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Edward Tenner with The Efficiency Paradox: What Big Data Can’t Do
Edward Tenner is a distinguished scholar of the Smithsonian’s Lemelson Center for the Study of Invention and Innovation and a visiting scholar in the Rutgers University Department of History. He was a visiting lecturer at the Humanities Council at Princeton, and has held visiting research positions at the Institute for Advanced Study and the University of Pennsylvania. His essays and reviews have appeared in The New York Times, The Washington Post, The Wall Street Journal, The Atlantic, The Wilson Quarterly, and Forbes.com, and he has given talks for many organizations, including Microsoft, AT&T, the National Institute on White Collar Crime, the Smithsonian Associates, and TED. His book, Why Things Bite Back: Technology and the Revenge of Unintended Consequences, written in part with a Guggenheim Fellowship, has been translated into German, Japanese, Chinese, Italian, Portuguese, and Czech.
About The Efficiency Paradox: What Big Data Can’t Do
A bold challenge to our obsession with efficiency–and a new understanding of how to benefit from the powerful potential of serendipity
Algorithms, multitasking, the sharing economy, life hacks: our culture can’t get enough of efficiency. One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than we ever have before. There is no doubt that we’re performing at higher levels and moving at unprecedented speed, but what if we’re headed in the wrong direction?
Melding the long-term history of technology with the latest headlines and findings of computer science and social science, The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of digital platforms can in fact lead to wasted efforts, missed opportunities, and above all an inability to break out of established patterns. Edward Tenner offers a smarter way of thinking about efficiency, revealing what we and our institutions, when equipped with an astute combination of artificial intelligence and trained intuition, can learn from the random and unexpected.