Books of Interest
Website: chetyarbrough.blog
Prediction Machines (The Simple Economics of Artificial Intelligence)
By: Ajay Agrawal, Joshua Gans, Avi Goldfarb
Narrated By: U Ganser
Authors, from left to right: Ajay Agrawal (Professor at Rotman School of Management @ University of Toronto), Joshua Gans (Chair in Technical Innovation and Entrepreneurship at the Rotman School), Avi Goldfarb (Chair in Artificial Intelligence, Healthcare, and Marketing at the Rotman School).
This is a tedious book about the mechanics of artificial intelligence and how it works, i.e., at least in its early stages of development.
Like in the early days of computer science, the phrase “garbage in, garbage out” comes to mind. “Prediction Machines” makes the point that A.I. is software creation for “…Machines” that are only as predictive as the ability of its programmers. Agrawal, Gans, and Goldfarb give a step-by-step explanation of a programmer’s thought process in creating a predictive machine that does not think but can produce predictions.
The obvious danger of A.I. is that users may believe computers think when in fact they only reproduce what they are programmed to reveal.
They can be horribly wrong based on misrepresentation or misunderstanding of the real world by programmers who are trapped in their own beliefs and prejudices. A. I.’s threat rests in the hands of those who view it as a “god-like” oracle of truth when it is only a tool of human beings.
The horrible and unjust murder of the United Health Care executive reminds one of how critical it is for all business managers to be careful about how A.I. is used and the way it affects its customers.
“Prediction Machines” is a poorly written book that illustrates how a programmer methodically organizes information with decisions and actions triggered by A.I.’ users who believe machines can be programmed to think. A.I. machines do not think!
Managers must be alert and always inspect what they expect.
It is critically important for users of A.I. to continually measure the human results of “A.I. based” decisions. Users must be educated to understand A.I. is a tool of humanity, not an oracle of truth. A.I. must be constantly reviewed and reprogrammed based on its positive contribution to society.
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