A fun, timely, and optimistic treatment of the big ideas that every citizen of the 21st century should know if they want to understand how intelligent machines operating on massive data sets are changing the world around them, and how they can use this knowledge to make better decisions in their own lives.
Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world of the 21st century in the same way that the Industrial Revolution remade the world of the 19th century.
In the face of all these changes, we believe that there is a simple premise worth keeping in mind. If you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. Our book will teach you that language-but in an unconventional way, anchored around stories rather than mathematics.
You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability, and better thinking. Along the way, you'll see how these same ideas are playing out in the modern age of big data and intelligent machines-and how these technologies will soon help you to overcome some of your built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.
Author Biography
Nick Polson (Author)
Nick Polson is Professor of Econometrics and Statistics at the Chicago Booth School of Business. Nick is a Bayesian statistician involved in research in machine intelligence, deep learning, and computational methods for Bayesian inference. He has developed a number of new algorithms and applied them across a variety of fields, including finance, economics, transportation and applied statistics. Nick was born in England, studied maths at Worcester College, Oxford; and obtained a PhD in Bayesian Statistics. He regularly speaks to large audiences in the US, UK and the rest of Europe.
James Scott (Author)
James Scott is Associate Professor of Statistics at the University of Texas at Austin. James is a statistician and data scientist who studies Bayesian inference and computational methods for big data. His has collaborated with scientists in a wide variety of fields, including health care, nuclear security, linguistics, political science, finance, management, infectious disease, astronomy, neuroscience, transportation and molecular biology. He has also worked with clients across many different industries, from tech startups to large multinational firms. James lives in Austin, Texas with his wife, Abigail.
His academic research has been featured in The New York Times, the Washington Post, ABC, NBC, Fox, the BBC UK, BBC World News, Radio 4, The Guardian and many other prominent media outlets.
