Cover image for The efficiency paradox : what big data can't do
The efficiency paradox : what big data can't do
Tenner, Edward, author.
Personal Author:
First edition.
Publication Information:
New York : Alfred A. Knopf, 2018.

Physical Description:
xxxi, 282 pages ; 25 cm
"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 ever 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 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."--
Preface: The seven deadly sins of efficiency : why it is still a work in progress -- From mill to platform : how the nineteenth century redefined efficiency and the twenty-first has transformed it -- The failed promise of the information explosion : how the quest to measure elite science empowered populist culture -- The mirage of the teaching machine : why learning is still a slog after fifty years of Moore's Law -- Moving targets : what geographic information can't do -- The managed body : why we are still waiting for RoboDoc -- Inspired inefficiency : how to balance algorithm and intuition.

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