AI technology is moving along in leaps and bounds - you only have to look at what’s accessible with a few clicks to know that the future is likely going to lay heavily in the hands of it. The major question for businesses is - when do they adopt AI over human, and how do they deal with the impact while preparing for the shift? Agrawal, Gans, and Goldfarb do an excellent job of breaking down these questions, and some possible solutions. They present not only how AI could be a cheaper option, but it’s potential to be beneficial for predictions in many different areas. Even if you aren’t super-tech oriented and keeping up on the cutting edge of AI technology, you’ll be able to follow along with what’s being said in this book and understand the concepts presented, as they are done so in a very accessible manner. A great book overall, and I look forward to seeing where AI takes us in the future, and how many of the authors’ predictions come to life. 

 

This was a very interesting book about Artificial Intelligence and the ways that it is used to predict how to market products, design airports, manage healthcare concerns, and more. I liked reading about all the examples that the authors gave, which helped to explain the concepts in an easy to understand manner. I also liked that the authors discussed issues with using AI without human interaction and/or judgement. 

This is a fascinating field with a lot of growth still to come. There are many uses as to how AI can improve our lives, but there is also a healthy amount of caution to be used before deploying it widely, as the authors note. 

 

Power and Prediction: The Disruptive Economics of Artificial Intelligence is a well-written book and contains a ton of information. For the novice, the book gives a solid overview of AI, and for the more advanced a pretty in-depth analysis of the benefits and concerns about the technology. The book deals with the latest developments and techniques as well. With examples from various business fields, like medicine, and insurance, you will learn about how the AI experience works in the real world. Overall a solid read. 

 

"It is easier not to have to make a decision than to make one. " 

 

 

It is a highly informative and practical guide on how businesses can prepare for the coming disruption resulting from the proliferation of AI. The authors build upon their previous work in Prediction Machines to explain how AI is a prediction technology that directly impacts decision-making and how businesses can leverage this technology to identify disruptive opportunities and threats. 

 

The book explores the "Between Times," a phase in which we are witnessing the power of AI before its widespread adoption. The authors explain that while there are still significant opportunities for businesses, there are also threats of disruption, as old ways of doing things will be upended. The uneven process by which AI filters into the many systems involved in its application will have winners and losers, and businesses must leverage or protect their positions accordingly. 

 

The authors use rich examples and practical advice to help business leaders and policymakers understand how to make the coming AI disruptions work for them. They explore the impact of AI on various industries, such as banking, finance, pharmaceuticals, automotive, medical technology, and retail, and explain how businesses can use AI to make cheaper, better, and faster predictions that drive strategic decision-making. 

 

It is an insightful and convenient guide for businesses seeking to prepare for the coming disruption resulting from the proliferation of AI. The book is well-researched and well-written, and the authors' expertise in the field is evident. Anyone looking to stay ahead of the curve in the AI revolution would do well to read this book. 

 

Canadian authors Ajay Agrawal, Joshua Gans, and Avi Goldfarb offer their expertise on subjects of strategic management, entrepreneurship, innovation, artificial intelligence and healthcare as educators at the Rotman School of Management in Toronto, as well as important contributions in other areas of note. In this excellent book they bland their input to create one of the most accessible, informative, and reliably accurate accounts of the impact of artificial intelligence on individuals, companies, and organizations. As they state in the Preface addressing their prior tome on AI, ‘ We realized that we must consider not only the economics of the technology itself but also the systems in which the technology operates. We must understand the economic forces that led to the rapid adoption of AIs for automated fraud detection in banking and product recommendation in e-commerce on the one hand, but the slow adoption of AIs for automated underwriting in insurance and drug discovery in pharmaceuticals on the other…. We shifted our focus from exploring neural networks to exploring human cognition (how we make decisions), social behavior (why people in some industries are keen to embrace AI quickly while others are resistant), production systems (how some decisions depend on others), and industry structures (how we’ve hidden certain decisions to shield ourselves from uncertainty).’ 

 

While everyone has heard of Artificial Intelligence (AI) in the news, social media, television and movies - the ‘topic du jour’ in our lives today - few resources match the accessible, credible, well presented examination of this technology as this excellent book relates. The authors’ skills as educators make this fascinating tome a primary guidebook for insight into the effect AI has on economics at present and the possible permutations and potentials of AI in the future. In other words, read this book and understand AI - scholarly and accessible - finally! Highly recommended. 

This is written from the prospective of an economist. As such, it focuses on that point of view when discussing AI and its implementation. The authors mention privacy concerns as sticking points and claims that AI is easier to fix biases than people, ignoring that Amazon and Google have admitted they do not know how their algorithms work. 

Since the book ignores those problems and a few others, I have given it a lower score. As a discussion on just the business side, it is sound.