Learn how evolve your business in 2020 by understanding the economics of Artificial Intelligence
Want to learn the economics of Artificial Intelligence?
Hi there lovely reader! The following is a review of the book Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi Goldfarb. We hope you like it!
Prediction Machines is a short book by three top economists with an outstanding AI foundation from the University of Toronto.
They view the current wave of Artificial Intelligence machines in the same terms as previous technological revolutions like electricity or the internet, which leads to a very insightful analysis that is refreshing, eye-opening, and highly interesting for those that are wondering how Artificial Intelligence might impact their business, and searching for the way to surf this wave to their advantage.
The high level analysis of this trend of AI results in a simple yet relevant conclusion: this new technology, at the stage it is at in 2020, is mostly an increase in the prediction capacity that we have at the moment. Machine Learning and Artificial Intelligence will impact our decision making and productivity through putting accurate, cheap, and scalable predictions at our disposal.
What does this mean? How can AI impact your business? You better read the book if you want to find out.
Using numerous examples the book also discusses how this trend has been used by the worlds top companies (Google, Amazon, Baidu, amongst others) and by small startups to enhance their profits, it speaks about complements to prediction (like judgement), what will happen to these complements with AI, the importance of Data, the always present question of whether we will loose our jobs to AI, and topics like monopolies and Artificial General Intelligence.
About the book
- Ajay Agrawal is a Professor of Strategic Management and Peter Munk Professor of Entrepreneurship at the University of Toronto’s Rotman School of Management. He is also co-founder of The Next 36 and Next AI, co-founder of the AI/robotics company Kindred, and founder of the Create Destruction Lab.
- Joshua Gans is a Professor of Strategic Management and the holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at Toronto’s Rotman School of Management. He is a frequent contributor to many major media outlets and also writes regularly at several blogs like Digitopoly.
- Avi Goldfarb is the Ellison Professor of Marketing at Toronto’s Rotman School of Management. He is also Chief Data Scientist at The Creative Destruction Lab, having much of his research covered in the regular press.
Pages: 222 pages of content and 50 pages of notes and references.
Publication year: 2018
You can see an outline of what the economics of Artificial Intelligence is about in the following video:
The following is the official website of the book: https://www.predictionmachines.ai/.
Who is this book for?
This book has no technical baggage whatsoever, so anybody who runs a business can understand it and retrieve pretty clear conclusions from it. At no point it goes into the mathematical or algorithmic details of Machine Learning, or Data Science, so it should be understandable for everybody.
It is oriented mostly for business owners, and entrepreneurs that want to see how AI can impact their environment, however, people with experience in the field of AI, Data Scientist and engineers will also enjoy it, as it treats these ‘new’ technologies in a refreshing and inspiring way.
Summary of Prediction Machines and the Economics of Artificial Intelligence
Fun, full of examples, and very insightful and practical Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon that Artificial Intelligence is opening up. It’s impact will be profound, so you better be ready for it. For us, there is no better way to do this than by reading this profound yet surprisingly simple book. Enjoy it!
Lastly, you can buy the book on amazon here:
Prediction Machines: The simple economics of Artificial Intelligence
- Hardcover Book
- Agrawal, Ajay (Author)
- English (Publication Language)
- 272 Pages - 04/17/2018 (Publication Date) - Harvard Business Review Press (Publisher)
Lastly, this book is a great complement to other non-technical books like Weapons of Math Destruction or if you are looking to get a little bit technical but with care, books like The Hundred-Page Machine learning book. You can find all of them here:
Enjoy, thanks for reading How to Learn Machine Learning, and take good care!
Other resources you might like are:
Other resources around this topic are:
- Weapons of Math Destruction by Cathy O’Neil: A book similar to this one, covering the discrimination and issues that arise from the un-ethical use of AI.
- Medium Post: Bias in Artificial Intelligence.
Til’ the next time!