Cargando…
Augmented Intelligence The Business Power of Human-Machine Collaboration.
The AI revolution is moving at a breakneck speed. Organizations are beginning to invest in innovative ways to monetize their data through the use of artificial intelligence. Businesses need to understand the reality of AI. To be successful, it is imperative that organizations understand that augment...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Milton :
Auerbach Publishers, Incorporated,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Half Title; Title Page; Copyright Page; Endorsements; Dedications; Contents; Foreword; Preface; Why This Book? Why Now?; Why You Should Read This Book; What Is in This Book; About the Authors; Chapter 1: What Is Augmented Intelligence?; Introduction; Defining Augmented Intelligence; The Goal of Human-Machine Collaboration; How Augmented Intelligence Works in the Real World; Improving Traditional Applications with Machine Intelligence; Historical Perspective; The Three Principles of Augmented Intelligence; Explaining the Principles of Augmented Intelligence
- Machine Intelligence Addresses Human Intelligence LimitationsHuman Intelligence Should Provide Governance and Controls; Summary: How Augmented Intelligence and Artificial Intelligence Differ; Chapter 2: The Technology Infrastructure to Support Augmented Intelligence; Introduction; Beginning with Data Infrastructure; What a Difference the Cloud Makes; The Cloud Changes Everything; Big Data as Foundation; Understanding the Foundation of Big Data; Structured versus Unstructured Data; Machine Learning Techniques; Dealing with Constraints; Understanding Machine Learning; What Is Machine Learning?
- Iterative Learning from DataThe Roles of Statistics and Data Mining in Machine Learning; Putting Machine Learning in Context; Approaches to Machine Learning; Supervised Learning; Unsupervised Learning; Reinforcement Learning; Neural Networks and Deep Learning; Evolving to Deep Learning; Preparing for Augmented Intelligence; Chapter 3: The Cycle of Data; Introduction; Knowledge Transfer; Personalization; Determining the Right Data for Building Models; The Phases of the Data Cycle; Data Acquisition; Identifying Data Already within the Organization; Reasons for Acquiring Additional Data
- Data PreparationPreparing Data for Machine Learning and AI; Data Exploration; Data Cleansing; Feature Engineering; Overfitting versus Underfitting; Overfitting versus Underfitting for a Model Predicting Housing Prices; From Model Development and Deployment Back to Data Acquisition and Preparation; Chapter 4: Building Models to Support Augmented Intelligence; Introduction; Explaining Machine Learning Models; Understanding the Role of ML Algorithms; Inspectable Algorithms; Black Box Algorithms; Supervised Algorithms; Creating a Gold Standard for Supervised Learning; K-Nearest Neighbors
- Support Vector MachinesUnsupervised Algorithms; Understanding Reinforcement Learning and Neural Networks; The Value of Machine Learning Models; Summary; Chapter 5: Augmented Intelligence in a Business Process; Introduction; Defining the Business Process in Context with Augmented Intelligence; Weak Augmentation; Strong Augmentation; Strong Augmentation: Business Process Redesign; Augmented Intelligence in a Business Process about People; Strong Augmentation for Predictive Digital Marketing Campaign Management; Redefining Fashion Retailer Business Models with Augmented Intelligence