June 24, 2018

Prediction Machine Eyes

Recently I read Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, three professors from UofT’s Rotman School of Management. I recommend this book to anyone who wants to go ‘beyond the headlines’ with respect to what impact on the world machine learning & AI will have.

Topic

“Where others see transformational new innovation, we see a simple fall in price.”

The key idea the book revolves around is that machine learning & AI have brought about a dramatic fall in the price of prediction. The authors argue this fall in price will lead to the emergence of new business models (similar to how new business models emerged as Google search became popular), and it will also increase the value of other things (e.g. sensors which accurately capture data will become more valuable).

Review

This may be the best book yet I have read in the ‘machine learning/AI/robots will transform our world’ genre. Part of the reason why is that Prediction Machines does not try to do too much. The authors focus on a few key ideas (as mentioned above), and then carefully evaluate the ramifications of them. The authors’ writing style is clear (the chapter-by-chapter bullet points help with this), and back their arguments up with numerous real-world examples. The book is similar in some ways to Martin Ford’s Rise of the Robots, and the overall style is reminiscent of Robert Shiller’s Animal Spirits.

Two things I was not wild about. I found the section ‘Part 3: Tools’ a bit dry, and wish the authors had gone into a little bit more detail about how machine learning actually works (potentially via an appendix).

Best Bits

A few sections/arguments/examples I found interesting:

  • Rating agencies’ models before the financial crisis did not sufficiently incorporate how housing prices are correlated across regions. “Machine learning enables predictions based on unanticipated correlations”, and this feature could have been helpful at the time. (pg. 37)
  • “The value of substitutes to prediction machines, namely human prediction, will decline. However, the value of complements, such as the human skills associated with data collection, judgment, and actions, will become more valuable.” (pg. 81)
  • “The recent developments in AI and machine learning have convinced us that this innovation is on par with the great, transformative technologies of the past: electricity, cars, plastics, the microchip, the internet, and the smartphone”. (pg. 155)
  • The authors cite an interesting example of how in the 1930s a new strain of higher yielding corn took a very long time to become widely used in some states (e.g. Texas, Alabama). Part of the reason for this was that farms in these states were smaller & less profitable, making experimentation on new corn varieties hard to justify. The authors argue that the large profit margins of firms like Google/Facebook are enabling them to experiment broadly with AI techniques, and “reap huge rewards from successful experiments by applying them across a wide range of products operating at large scale”. (pg. 160)