Contents
Contents
Chapter 1: What Is Augmented Intelligence?
Defining Augmented Intelligence
The Goal of Human–Machine Collaboration
How Augmented Intelligence Works in the Real World
Improving Traditional Applications with Machine Intelligence
The Three Principles of Augmented Intelligence
Explaining the Principles of Augmented Intelligence
Machine Intelligence Addresses Human Intelligence Limitations
Human Intelligence Should Provide Governance and Controls
Summary: How Augmented Intelligence and Artificial Intelligence Differ
Chapter 2: The Technology Infrastructure to Support Augmented Intelligence
Beginning with Data Infrastructure
What a Difference the Cloud Makes
Understanding the Foundation of Big Data
Structured versus Unstructured Data
Understanding Machine Learning
The Roles of Statistics and Data Mining in Machine Learning
Putting Machine Learning in Context
Approaches to Machine Learning
Neural Networks and Deep Learning
Preparing for Augmented Intelligence
Determining the Right Data for Building Models
Identifying Data Already within the Organization
Reasons for Acquiring Additional Data
Preparing Data for Machine Learning and AI
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
Explaining Machine Learning Models
Understanding the Role of ML Algorithms
Creating a Gold Standard for Supervised Learning
Understanding Reinforcement Learning and Neural Networks
The Value of Machine Learning Models
Chapter 5: Augmented Intelligence in a Business Process
Defining the Business Process in Context with Augmented Intelligence
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
Business Model Changes at The Gap, Inc., Using Algorithmic Fashion Predictions
Another Fashion Retailing Business Model Using Algorithmic Predictions: Stitch Fix
Chapter 6: Risks in Augmented Intelligence
Providing Context and Understanding
Understanding the Risks of a ML Model
The Importance of Digital Auditing
The Risks in Capturing More Data
1. The Risk of Overfitting or Underfitting
2. Changing Business Processes Increases Risk
4. The Risk of Over Relying on the Algorithm
5. The Risk of Lack of Explainability
6. The Risk of Revealing Confidential Information
7. The Risk of a Poorly Constructed Team
Chapter 7: The Criticality of Governance and Ethics in Augmented Intelligence
Creating Your Augmented Intelligence Control Framework
Steps in Your AI Control Framework
On an Organizational Approach to Controls
Chapter 8: The Business Case for Augmented Intelligence
Taking Advantage of Disruption
Advantages of New Disruptive Models
The Four Stages of Data Maturity
Building Business-Specific Solutions
Making Augmented Intelligence a Reality
How Augmented Intelligence Is Changing the Market
Chapter 9: Getting Started on Your Journey to Augmented Intelligence
Moving Forward with the Foundation
Selecting a Project that Can Be a Reference for Future Projects
Chapter 10: Predicting the Future of Augmented Intelligence
The Future of Governance and Compliance
Machines Will Learn to Train Humans
New Techniques for Identifying Bias in Data
Emerging Techniques for Understanding Unlabeled Data
Emerging Techniques for Training Data
Reinforcement Learning Will Gain Huge Momentum
New Algorithms Will Improve Accuracy
Distributed Data Models Will Protect Data
Explainability Will Become a Requirement