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Bitwise: A Life in Code


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Bitwise illustration

Credit: Garry Killian

In 1960, physicist Eugene Wigner pondered "The Unreasonable Effectiveness of Mathematics in the Natural Sciences," wondering why it was that mathematics provided the "miracle" of accurately modeling the physical world. Wigner remarked, "it is not at all natural that 'laws of nature' exist, much less that man is able to discover them." Fifty years later, artificial intelligence researchers Alon Halevy, Peter Norvig, and Fernando Pereira paid homage to Wigner in their 2009 paper "The Unreasonable Effectiveness of Data," an essay describing Google's ability to achieve higher quality search results and ad relevancy not primarily through algorithmic innovation but by amassing and analyzing orders of magnitude more data than anyone had previously. The article both summarized Google's successes to that date and presaged the jumps in "deep learning" in this decade. With sufficient data and computing power, computer-constructed models obtained through machine learning raise the possibility of performing as well if not better than human-crafted models of human behavior. And since machines can craft such models far more quickly than humans, data-driven analytics and machine learning appear to promise more efficient, accurate, and rich models—at the cost of transparency and modularity, hence why such systems are frequently seen as black boxes.

Ironically, Halevy, Norvig, and Pereira's insights were driven by the ineffectiveness of mathematics in the life and social sciences. It is, I hope, an undisputed contention that models of biological and human behavior have nowhere near as strong the predictive power as do physical laws. While little tenable survives of Aristotle's physics, the fourfold humoural classification of ancient Greece endures through the Jungian temperament classifying systems employed by the majority of Fortune 500 companies. Where such folk theories still prevail, there is the potential for automated computer models to do better than our own. We do not need machine learning for physical laws, only for phenomena so apparently complex we lack an unreasonably effective model of them.


 

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