Enterprise Artificial Intelligence Transformation

ISBN-13: 9781119665939 / Angielski / Twarda / 2020 / 368 str.

Rashed Haq

Spis treści

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

O autorze

RASHED HAQ is an AI and robotics technologist. He was recently appointed as the Vice President of Robotics at Cruise, one of the leading autonomous vehicle companies. He was previously the Global Head of AI & Data and Group Vice President at Publicis Sapient. He has spent over 20 years helping companies transform and create sustained competitive advantage through technology and data. Rashed holds advanced degrees in theoretical physics and mathematics. An accomplished author and sought-after speaker, Rashed frequently writes about the practical uses of AI in business.