Manager
		Technical Patterns
			Intelligent Virtual Assistant
			Personalization and Recommendation Engines
			Anomaly Detection
			Ambient Sensing and Physical Control
			Digital Workforce
		Conclusion
	Chapter 13 The AI Modeling Process
		Defining the Use Case and the AI Task
		Selecting the Data Needed
		Setting Up the Notebook Environment and Importing Data
		Cleaning and Preparing the Data
		Understanding the Data Using Exploratory Data Analysis
		Feature Engineering
		Creating and Selecting the Optimal Model
PART V Looking Ahead
	Chapter 14 The Future of Society, Work, and AI
		AI and the Future of Society
		AI and the Future of Work
		Regulating Data and Artificial Intelligence
		The Future of AI: Improving AI Technology
			Reinforcement Learning
			Generative Adversarial Learning
			Federated Learning
			Natural Language Processing
			Capsule Networks
			Quantum Machine Learning
		And This Is Just the Beginning
Further Reading
Acknowledgments
About the Author
Index
EULA