Prediction Machines: The Simple Economics of Artificial Intelligence

TLDR;1. AI is mostly prediction.2. Ubiquitous prediction by computers is very cheap.3. The value complement is decision making which will rise in demand.That is about the sum of economics in this book. Most of the content is padding, providing a general overview of issues around AI in this highly prediction automated world.While the authors acknowledge that prediction is not new, they imply that AI is going to hufely change the volume of prediction, despite the fact that almost every human activity involves prediction of some sort, including well-established methods that most businesses already use. AI prediction using deep learning has certainly upped the bar in some domains, but traditional methods are not going to disappear or be bested by AI.This book seems to be part of the current AI hype cycle, which will inevitably lead to disillusion as current techniques prove not the magic pixie dust that many hope for. AI as a prediction tool will expand the domain, especially in pattern recognition, but how much qualitative change will occur is unclear. Most likely we will see the highly discussed techniques deployed, although if it turned out that most of the benefit was natural language processing and face and object recognition, consumers won't be applying more decision making, and nor will companies. Facebook's recent problems with data suggest that their development of AI isn't having much effect on improving management's decision making.The flaws in this book are similar to historically breathy books on automation, computers in the workplace, and even general artificial intelligence. I would accept that their basic premise is largely correct today and for the near future, accept the economic arguments, and then read other books on the specifics.

The authors, three economists from the University of Toronto, do a great job of demystifying artificial intelligence by examining it through the lens of standard economic theory. The authors are clear that they are not examining AGI (artificial general intelligence), but rather the artificial intelligence produced by algorithms in common, and ever-increasing, use today. When you go online and get a recommendation for a product or you ask a question of Alexa, Siri, or Google, the recommendation or answer that you get is produced by algorithms (prediction machines in the authors' definition).The authors' basic premise is that these prediction machines have become, and are becoming, so cheap that their use has expanded, and will continue to expand, dramatically across a range of businesses. They analogize this expansion to the expansion in the use of electricity or cars during the early parts of the last century. The processes for how work was done and the skills needed to do it dramatically changed the number and type of jobs required by the economy. Jobs were both created and destroyed. It took time for this to occur. The authors expect the same effect from the prediction machines.The book looks at the possible effect on the types of jobs at which humans will excel. Judgement will become more valuable to augment the input of artificial intelligence. Jobs will have to be redesigned. Work flows altered.Strategy in the C-suite will be impacted by artificial intelligence. The occupants of top management positions will have to adjust. The book suggests how.After reading this book, I read the July/August edition of MIT Technology Review, which states on the cover "AI and robots are wreaking economic havoc. We need more of them." There are a number of articles in the magazine that paint a cautionary picture of the prediction machines ("Confessions of an accidental job destroyer"). The authors of Prediction Machines recognize the potential adverse consequences and social risk that the current edition of MIT Technology Review addresses so the book and the magazine are not in conflict.If you're interested in artificial intelligence and want to read a book that examines the topic dispassionately, then I recommend it highly. The authors did a fine job of making the topic highly accessible.

**updated this score. the author made the point the book is not intended for data scientists so maybe my initial review was a bit harsh**I'm a data scientist and reader of Gans blog so thought I'd give this book a try. The basic premise is that machine learning / AI lowers cost of predictions and this will change how we do business. Just like how the decrease cost in electricity changed how we structure the economy so will AI.So how will it change how we structure our economy? Well the authors basically spend the rest of the book using anecdotes as to how we can possibly change processes ect. Some of these are interesting. But there is no over arching theme among them besides the fact that we will use predictions. Overall it seems like an interesting conversations to have with coworkers or a blog post not enough there to constitute a 200 page book.

This book is about a simple idea - as the cost of prediction declines due to AI, the value of substitues decreases (human prediction) and the value of complements (data and judgement) increases.The book creates a new point of view on how decison making will change as AI becomes more pervasive. The model showing how the value of human decision making declines as machine decision making becomes more popular, and the implication that this drives up the cost of skills associated with human activities espcially the role and value of judgement. This gives us an insight to how jobs and roles will change - those with more ownership of data, and those where judgement is key will be more valuable to society than those that process data.More concerning is that again this technology has the potential to place much more capital (intellectual and otherwise) into the hands of a small number of people. We need to think more about the ethics, controls and regulations needed to ensure that gains from this technology are inclusive to everyone.

I’ve been putting this book off for a little while, and I’m not really sure why. I think I thought it was going to be slow going, but it turns out that I whizzed through it in a day or so. I wasn’t too worried about the subject matter, even though it’s non-fiction about artificial intelligence from the Harvard Business Review press.This was actually recommended to me by a client of mine, Emmanuel Fombu, who specialises in writing and talking about the future of healthcare. It was a good recommendation on his part, especially because there is some stuff here that focusses directly on the impact of artificial intelligence in the healthcare industry. But it’s not just healthcare that’s covered here, and indeed I think the authors did a good job of covering a wide variety of different use cases.It was also interesting to read right now because my current “bedtime book” is The Enigma by Andrew Hodges, a biography of Alan Turing. His work had a huge impact on the development of artificial intelligence and what the authors here call “prediction machines”, and indeed one of the main tests that an AI must pass if it’s to be able to pass as a human is named after Turing: the Turing Test.All in all then, if you have an interest in AI and the way in which it’s changing our society, read this.

An enjoyable and readable introduction to the potential of AI in business.Filled with interesting and thought-provoking concepts, some of the graphics are better written about than drawn, but I thoroughly enjoyed this, and see many applications to help shape my future thinking at work.A comprehensive good read.

Engaging book, well written but a bit too generic and high level. The focus is on examples of how AI could help companies but I found the examples generic and not deep enough. Entertaining reading but don’t expect more than that.

The central idea that machine learning uses data to predict is strong. It would make an absorbing paper. Once stretched to a book, diluted with some management speak, this book is only useful to an ageing middle manager with no IT skills. Not worth the cover price, nor the time investment.