Causal Artificial Intelligence

Introduction

Why this book, and why now?

We have spent decades exploring, researching, writing, and working with the most important emerging technologies. We have seen hundreds of innovative and novel technologies come and go, each promising to turn human knowledge into packaged solutions that are easy to understand and implement. The history of technology solutions has proven repeatedly that there are no simple solutions to complex problems. However, each technological solution takes us a step further to addressing the core issues. For the past 10 years, the focus of AI and advanced analytics has been on analyzing massive amounts of data to understand the answers to difficult problems. Big data was the silver bullet that offered some success but did not go far enough. In fact, often beginning with big data created correlations that sent businesses in the wrong direction.

One of the problems with leveraging complex technology solutions is that they are multifaceted, interconnected, and complex. It is possible that the data scientist can understand all the ins and outs of the underlying math and technology, but to be successful, the data team must work in collaboration with IT and business to anticipate customer needs and to plan for what's next. In most cases, business leaders do not understand emerging technologies, the data, or the underlying math; hence, they don't know what questions to ask to determine if the technology is well suited to solving their specific operational ...