Haq, Rashed <br/><br/>
Enterprise artificial intelligence transformation: a playbook for the next generation of business and technology leaders 

 - New Jersey  John Wiley & Sons, Inc.  2020 
 - xxiv, 342 p. 
<br/><br/>TABLE OF CONTENTS<br/>Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks xv<br/><br/>Prologue: A Guide to This Book xxi<br/><br/>Part I: A Brief Introduction to Artificial Intelligence 1<br/><br/>Chapter 1: A Revolution in the Making 3<br/><br/>The Impact of the Four Revolutions 4<br/><br/>AI Myths and Reality 6<br/><br/>The Data and Algorithms Virtuous Cycle 7<br/><br/>The Ongoing Revolution – Why Now? 8<br/><br/>AI: Your Competitive Advantage 13<br/><br/>Chapter 2: What Is AI and How Does It Work? 17<br/><br/>The Development of Narrow AI 18<br/><br/>The First Neural Network 20<br/><br/>Machine Learning 20<br/><br/>Types of Uses for Machine Learning 23<br/><br/>Types of Machine Learning Algorithms 24<br/><br/>Supervised, Unsupervised, and Semisupervised Learning 28<br/><br/>Making Data More Useful 32<br/><br/>Semantic Reasoning 34<br/><br/>Applications of AI 40<br/><br/>Part II: Artificial Intelligence In the Enterprise 43<br/><br/>Chapter 3: AI in E-Commerce and Retail 45<br/><br/>Digital Advertising 46<br/><br/>Marketing and Customer Acquisition 48<br/><br/>Cross-Selling, Up-Selling, and Loyalty 52<br/><br/>Business-to-Business Customer Intelligence 55<br/><br/>Dynamic Pricing and Supply Chain Optimization 57<br/><br/>Digital Assistants and Customer Engagement 59<br/><br/>Chapter 4: AI in Financial Services 67<br/><br/>Anti-Money Laundering 68<br/><br/>Loans and Credit Risk 71<br/><br/>Predictive Services and Advice 72<br/><br/>Algorithmic and Autonomous Trading 75<br/><br/>Investment Research and Market Insights 77<br/><br/>Automated Business Operations 81<br/><br/>Chapter 5: AI in Manufacturing and Energy 85<br/><br/>Optimized Plant Operations and Assets Maintenance 88<br/><br/>Automated Production Lifecycles 91<br/><br/>Supply Chain Optimization 91<br/><br/>Inventory Management and Distribution Logistics 93<br/><br/>Electric Power Forecasting and Demand Response 94<br/><br/>Oil Production 96<br/><br/>Energy Trading 99<br/><br/>Chapter 6: AI in Healthcare 103<br/><br/>Pharmaceutical Drug Discovery 104<br/><br/>Clinical Trials 105<br/><br/>Disease Diagnosis 106<br/><br/>Preparation for Palliative Care 109<br/><br/>Hospital Care 111<br/><br/>PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117<br/><br/>Chapter 7: Developing an AI Strategy 119<br/><br/>Goals of Connected Intelligence Systems 120<br/><br/>The Challenges of Implementing AI 122<br/><br/>AI Strategy Components 126<br/><br/>Steps to Develop an AI Strategy 127<br/><br/>Some Assembly Required 129<br/><br/>Creating an AI Center of Excellence 130<br/><br/>Building an AI Platform 131<br/><br/>Defining a Data Strategy 132<br/><br/>Moving Ahead 134<br/><br/>Chapter 8: The AI Lifecycle 137<br/><br/>Defining Use Cases 138<br/><br/>Collecting, Assessing, and Remediating Data 143<br/><br/>Data Instrumentation 144<br/><br/>Data Cleansing 145<br/><br/>Data Labeling 146<br/><br/>Feature Engineering 148<br/><br/>Selecting and Training a Model 151<br/><br/>Managing Models 160<br/><br/>Testing, Deploying, and Activating Models 164<br/><br/>Testing 164<br/><br/>Governing Model Risk 165<br/><br/>Deploying the Model 166<br/><br/>Activating the Model 166<br/><br/>Production Monitoring 168<br/><br/>Conclusion 169<br/><br/>Chapter 9: Building the Perfect AI Engine 171<br/><br/>AI Platforms versus AI Applications 172<br/><br/>What AI Platform Architectures Should Do 172<br/><br/>Some Important Considerations 179<br/><br/>Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? 179<br/><br/>Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? 180<br/><br/>Should a Business Use Batch or Real-Time Processing? 182<br/><br/>Should a Business Use Monolithic or Microservices Architecture? 184<br/><br/>AI Platform Architecture 186<br/><br/>Data Minder 186<br/><br/>Model Maker 187<br/><br/>Inference Activator 188<br/><br/>Performance Manager 190<br/><br/>Chapter 10: Managing Model Risk 193<br/><br/>When Algorithms Go Wrong 195<br/><br/>Mitigating Model Risk 197<br/><br/>Before Modeling 197<br/><br/>During Modeling 199<br/><br/>After Modeling 201<br/><br/>Model Risk Office 209<br/><br/>Chapter 11: Activating Organizational Capability 213<br/><br/>Aligning Stakeholders 214<br/><br/>Organizing for Scale 215<br/><br/>AI Center of Excellence 217<br/><br/>Standards and Project Governance 218<br/><br/>Community, Knowledge, and Training 220<br/><br/>Platform and AI Ecosystem 221<br/><br/>Structuring Teams for Project Execution 222<br/><br/>Managing Talent and Hiring 225<br/><br/>Data Literacy, Experimentation, and Data-Driven Decisions 228<br/><br/>Conclusion 230<br/><br/>Part IV: Delving Deeper Into AI Architecture and Modeling 233<br/><br/>Chapter 12: Architecture and Technical Patterns 235<br/><br/>AI Platform Architecture 236<br/><br/>Data Minder 236<br/><br/>Model Maker 239<br/><br/>Inference Activator 242<br/><br/>Performance Manager 244<br/><br/>Technical Patterns 244<br/><br/>Intelligent Virtual Assistant 244<br/><br/>Personalization and Recommendation Engines 247<br/><br/>Anomaly Detection 250<br/><br/>Ambient Sensing and Physical Control 251<br/><br/>Digital Workforce 255<br/><br/>Conclusion 257<br/><br/>Chapter 13: The AI Modeling Process 259<br/><br/>Defining the Use Case and the AI Task 260<br/><br/>Selecting the Data Needed 262<br/><br/>Setting Up the Notebook Environment and Importing Data 264<br/><br/>Cleaning and Preparing the Data 265<br/><br/>Understanding the Data Using Exploratory Data Analysis 268<br/><br/>Feature Engineering 274<br/><br/>Creating and Selecting the Optimal Model 277<br/><br/>Part V: Looking Ahead 289<br/><br/>Chapter 14: The Future of Society, Work, and AI 291<br/><br/>AI and the Future of Society 292<br/><br/>AI and the Future of Work 294<br/><br/>Regulating Data and Artificial Intelligence 296<br/><br/>The Future of AI: Improving AI Technology 300<br/><br/>Reinforcement Learning 300<br/><br/>Generative Adversarial Learning 302<br/><br/>Federated Learning 303<br/><br/>Natural Language Processing 304<br/><br/>Capsule Networks 305<br/><br/>Quantum Machine Learning 306<br/><br/>And This Is Just the Beginning 307<br/><br/>Further Reading 313<br/><br/>Acknowledgments 317<br/><br/>About the Author 319<br/><br/>Index 321 
<br/><br/> DESCRIPTION<br/>Enterprise Artificial Intelligence Transformation<br/><br/>AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals.<br/><br/>Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation.<br/><br/>The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning.<br/><br/>Enterprise Artificial Intelligence Transformation covers a wide range of topics, including:<br/><br/>Real-world AI use cases and examples<br/>Machine learning, deep learning, and slimantic modeling<br/>Risk management of AI models<br/>AI strategies for development and expansion<br/>AI Center of Excellence creating and management<br/>If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation. 
<br/><br/><label>ISBN: </label>9781119665939 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence--Economic aspects<br/>Business enterprises--Technological innovations<br/>Organizational effectiveness<br/>Organizational learning
<br/><br/><label>Dewey Class. No.: </label>006.3068  / HAQ