About The Book
A fascinating exploration of how computer algorithms can be applied to our everyday lives.
What should we do, or leave undone, in a day or a lifetime?
Exploring how insights from computer algorithms can be applied to our everyday lives, 'Algorithms To Live By' helps to solve common decision-making problems and illuminate the workings of the human mind. When should you switch between different tasks, and how many tasks should you take on in the first place? How much messiness should you accept? What balance of new activities and familiar favourites is the most fulfilling? When computers face constraints of time and space, they too must untangle very human questions: how to have better hunches, when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. And the solutions they've found have much to teach us.
Acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms developed for computers can be applied from finding your spouse to finding a parking spot, from organizing your inbox to understanding the workings of memory. Where you have a dilemma, they have a rule, and each fascinating algorithm turns the wisdom of computer science into strategies for human living.
About The Author
Brian Christian is the bestselling author of The Most Human Human, which was named a Wall Street Journal bestseller and a New Yorker favorite book of 2011. His writing has appeared in Wired, TheAtlantic, The Wall Street Journal , and The Paris Review, among others. Brian has been a featured guest on The Daily Show with Jon Stewart, The Charlie Rose Show, NPR's Radiolab, and the BBC, and has lectured at Google,Microsoft, SETI, the Santa Fe Institute,the Royal Institution of Great Britain, andthe London School of Economics.
Tom Griffiths is an Associate Professor of Psychology and Cognitive Science and Director of the Institute of Cognitive and Brain Sciences at the University of California, Berkeley. He has published over 150 scientific papers on a wide range of topics, including machine learning and cultural evolution in addition.