Causal Artificial Intelligence: The Next Step in Effective Business AI 1st Edition by Judith 9781394184156 1394184158

Causal Artificial Intelligence: The Next Step in Effective Business AI 1st Edition Judith S. Hurwitz – Ebook Instant Download/Delivery ISBN(s): 9781394184156, 1394184158

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Causal Artificial Intelligence
The Next Step in Effective Business AI

Table contents:

CHAPTER 1: Setting the Stage for Causal AI
Why Causality Is a Game Changer
Causal AI in Perspective with Analytics
Analytical Sophistication Model
Scope of Services to Support Causal AI
The Value of the Hybrid Team
The Promise of AI
Understanding the Core Concepts of Causal AI
Summary
Note
CHAPTER 2: Understanding the Valueof Causal AI
Defining Causal AI
The Origins of Causal AI
Why Causal AI Is the Next Generation of AI
The Business Imperative of a Causal Model
The Importance of Augmented Intelligence
The Importance of Data, Visualization, and Frameworks
Getting Started with Causal AI
Summary
Notes
CHAPTER 3: Elements of Causal AI
Conceptual Models
Process Models
Collaboration Between Business and Analytics Professionals
The Fundamental Building Blocks of Causal AI Models
The Elements of Visual Modeling
Summary
Notes
CHAPTER 4: Creating Practical Causal AI Models and Systems
Understanding Complex Models
Causal Modeling Process: Part 1
Causal-Based Approach: Part 2
Summary
Notes
CHAPTER 5: Creating a Model with a Hybrid Team
The Hybrid Team
Defining Roles
The Basics Steps for a Hybrid Team Project
An Overview of Model Creation
Summary
CHAPTER 6: Explainability, Bias Detection, and AI Responsibility in Causal AI
Explainability
Detecting Bias and Ensuring Responsible AI
Summary
Note
CHAPTER 7: Tools, Practices, and Techniques to Enable Causal AI
The Causal AI Pipeline
The Importance of Synthetic Data
Current State of Tools and Software in Causal AI
Summary
CHAPTER 8: Causal AI in Action
Enterprise Marketing in a Business-to-Consumer Scenario
Moving from Strategy to Building and Implementing Causal AI Solutions
Summary
CHAPTER 9: The Future of Causal AI
Where We Stand Today
Foundations of Causal AI
The Causal AI Journey
Integrating Causal AI and Traditional AI
The Imperative for Managing Data
Ensembles of Data
Generative AI Is Emerging as a Game Changer for Causal AI
The Future of Causal Discovery
The Emergence of Causal AI Reinforcement Learning Will Accelerate Model Training
Causal AI as a Common Language Between Business Leaders and Data Scientists
The Emergence of Probabilistic Programming Languages
The Predictable Model Evolution Cycle
The Emergence of the Digital Twin
Improving the Ability to Understand Ground Truth
The Development of More Sophisticated DAGs
Visualizing Complex Relationships in the DAGs
The Merging of Causal and Traditional AI Models
The Future of Explainability
The Evolution of Responsible AI
Advances in Data Security and Privacy
Integration Will Be Between Models and Business Applications
Summary
Glossary
Appendix: Causal AI Tools and Libraries
Selected Resources
Acknowledgments
About the Authors
About the Contributor
Index
Copyright
Dedication
End User License Agreement

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