How intelligent machines operating on massive data sets are changing the world around you, and how you can use this knowledge to make better decisions in your own life. Essential reading for every citizen of the 21st Century.
Two leading data scientists offer an up-close and user-friendly look at artificial intelligence: what it is, how it works, where it came from and how to harness its power for a better world.
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 in the twenty first century in the same way that the Industrial Revolution remade the world in the nineteenth.
AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language but in an unconventional way, anchored in stories rather than equations.
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.
About the Authors
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 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.
"There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines." -- Steven Levitt, bestselling co-author of Freakonomics
"Entertaining and persuasive. The book's goal is to explain how artificial intelligence delivers its incredible results, and Polson and Scott are like a pair of excitable mechanics lifting up the bonnet of a sports car. This is a passionate book, and it is a model of how to make data science accessible and exciting." -- James McConnachie * The Sunday Times *
"Grounding AI in tried-and-true methods makes it seem less alien: Computers are simply faster ways to solve familiar problems. Hence the book's title, a portmanteau of AI and IQ-the point being that we need both." -- Sam Kean * Wall Street Journal *
"In an entertaining primer, two academic data scientists put the case for the defence on artificial intelligence, and show how we can harness its power for a better world." * The Times *
"At last, a book on the ideas behind AI and data science by people who really understand data. Cutting through the usual journalistic puff and myths, they clearly explain the underlying ideas behind the way that troughloads of data are being harnessed to build the algorithms that can carry out such extraordinary feats. But they are also clear about the limitations and potential risks of these algorithms, and the need for society to scrutinise and even regulate their use. A real page-turner, with fine stories and just enough detail: I learned a lot." -- David Spiegelhalter, Winton Professor of the Public Understanding of Risk, University of Cambridge