Enterprise artificial intelligence transformation: a playbook for the next generation of business and technology leaders
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
[2020]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | xxiv, 342 Seiten Illustrationen |
| ISBN: | 9781119665939 |
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Datensatz im Suchindex
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Contents Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks Prologue: À Guide to This Book PARTI: xv xxi A BRIEF INTRODUCTION TO ARTIFICIAL INTELLIGENCE . 1 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 ix 3 4 6 7 8 13 · , 17 18 20
x CONTENTS Machine Learning Types of Uses for Machine Learning Types of Machine Learning Algorithms Supervised, Unsupervised, and Semisuperviscd Learning Making Data More Useful Semantic Reasoning Applications ofAI PART II: ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE 20 23 24 28 32 34 40 43 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 45 46 48 52 55 57 59 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 67 68 71 72 75 77 81 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 85 88 91 91 93 94 96 99 Chapter 6: AI in Healthcare Pharmaceutical Drug Discovery Clinical Trials 103 104 105
Contents xi Disease Diagnosis Preparation for Palliative Care Hospital Care 106 109 Ш PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117 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 119 120 122 126 127 129 130 131 132 134 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 137 Chapter 9: Building the Perfect AI Engine AI Platforms versus AĪ Applications What AI Platform Architectures Should Do ՜ , 138 143 144 145 146 148 151 160 164 164 165 166 166 168 169 171 172 172
xii CONTENTS 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 180 182 1S4 186 186 187 188 190 Chapter 10: Managing Model Risk When Algorithms Go Wrong Mitigating Model Risk Before Modeling During Modeling After Modeling Model Risk Office 193 195 197 197 199 201 209 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 213 214 215 217 218 PART IV: DELVING DEEPER INTO AI ARCHITECTURE AND MODELING Chapter 12: Architecture and Technical Patterns AI Platform Architecture Data Minder Model Maker 1'° 179 220 221 222 225 228 230 233 235 236 236 239
Contents Inference Activator Performance Manager Technical Patterns . Intelligent Virtual Assistant Personalization and Recommendation Engines Anomaly Detection Ambient Sensing and Physical Control Digital Workforce Conclusion xiii 242 244 244 , 244 247 250 251 255 257 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 259 260 262 264 265 268 274 277 PART V: 289 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 ofAI: Improving AI Technology Reinforcement Learning Generative Adversarial Learning Federated Learning Natural Language Processing Capsule Networks Quantum Machine Learning And This Is Just the Beginning 291 292 294 296 300 300 302 303 304 305 306 307 Further Readinq ^13 Acknowledgments About the Author Index ՝ii/ ^ |
| adam_txt |
Contents Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks Prologue: À Guide to This Book PARTI: xv xxi A BRIEF INTRODUCTION TO ARTIFICIAL INTELLIGENCE . 1 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 ix 3 4 6 7 8 13 · , 17 18 20
x CONTENTS Machine Learning Types of Uses for Machine Learning Types of Machine Learning Algorithms Supervised, Unsupervised, and Semisuperviscd Learning Making Data More Useful Semantic Reasoning Applications ofAI PART II: ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE 20 23 24 28 32 34 40 43 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 45 46 48 52 55 57 59 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 67 68 71 72 75 77 81 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 85 88 91 91 93 94 96 99 Chapter 6: AI in Healthcare Pharmaceutical Drug Discovery Clinical Trials 103 104 105
Contents xi Disease Diagnosis Preparation for Palliative Care Hospital Care 106 109 Ш PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117 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 119 120 122 126 127 129 130 131 132 134 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 137 Chapter 9: Building the Perfect AI Engine AI Platforms versus AĪ Applications What AI Platform Architectures Should Do ՜ , 138 143 144 145 146 148 151 160 164 164 165 166 166 168 169 171 172 172
xii CONTENTS 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 180 182 1S4 186 186 187 188 190 Chapter 10: Managing Model Risk When Algorithms Go Wrong Mitigating Model Risk Before Modeling During Modeling After Modeling Model Risk Office 193 195 197 197 199 201 209 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 213 214 215 217 218 PART IV: DELVING DEEPER INTO AI ARCHITECTURE AND MODELING Chapter 12: Architecture and Technical Patterns AI Platform Architecture Data Minder Model Maker 1'° 179 220 221 222 225 228 230 233 235 236 236 239
Contents Inference Activator Performance Manager Technical Patterns . Intelligent Virtual Assistant Personalization and Recommendation Engines Anomaly Detection Ambient Sensing and Physical Control Digital Workforce Conclusion xiii 242 244 244 , 244 247 250 251 255 257 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 259 260 262 264 265 268 274 277 PART V: 289 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 ofAI: Improving AI Technology Reinforcement Learning Generative Adversarial Learning Federated Learning Natural Language Processing Capsule Networks Quantum Machine Learning And This Is Just the Beginning 291 292 294 296 300 300 302 303 304 305 306 307 Further Readinq ^13 Acknowledgments About the Author Index ՝ii/ ^ |
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| title | Enterprise artificial intelligence transformation a playbook for the next generation of business and technology leaders |
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| title_full_unstemmed | Enterprise artificial intelligence transformation a playbook for the next generation of business and technology leaders Rashed Haq |
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| title_sort | enterprise artificial intelligence transformation a playbook for the next generation of business and technology leaders |
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