Minds
Knowing Which Buttons to Push
Examining Key Industries
Chapter 21: Content Management: Finding What You Want, When You Want It
Introducing the Square Peg to the Round Hole
Finding Content at the Speed of AI
Expanding Your Toolbox
Examining the Use Cases
Chapter 22: AI-Enhanced Content Capture: Gathering All Your Eggs into the Same Basket
Counting All the Chickens, Hatched and Otherwise
Monetizing All the Piggies, Little and Otherwise
Getting All Your Ducks in a Row
Examining Key Industries
Chapter 23: Regulatory Compliance and Legal Risk Reduction: Hitting the Bullseye on a Moving Target
Dodging Bullets
Shooting Back
Building an Arsenal
Examining the Use Cases
Chapter 24: Knowledge Assistants and Chatbots: Monetizing the Needle in the Haystack
Missing the Trees for the Forest
Hearing the Tree Fall
Making Trees from Acorns
Examining the Use Cases
Chapter 25: AI-Enhanced Security: Staying Ahead by Watching Your Back
Closing the Barn Door
Locking the Barn Door
Knowing Which Key to Use
Examining the Use Cases
6 Part 4: The Part of Tens Chapter 26: Ten Ways AI Will Influence the Next Decade Proliferation of AI in the Enterprise AI Will Reach Across Functions AI R&D Will Span the Globe The Data Privacy Iceberg Will Emerge More Transparency in AI Applications Augmented Analytics Will Make It Easier Rise of Intelligent Text Mining Chatbots for Everyone Ethics Will Emerge for the AI Generation Rise of Smart Cities through AI Chapter 27: Ten Reasons Why AI Is Not a Panacea AI Is Not Human Pattern Recognition Is Not the Same As Understanding AI Cannot Anticipate Black Swan Events AI Might Be Democratized, but Data Is Not AI Is Susceptible to Inherent Bias in the Data AI Is Susceptible to Poor Problem Framing AI Is Blind to Data Ambiguity AI Will Not, or Cannot, Explain Its Own Results AI Is Not Immune to the Law of Unintended Consequences
7 Index
List of Tables
1 Chapter 1TABLE 1-1 Case Relationship for a SentenceTABLE 1-2 Data Mining Versus Text MiningTABLE 1-3 Machine Learning as a RecipeTABLE 1-4 Artificial Intelligence, Machine Learning, and Deep Learning
2 Chapter 3TABLE 3-1 Types of Visualizations and UsesTABLE 3-2 Pyramid of Critical Success Factors for AI and AnalyticsTABLE 3-3 Types of Dirty DataTABLE 3-4 Unsupervised Learning AlgorithmsTABLE 3-5 Supervised Learning AlgorithmsTABLE 3-6 Deep Learning Algorithms
3 Chapter 4TABLE 4-1 The Machine Learning Development Life Cycle: Elements and QuestionsTABLE 4-2 Example of Binary Classifier ResultsTABLE 4-3 Example Results CategoriesTABLE 4-4 Build versus Buy Pros and Cons
4 Chapter 6TABLE 6-1 Marketing Content Management Workflow
5 Chapter
