About US

Caleb is a fresh mind, approaching AI as mathematician with unbounded curiosity. Caleb has written a wide range of AI applications from scratch, without the aid of libraries. The AI applications span computer vision, natural language processing, and genetic algorithms. He has also spent extensive time with GPT-3, studying the strengths and weaknesses.

Caleb began programming at age 10, with Scratch.MIT.edu. By 14, he was instructing dozens of adult teachers to code so they could carry what they learned into their classrooms. At 14, after teaching himself Lisp (developed at MIT by John McCarthy for the first generation of AI programming), and Java, Caleb attended the MIT AI event in Silicon Valley, which led to this book. Throughout high school, Caleb developed AI applications. Caleb was also active in the First Robotics competition, where he was responsible for all programming of computer vision and autonomous operations. His team advanced to the International competition twice. Caleb applied his skills to AI applications for computer vision object detection, NLP, and genetic algorithms. 

At the same time, Caleb was advancing his mathematics foundation. As a junior in High School, Caleb attended Harvey Mudd College to study mathematics and as a senior in High School, he attended Stanford University to further advance his math skills. Currently, Caleb attends Reed College where he studies pure math and computer science. 

Many people use AI through libraries, which seperates them from the math of what the AI is really doing. Caleb, in contrast, has developed AI from scratch, allowing him to more deeply understand the capabilities and limitations of AI.

Rex is a seasoned business leader in marketing and data science that has used AI for more than two decades. 

Rex was WIRED’s first director of Research in 1995, where he first applied Neural Networks and other personalization technologies to media. He is credited with developing pioneering digital advertising measurement and cross-media attribution techniques, which rely on Machine Learning. He is responsible for the first published studies measuring the effectiveness of social media, and has come to appreciate the risks in using AI in media. 

In business, Rex is an entrepreneur that has applied AI to build multiple businesses, each valued at over $100 million. He holds five data science and AI related patents. His TED Talk on how AI can help humans shift from being knowledge workers to insights workers based on the AI’s awareness of what a person in business is working on, and AI's ability to provide contextually useful best practices is one of many examples of how Rex has applied AI in business.  

Rex honors the responsibility of sharing what he learns. Rex is co-author of What Sticks (2006), a book that has been required reading at Wharton, Harvard, and other leading universities, which advanced the use statistical analysis of marketing. He is also author of SIRFs Up, How Software and Algorithms Are Changing Marketing (2012). Rex is a member of California Polytechnic State University, San Luis Obispo College of Business Analytics’ Board and an award-winning marketing researcher. The Market Research Council named Rex as the 2022 Change Maker in their annual Hall of Fame inductions for his work with the Ad Council on applying AI to reduce hospitalizations and save lives from COVID. He is the Subject Matter Expert (SME) in AI and Data Science for the marketing trade organization MMA Global.

This book came about when Rex had a chance to attend Caleb’s high school thesis presentation entitled “Fundamental Restrictions on the Current State-of-the-Art in AI.” The paper had received buzz among his teachers. Rex was intrigued to learn about Caleb’s perspective on AI’s weaknesses. 

Over the prior three years, Rex had seen Caleb produce some fascinating applications of AI. At the same time, Rex was using AI in his marketing analytics business. 

Jens Peter Clausen, an executive from Google and a fellow parent, heard about Caleb’s research and sat-in to hear Caleb present his paper as well. After the presentation, he turned to Rex and noted that that Caleb had explained, better than anyone else he had encountered , the fundamental sources of weaknesses for Today's AI. Rex and Jens Peter agreed that too few understood these weaknesses, and the paper needed to be shared more broadly. 

Rex wrote Part 2 and 3 to bridge the content from Caleb's original paper into business and policy implications for the reader’s consideration. 

The authors how that reading this book will encourage the reader to take a step back and evaluate Today's AI with a new lens. Our hope is that the reader will find the concepts as interesting as the authors did and find value in the way Caleb connected the conceptual and mathematical to explain AI’s weaknesses, so that practitioners apply AI appropriately. The authors hope the reader will be able to relate to the case examples Rex assembled, and use the framework to identify risk and implement countermeasures to apply AI in a responsible manner.