Our search has the following Google-type functionality:
If you use '+' at the start of a word, that word will be present in the search results.
eg. Harry +Potter
Search results will contain 'Potter'.
If you use '-' at the start of a word, that word will be absent in the search results.
eg. Harry -Potter
Search results will not contain 'Potter'.
If you use 'AND' between 2 words, then both those words will be present in the search results.
eg. Harry AND Potter
Search results will contain both 'Harry' and 'Potter'.
NOTE: AND will only work with single words not phrases.
If you use 'OR' between 2 single words, then either or both of those words will be present in the search results.
eg. 'Harry OR Potter'
Search results will contain just 'Harry', or just 'Potter', or both 'Harry' and 'Potter'.
NOTE: OR will only work with single words not phrases.
If you use 'NOT' before a word, that word will be absent in the search results. (This is the same as using the minus symbol).
eg. 'Harry NOT Potter'
Search results will not contain 'Potter'.
NOTE: NOT will only work with single words not phrases.
If you use double quotation marks around words, those words will be present in that order.
eg. "Harry Potter"
Search results will contain 'Harry Potter', but not 'Potter Harry'.
NOTE: "" cannot be combined with AND, OR & NOT searches.
If you use '*' in a word, it performs a wildcard search, as it signifies any number of characters. (Searches cannot start with a wildcard).
Search results will contain words starting with 'Pot' and ending in 'er', such as 'Potter'.
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.
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