Physicist Niels Bohr observed: “Prediction is very difficult, especially if it’s about the future!” Despite that warning, scientists, politicians, journalists, doctors, economists--professionals in nearly every knowledge domain--try to anticipate what’s in store for us using everything from divination, “gut feelings,” and “educated guessing” to complex statistical modeling, machine learning, and artificial intelligence (AI).

Data science expert and educator Stylianos Kampakis has written a valuable and instructive book on prediction, subtitled the history and future of data science and artificial intelligence, not to tell readers what might happen next but to explain the tools of prediction under uncertainty: their history, utility, and potential impact. He calls uncertainty an unavoidable “enemy” that humanity has been fighting to control using logic, science, and reason, recognizing that it can’t be eliminated, only understood and reduced.

The opening chapter presents a short history of uncertainty, citing the development of statistical science; noting that there are different kinds and levels of uncertainty; and providing examples from weather, polling, medicine, economics, life choices, and investing. Subsequent chapters discuss the predictive power and challenges of logic, inductive reasoning, probability, and hypothesis testing, including an obligatory yet readable review of Bayesian versus frequentist analyses.

The second half of the book tends more philosophical, considering the nature of causality, human perception and interpretation of uncertainty, and the limits of prediction, including the shortcomings of machine learning, simulations, and trust technologies like blockchain. Throughout, the author highlights views about probability and uncertainty from historical practitioners and thinkers, including Locke, Bacon, Shannon, Fisher, Laplace, Gauss, Pascal, and Tukey, to name but a few luminaries in this domain.

Understanding uncertainty, and how to describe it and manage it using modern data analytics tools and methods, is increasingly critical in today’s social, economic, and scientific endeavors. Kampakis’s book clearly and readably covers the essence of uncertainty and the human efforts to address it, written for both professional data scientists and anyone attempting to predict life’s unknowable and unexpected outcomes.

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