| Description |
1 online resource (170 pages) |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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 augmented intelligence is the secret to success. Augmented Intelligence: The Business Power of Human-Machine Collaboration is about the process of combining human and machine intelligence. This book provides business leaders and AI data experts with an understanding of the value of augmented intelligence and its ability to help win competitive markets. This book focuses on the requirement to clearly manage the foundational data used for augmented intelligence. It focuses on the risks of improper data use and delves into the ethics and governance of data in the era of augmented intelligence. In this book, we explore the difference between weak augmentation that is based on automating well understood processes and strong augmentation that is designed to rethink business processes through the inclusion of data, AI and machine learning. What experts are saying about Augmented Intelligence "The book you are about to read is of great importance because we increasingly rely on machine learning and AI. Therefore, it is critical that we understand the ability to create an environment in which businesses can have the tools to understand data from a holistic perspective. What is imperative is to be able to make better decisions based on an understanding of the behavior and thinking of our customers so that we can take the best next action. This book provides a clear understanding of the impact of augmented intelligence on both society and business."--Tsvi Gal, Managing Director, Enterprise Technology and Services, Morgan Stanley "Our mission has always been to help clients apply AI to better predict and shape future outcomes, empower higher value work, and automate how work gets done. I have always said, 'AI will not replace managers, but managers who use AI will replace managers who don't.' This book delves into the real value that AI promises, to augment existing human intelligence, and in the process, dispels some of the myths around AI and its intended purpose."--Rob Thomas, General Manager, Data and AI, IBM. |
| Note |
Description based upon print version of record. |
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Business Model Changes at The Gap, Inc., Using Algorithmic Fashion Predictions. |
| Contents |
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 |
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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? |
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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 |
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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 |
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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 |
| Subject |
Artificial intelligence.
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Technological innovations.
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Artificial Intelligence |
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Intelligence artificielle. |
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Innovations. |
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artificial intelligence. |
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Artificial intelligence |
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Technological innovations |
| Added Author |
Morris, Henry.
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Sidner, C. L.
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Kirsch, Daniel.
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| Other Form: |
Print version: 0367184893 |
| ISBN |
0429589719 |
|
9780429589713 |
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0429196644 |
|
9780429196645 |
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0429591659 |
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9780429591655 |
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(e-book) |
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