,Table of Contents
1. Cover
2. Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks
3. Prologue: A Guide to This Book
4. Part I: A Brief Introduction to Artificial Intelligence
1. Chapter 1: A Revolution in the Making
1. The Impact of the Four Revolutions
2. AI Myths and Reality
3. The Data and Algorithms Virtuous Cycle
4. The Ongoing Revolution – Why Now?
5. AI: Your Competitive Advantage
6. Notes
2. Chapter 2: What Is AI and How Does It Work?
1. The Development of Narrow AI
2. The First Neural Network
3. Machine Learning
4. Supervised, Unsupervised, and Semisupervised Learning
5. Making Data More Useful
6. Semantic Reasoning
7. Applications of AI
8. Notes
5. Part II: Artificial Intelligence in the Enterprise
1. Chapter 3: AI in E-Commerce and Retail
1. Digital Advertising
2. Marketing and Customer Acquisition
3. Cross-Selling, Up-Selling, and Loyalty
4. Business-to-Business Customer Intelligence
5. Dynamic Pricing and Supply Chain Optimization
6. Digital Assistants and Customer Engagement
7. Notes
2. Chapter 4: AI in Financial Services
1. Anti-Money Laundering
2. Loans and Credit Risk
3. Predictive Services and Advice
4. Algorithmic and Autonomous Trading
5. Investment Research and Market Insights
6. Automated Business Operations
7. Notes
3. Chapter 5: AI in Manufacturing and Energy
1. Optimized Plant Operations and Assets Maintenance
2. Automated Production Lifecycles
3. Supply Chain Optimization
4. Inventory Management and Distribution Logistics
5. Electric Power Forecasting and Demand Response
, 6. Oil Production
7. Energy Trading
8. Notes
4. Chapter 6: AI in Healthcare
1. Pharmaceutical Drug Discovery
2. Clinical Trials
3. Disease Diagnosis
4. Preparation for Palliative Care
5. Hospital Care
6. Notes
6. Part III: Building Your Enterprise AI Capability
1. Chapter 7: Developing an AI Strategy
1. Goals of Connected Intelligence Systems
2. The Challenges of Implementing AI
3. AI Strategy Components
4. Steps to Develop an AI Strategy
5. Some Assembly Required
6. Moving Ahead
7. Notes
2. Chapter 8: The AI Lifecycle
1. Defining Use Cases
2. Collecting, Assessing, and Remediating Data
3. Feature Engineering
4. Selecting and Training a Model
5. Managing Models
6. Testing, Deploying, and Activating Models
7. Conclusion
3. Chapter 9: Building the Perfect AI Engine
1. AI Platforms versus AI Applications
2. What AI Platform Architectures Should Do
3. Some Important Considerations
4. AI Platform Architecture
5. Notes
4. Chapter 10: Managing Model Risk
1. When Algorithms Go Wrong
2. Mitigating Model Risk
3. Model Risk Office
4. Notes
5. Chapter 11: Activating Organizational Capability
1. Aligning Stakeholders
2. Organizing for Scale
3. AI Center of Excellence
4. Structuring Teams for Project Execution
5. Managing Talent and Hiring
6. Data Literacy, Experimentation, and Data-Driven Decisions
7. Conclusion
, 8. Notes
7. Part IV: Delving Deeper into AI Architecture and Modeling
1. Chapter 12: Architecture and Technical Patterns
1. AI Platform Architecture
2. Technical Patterns
3. Conclusion
2. Chapter 13: The AI Modeling Process
1. Defining the Use Case and the AI Task
2. Selecting the Data Needed
3. Setting Up the Notebook Environment and Importing Data
4. Cleaning and Preparing the Data
5. Understanding the Data Using Exploratory Data Analysis
6. Feature Engineering
7. Creating and Selecting the Optimal Model
8. Note
8. Part V: Looking Ahead
1. Chapter 14: The Future of Society, Work, and AI
1. AI and the Future of Society
2. AI and the Future of Work
3. Regulating Data and Artificial Intelligence
4. The Future of AI: Improving AI Technology
5. And This Is Just the Beginning
6. Notes
9. Further Reading
1. General
2. Society
3. Work
10. Acknowledgments
11. About the Author
12. Index
13. End User License Agreement
List of Illustrations
1. Chapter 2
1. Figure 2.1 Examples of functions f(x) that can be estimated by using machine...
2. Figure 2.2 Using training data for customers 1 to m to estimate f that will ...
3. Figure 2.3 Using the machine-learning model (f) to predict if customer numbe...
4. Figure 2.4 An example of a deep neural network.
5. Figure 2.5 Example of a type of knowledge graph.
6. Figure 2.6 Types of AI systems.
2. Chapter 5
1. Figure 5.1 Heuristic showing different failure rates during equipment compon...
2. Figure 5.2 Demand forecasting using historical sales and new data sources.
3. Figure 5.3 Energy trading scenario.
3. Chapter 7
1. Cover
2. Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks
3. Prologue: A Guide to This Book
4. Part I: A Brief Introduction to Artificial Intelligence
1. Chapter 1: A Revolution in the Making
1. The Impact of the Four Revolutions
2. AI Myths and Reality
3. The Data and Algorithms Virtuous Cycle
4. The Ongoing Revolution – Why Now?
5. AI: Your Competitive Advantage
6. Notes
2. Chapter 2: What Is AI and How Does It Work?
1. The Development of Narrow AI
2. The First Neural Network
3. Machine Learning
4. Supervised, Unsupervised, and Semisupervised Learning
5. Making Data More Useful
6. Semantic Reasoning
7. Applications of AI
8. Notes
5. Part II: Artificial Intelligence in the Enterprise
1. Chapter 3: AI in E-Commerce and Retail
1. Digital Advertising
2. Marketing and Customer Acquisition
3. Cross-Selling, Up-Selling, and Loyalty
4. Business-to-Business Customer Intelligence
5. Dynamic Pricing and Supply Chain Optimization
6. Digital Assistants and Customer Engagement
7. Notes
2. Chapter 4: AI in Financial Services
1. Anti-Money Laundering
2. Loans and Credit Risk
3. Predictive Services and Advice
4. Algorithmic and Autonomous Trading
5. Investment Research and Market Insights
6. Automated Business Operations
7. Notes
3. Chapter 5: AI in Manufacturing and Energy
1. Optimized Plant Operations and Assets Maintenance
2. Automated Production Lifecycles
3. Supply Chain Optimization
4. Inventory Management and Distribution Logistics
5. Electric Power Forecasting and Demand Response
, 6. Oil Production
7. Energy Trading
8. Notes
4. Chapter 6: AI in Healthcare
1. Pharmaceutical Drug Discovery
2. Clinical Trials
3. Disease Diagnosis
4. Preparation for Palliative Care
5. Hospital Care
6. Notes
6. Part III: Building Your Enterprise AI Capability
1. Chapter 7: Developing an AI Strategy
1. Goals of Connected Intelligence Systems
2. The Challenges of Implementing AI
3. AI Strategy Components
4. Steps to Develop an AI Strategy
5. Some Assembly Required
6. Moving Ahead
7. Notes
2. Chapter 8: The AI Lifecycle
1. Defining Use Cases
2. Collecting, Assessing, and Remediating Data
3. Feature Engineering
4. Selecting and Training a Model
5. Managing Models
6. Testing, Deploying, and Activating Models
7. Conclusion
3. Chapter 9: Building the Perfect AI Engine
1. AI Platforms versus AI Applications
2. What AI Platform Architectures Should Do
3. Some Important Considerations
4. AI Platform Architecture
5. Notes
4. Chapter 10: Managing Model Risk
1. When Algorithms Go Wrong
2. Mitigating Model Risk
3. Model Risk Office
4. Notes
5. Chapter 11: Activating Organizational Capability
1. Aligning Stakeholders
2. Organizing for Scale
3. AI Center of Excellence
4. Structuring Teams for Project Execution
5. Managing Talent and Hiring
6. Data Literacy, Experimentation, and Data-Driven Decisions
7. Conclusion
, 8. Notes
7. Part IV: Delving Deeper into AI Architecture and Modeling
1. Chapter 12: Architecture and Technical Patterns
1. AI Platform Architecture
2. Technical Patterns
3. Conclusion
2. Chapter 13: The AI Modeling Process
1. Defining the Use Case and the AI Task
2. Selecting the Data Needed
3. Setting Up the Notebook Environment and Importing Data
4. Cleaning and Preparing the Data
5. Understanding the Data Using Exploratory Data Analysis
6. Feature Engineering
7. Creating and Selecting the Optimal Model
8. Note
8. Part V: Looking Ahead
1. Chapter 14: The Future of Society, Work, and AI
1. AI and the Future of Society
2. AI and the Future of Work
3. Regulating Data and Artificial Intelligence
4. The Future of AI: Improving AI Technology
5. And This Is Just the Beginning
6. Notes
9. Further Reading
1. General
2. Society
3. Work
10. Acknowledgments
11. About the Author
12. Index
13. End User License Agreement
List of Illustrations
1. Chapter 2
1. Figure 2.1 Examples of functions f(x) that can be estimated by using machine...
2. Figure 2.2 Using training data for customers 1 to m to estimate f that will ...
3. Figure 2.3 Using the machine-learning model (f) to predict if customer numbe...
4. Figure 2.4 An example of a deep neural network.
5. Figure 2.5 Example of a type of knowledge graph.
6. Figure 2.6 Types of AI systems.
2. Chapter 5
1. Figure 5.1 Heuristic showing different failure rates during equipment compon...
2. Figure 5.2 Demand forecasting using historical sales and new data sources.
3. Figure 5.3 Energy trading scenario.
3. Chapter 7
