Prediction Machines: The Simple Economics of Artificial Intelligence

I spent my last weeks of summer reading a well-crafted book about A.I. and business, Prediction Machines: The Simple Economics of Artificial Intelligence

The book doesn't try to explain in detail how current A.I. works, but rather focuses on cutting through the hype and giving an economic framework on how businesses can assess the impact this technology will have on their current businesses. Moreover, the authors explain with plenty of examples the key questions that any business leader should ask themselves to develop a strategy to compete in an "A.I. first" world and which tradeoffs to focus on.

"We don’t prescribe the best strategy for your business. That’s your job. The best strategy for your company or career or country will depend on how you weigh each side of every trade-off. This book gives you a structure for identifying the key trade-offs and how to evaluate the pros and cons in order to reach the best decision for you."

The authors structured the chapters in 5 main topics, starting from the specifics up to broader thoughts on how society will be affected by the A.I. revolution.

The first chapter "Prediction" makes a good job of explaining the difference between current machine learning algorithms and the philosophical concept of intelligence. Furthermore, the book takes time to explain which are the current limits of the predictive approach and how those limitations trace the boundaries between machine and human tasks.

There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones... Perhaps the biggest weakness of prediction machines is that they sometimes provide wrong answers that they are confident are right.

In the second chapter "Decision Making" the book unpacks the anatomy of decision making and discuss the importance of judgement to this task. The authors brilliantly build up each scenario, starting with the case in which machines can make better decisions, thus, automate them, up to the cases in which human judgement is further enhanced with the help of prediction.

Prediction machines may also lack data because some events are rare. If a machine cannot observe enough human decisions, it cannot predict the judgment underlying those decisions.

Chapter three "Tools" focuses on how in real business scenarios A.I. can be used as a tool to power and improve workflows, processes and even products. With convincing examples like the case on how A.I. saved the iPhone by making its keyboard usable from launch, the book shows the importance of finding in which tasks the effort of implementing prediction machines will have the biggest impact. Furthermore, the authors propose an A.I. Canvas to contemplate, build and assess these tools.

"Strategy" the fourth chapter, in my opinion, is where the book really shines and again with plenty of examples, shows which questions should business leaders ask themselves and how in economic terms can really a prediction machine change a business model. It gracefully touches how over time A.I. will impact a company's capital, labour, data, and learning, additionally, to the new risks it will face in an A.I. first approach.

Certain AI tools are likely to transform the boundaries of your business. Prediction machines will change how businesses think about everything, from their capital equipment to their data and people.

Finally, in chapter five "Society" the book takes time to discuss each societal concern A.I. is raising, from whose fault is if an autonomous car crashes to if it will bring the end of humans jobs. I'll let you read the answers to those questions for when you read the book.

Prediction Machines is a well-written and engaging lecture with plenty of examples. People at any job position or level of experience in the field will enjoy reading it and having this piece of research as a go-to book when thinking in business and A.I. strategy. Personally, I recommend it to anyone interested in this topic.

References

All quotes from:Agrawal, Ajay; Gans, Joshua; Goldfarb, Avi. Prediction Machines. Harvard Business Review Press.