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.

Autorentext

JUDITH S. HURWITZ is the chief evangelist at Geminos Software, a causal AI platform company. For more than 35 years she has been a strategist, technology consultant to software providers, and a thought leader having authored 10 books in topics ranging from augmented intelligence, data analytics, and cloud computing.

JOHN K. THOMPSON is an international technology executive with over 37 years of experience in the fields of data, advanced analytics, and artificial intelligence (AI). John is responsible for the global AI function at EY. He has previously led the global Artificial Intelligence and Rapid Data Lab teams at CSL Behring and is the bestselling author of three books on data analytics.