Enterprise AI For Dummies. Zachary Jarvinen

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

      8  About the Author

      9  Advertisement Page

      10  Connect with Dummies

      11  End User License Agreement

      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 13TABLE 13-1 SAE J3016 Level of Driving Automation

      List of Illustrations

      1 Chapter 1FIGURE 1-1: Business value versus difficulty in analytics.

      2 Chapter 3FIGURE 3-1: Comparison: Total page visits by mean duration of visit.FIGURE 3-2: Composition: Employee per industry (top), revenue per market segmen...FIGURE 3-3: Distribution: Startups per county.FIGURE 3-4: Relationship: Call center wait time versus satisfaction score.FIGURE 3-5: Pyramid of critical success factors for AI and analytics.FIGURE 3-6: Microsoft Excel supports