Prediction Machines: A book review by Bob Morris

Prediction Machines: The Simple Economics of Artificial Intelligence
Ajay Agrawal, Joshua Gans, and Avi Goldfarb
Harvard Business Review Press (April 2018)

“Success in creating AI would be the biggest event in human history.” Stephen Hawking

According to Ajay Agrawal, Joshua Gans, and Avi Goldfarb,”Our first key insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence — prediction.” Other key insights are shared on Page 3 in the Introduction and later developed throughout their lively and eloquent narrative.

I agree with them that their book does not provide a recipe for success in the AI economy. As they correctly suggest, their emphasis is on trade-offs: “More data means less privacy. More speed means less accuracy. More autonomy means less control…The best strategy for your company or career or country will depend on how you weigh each side of every trade-off.” Of course, AI can help to ensure that the measurements are accurate when obtained even as the nature and extent of each trade-off are certain to change. That is especially true now, when the global marketplace is more volatile, more uncertain, more complex, and more ambiguous than at any prior time that I can recall.

According to Agrawal, Gans, and Goldfarb, “PREDICTION is the process of filling in missing information. Prediction takes information you have, often called ‘data,’ and uses it to generate information you don’t have.” The more and better the information you have, the more and better information will be what you add to it. Think of the information available as the foundation, the context, the frame-of-reference for  the prediction formulated as well as its degree of probability.

The recent advances in AI have recast translation as a prediction problem. “For example, over 500 million people in China already use a deep learning-powered service developed by iFlytek to translate, transcribe, and communicate using natural language. Landlords use it to communicate with tenants in other languages, hospital patients use it to communicate with robots for directions, doctors use it to dictate a patient’s medical details, and drivers use it to communicate with their vehicles. The more AI is used, the more data it collects, the more it learns, and the better it becomes.With so many users, the AI is improving rapidly.”

Just as a liquid assumes the shape of its container, AI can assume the shape of however many ways it is put to use. At some point, the Hawkin observation will need to be revised: “Success in creating AI that can, in turn, create improved AI would be the biggest event in human history.”


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