Enterprise Artificial Intelligence Transformation
-15%
portes grátis
Enterprise Artificial Intelligence Transformation
John Wiley & Sons Inc
08/2020
368
Dura
Inglês
9781119665939
15 a 20 dias
Descrição não disponível.
Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks xv Prologue: A Guide to This Book xxi Part I: A Brief Introduction to Artificial Intelligence 1 Chapter 1: A Revolution in the Making 3 The Impact of the Four Revolutions 4 AI Myths and Reality 6 The Data and Algorithms Virtuous Cycle 7 The Ongoing Revolution - Why Now? 8 AI: Your Competitive Advantage 13 Chapter 2: What Is AI and How Does It Work? 17 The Development of Narrow AI 18 The First Neural Network 20 Machine Learning 20 Types of Uses for Machine Learning 23 Types of Machine Learning Algorithms 24 Supervised, Unsupervised, and Semisupervised Learning 28 Making Data More Useful 32 Semantic Reasoning 34 Applications of AI 40 Part II: Artificial Intelligence In the Enterprise 43 Chapter 3: AI in E-Commerce and Retail 45 Digital Advertising 46 Marketing and Customer Acquisition 48 Cross-Selling, Up-Selling, and Loyalty 52 Business-to-Business Customer Intelligence 55 Dynamic Pricing and Supply Chain Optimization 57 Digital Assistants and Customer Engagement 59 Chapter 4: AI in Financial Services 67 Anti-Money Laundering 68 Loans and Credit Risk 71 Predictive Services and Advice 72 Algorithmic and Autonomous Trading 75 Investment Research and Market Insights 77 Automated Business Operations 81 Chapter 5: AI in Manufacturing and Energy 85 Optimized Plant Operations and Assets Maintenance 88 Automated Production Lifecycles 91 Supply Chain Optimization 91 Inventory Management and Distribution Logistics 93 Electric Power Forecasting and Demand Response 94 Oil Production 96 Energy Trading 99 Chapter 6: AI in Healthcare 103 Pharmaceutical Drug Discovery 104 Clinical Trials 105 Disease Diagnosis 106 Preparation for Palliative Care 109 Hospital Care 111 PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117 Chapter 7: Developing an AI Strategy 119 Goals of Connected Intelligence Systems 120 The Challenges of Implementing AI 122 AI Strategy Components 126 Steps to Develop an AI Strategy 127 Some Assembly Required 129 Creating an AI Center of Excellence 130 Building an AI Platform 131 Defining a Data Strategy 132 Moving Ahead 134 Chapter 8: The AI Lifecycle 137 Defining Use Cases 138 Collecting, Assessing, and Remediating Data 143 Data Instrumentation 144 Data Cleansing 145 Data Labeling 146 Feature Engineering 148 Selecting and Training a Model 151 Managing Models 160 Testing, Deploying, and Activating Models 164 Testing 164 Governing Model Risk 165 Deploying the Model 166 Activating the Model 166 Production Monitoring 168 Conclusion 169 Chapter 9: Building the Perfect AI Engine 171 AI Platforms versus AI Applications 172 What AI Platform Architectures Should Do 172 Some Important Considerations 179 Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? 179 Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? 180 Should a Business Use Batch or Real-Time Processing? 182 Should a Business Use Monolithic or Microservices Architecture? 184 AI Platform Architecture 186 Data Minder 186 Model Maker 187 Inference Activator 188 Performance Manager 190 Chapter 10: Managing Model Risk 193 When Algorithms Go Wrong 195 Mitigating Model Risk 197 Before Modeling 197 During Modeling 199 After Modeling 201 Model Risk Office 209 Chapter 11: Activating Organizational Capability 213 Aligning Stakeholders 214 Organizing for Scale 215 AI Center of Excellence 217 Standards and Project Governance 218 Community, Knowledge, and Training 220 Platform and AI Ecosystem 221 Structuring Teams for Project Execution 222 Managing Talent and Hiring 225 Data Literacy, Experimentation, and Data-Driven Decisions 228 Conclusion 230 Part IV: Delving Deeper Into AI Architecture and Modeling 233 Chapter 12: Architecture and Technical Patterns 235 AI Platform Architecture 236 Data Minder 236 Model Maker 239 Inference Activator 242 Performance Manager 244 Technical Patterns 244 Intelligent Virtual Assistant 244 Personalization and Recommendation Engines 247 Anomaly Detection 250 Ambient Sensing and Physical Control 251 Digital Workforce 255 Conclusion 257 Chapter 13: The AI Modeling Process 259 Defining the Use Case and the AI Task 260 Selecting the Data Needed 262 Setting Up the Notebook Environment and Importing Data 264 Cleaning and Preparing the Data 265 Understanding the Data Using Exploratory Data Analysis 268 Feature Engineering 274 Creating and Selecting the Optimal Model 277 Part V: Looking Ahead 289 Chapter 14: The Future of Society, Work, and AI 291 AI and the Future of Society 292 AI and the Future of Work 294 Regulating Data and Artificial Intelligence 296 The Future of AI: Improving AI Technology 300 Reinforcement Learning 300 Generative Adversarial Learning 302 Federated Learning 303 Natural Language Processing 304 Capsule Networks 305 Quantum Machine Learning 306 And This Is Just the Beginning 307 Further Reading 313 Acknowledgments 317 About the Author 319 Index 321
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Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks xv Prologue: A Guide to This Book xxi Part I: A Brief Introduction to Artificial Intelligence 1 Chapter 1: A Revolution in the Making 3 The Impact of the Four Revolutions 4 AI Myths and Reality 6 The Data and Algorithms Virtuous Cycle 7 The Ongoing Revolution - Why Now? 8 AI: Your Competitive Advantage 13 Chapter 2: What Is AI and How Does It Work? 17 The Development of Narrow AI 18 The First Neural Network 20 Machine Learning 20 Types of Uses for Machine Learning 23 Types of Machine Learning Algorithms 24 Supervised, Unsupervised, and Semisupervised Learning 28 Making Data More Useful 32 Semantic Reasoning 34 Applications of AI 40 Part II: Artificial Intelligence In the Enterprise 43 Chapter 3: AI in E-Commerce and Retail 45 Digital Advertising 46 Marketing and Customer Acquisition 48 Cross-Selling, Up-Selling, and Loyalty 52 Business-to-Business Customer Intelligence 55 Dynamic Pricing and Supply Chain Optimization 57 Digital Assistants and Customer Engagement 59 Chapter 4: AI in Financial Services 67 Anti-Money Laundering 68 Loans and Credit Risk 71 Predictive Services and Advice 72 Algorithmic and Autonomous Trading 75 Investment Research and Market Insights 77 Automated Business Operations 81 Chapter 5: AI in Manufacturing and Energy 85 Optimized Plant Operations and Assets Maintenance 88 Automated Production Lifecycles 91 Supply Chain Optimization 91 Inventory Management and Distribution Logistics 93 Electric Power Forecasting and Demand Response 94 Oil Production 96 Energy Trading 99 Chapter 6: AI in Healthcare 103 Pharmaceutical Drug Discovery 104 Clinical Trials 105 Disease Diagnosis 106 Preparation for Palliative Care 109 Hospital Care 111 PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117 Chapter 7: Developing an AI Strategy 119 Goals of Connected Intelligence Systems 120 The Challenges of Implementing AI 122 AI Strategy Components 126 Steps to Develop an AI Strategy 127 Some Assembly Required 129 Creating an AI Center of Excellence 130 Building an AI Platform 131 Defining a Data Strategy 132 Moving Ahead 134 Chapter 8: The AI Lifecycle 137 Defining Use Cases 138 Collecting, Assessing, and Remediating Data 143 Data Instrumentation 144 Data Cleansing 145 Data Labeling 146 Feature Engineering 148 Selecting and Training a Model 151 Managing Models 160 Testing, Deploying, and Activating Models 164 Testing 164 Governing Model Risk 165 Deploying the Model 166 Activating the Model 166 Production Monitoring 168 Conclusion 169 Chapter 9: Building the Perfect AI Engine 171 AI Platforms versus AI Applications 172 What AI Platform Architectures Should Do 172 Some Important Considerations 179 Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? 179 Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? 180 Should a Business Use Batch or Real-Time Processing? 182 Should a Business Use Monolithic or Microservices Architecture? 184 AI Platform Architecture 186 Data Minder 186 Model Maker 187 Inference Activator 188 Performance Manager 190 Chapter 10: Managing Model Risk 193 When Algorithms Go Wrong 195 Mitigating Model Risk 197 Before Modeling 197 During Modeling 199 After Modeling 201 Model Risk Office 209 Chapter 11: Activating Organizational Capability 213 Aligning Stakeholders 214 Organizing for Scale 215 AI Center of Excellence 217 Standards and Project Governance 218 Community, Knowledge, and Training 220 Platform and AI Ecosystem 221 Structuring Teams for Project Execution 222 Managing Talent and Hiring 225 Data Literacy, Experimentation, and Data-Driven Decisions 228 Conclusion 230 Part IV: Delving Deeper Into AI Architecture and Modeling 233 Chapter 12: Architecture and Technical Patterns 235 AI Platform Architecture 236 Data Minder 236 Model Maker 239 Inference Activator 242 Performance Manager 244 Technical Patterns 244 Intelligent Virtual Assistant 244 Personalization and Recommendation Engines 247 Anomaly Detection 250 Ambient Sensing and Physical Control 251 Digital Workforce 255 Conclusion 257 Chapter 13: The AI Modeling Process 259 Defining the Use Case and the AI Task 260 Selecting the Data Needed 262 Setting Up the Notebook Environment and Importing Data 264 Cleaning and Preparing the Data 265 Understanding the Data Using Exploratory Data Analysis 268 Feature Engineering 274 Creating and Selecting the Optimal Model 277 Part V: Looking Ahead 289 Chapter 14: The Future of Society, Work, and AI 291 AI and the Future of Society 292 AI and the Future of Work 294 Regulating Data and Artificial Intelligence 296 The Future of AI: Improving AI Technology 300 Reinforcement Learning 300 Generative Adversarial Learning 302 Federated Learning 303 Natural Language Processing 304 Capsule Networks 305 Quantum Machine Learning 306 And This Is Just the Beginning 307 Further Reading 313 Acknowledgments 317 About the Author 319 Index 321
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