Causal Artificial Intelligence The Next Step in Effective Business AI 1st Edition by Judith Hurwitz, John Thompson ISBN 9781394184156 1394184158
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Authors:Judith S. Hurwitz; John K. Thompson , Series:Psychology [72] , Tags:Computers; Artificial Intelligence; General; Business & Economics; Decision-Making & Problem Solving; Business & Productivity Software; Business Intelligence; Computer Science; Information Technology , Author sort:Hurwitz, Judith S. & Thompson, John K. , Ids:9781394184156 , Languages:Languages:eng , Published:Published:Aug 2023 , Publisher:John Wiley & Sons , Comments:Comments:Discover the next major revolution in data science and AI and how it applies to your organization In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings. Useful for both data scientists and business-side professionals, the book offers: Clear and compelling descriptions of the concept of causality and how it can benefit your organization Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems Useful strategies for deciding when to use correlation-based approaches and when to use causal inferenceAn enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.
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Product details:
ISBN 10: 1394184158
ISBN 13: 9781394184156
Author: Judith Hurwitz, John Thompson
In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.
Useful for both data scientists and business-side professionals, the book offers:
- Clear and compelling descriptions of the concept of causality and how it can benefit your organization
- Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems
- Useful strategies for deciding when to use correlation-based approaches and when to use causal inference
An enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.
Causal Artificial Intelligence The Next Step in Effective Business AI 1st Edition Table of 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
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