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AIQ: How artificial intelligence works and how we can harness its power for a better world Hardcover – 7 Jun 2018
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"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)
About the Author
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.
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The authors are Professors of Statistics, They are data scientists . The book does not avoid concerns about AI. People are anxious about the effect on their jobs, data privacy, fake news, and so on. Some are worried that robots may become self-aware and begin to rule us. Will we have to face a jobless world? Such questions are examined by the authors.
Polson teaches at Chicago University, and Scott at Texas University, Austen. In this account of A1 they recount the stories of the heroes of statistics who made advances in A1 possible. Henrietta Leavitt is one. She was an American astronomer who in 1912:worked out how to measure the true brightness of stars. According to the tech billionaire, Musk, A1 poses more risk than North Korea's nuclear weapons. Machines he and others warn are about to enslave us. The authors of this book disagree.
The authors argue persuasively that such views as Musk's are based on science fiction like RoboCop. They are optimistic basing their views on probability, big data and machine learning. They stress that machines are not humanoid intelligences. AI lmachines do not understand what they are doing. In fact they need us more than we need them. The book details how artificial intelligence gets its amazing results. A good example is speech recognition. A few years ago even dedicated systems were poor and very frustrating. Today it is first rate. Alexa and Home are excellent and easy to use. The authors explain how this has been achieved.
The principles involved are now being used to transform e-commerce, China's Alibaba company is an outstanding example. Google's neural network, Inception, performs 1,5 billion calculations in 0.0001seconds. Driverless cars and even flying taxis are on the road or under testing.
Health care is being transformed by AI. He Already malignant moles can be distinguished from benign ones. Eye disease can be detected without human intervention. It is possible in the future thar A1 will be able to predict conditions. There is concern that radiologists will soon be redundant, Machines it is said will enable faster and cheaper diagnosis; nuances may be seen that humans miss, and so on.
There seems little doubt that machine learning will alter many fields but whether this makes humans redundant is not so clear. We need to remember that deep learning is broader. Instead of wondering if AI can replace a job we should ponder whether AI can replace humans at a specific task. Most jobs involve multi tasks. Spreadsheets have not made accountants redundant. Radiologists far more than analyse many images. Let AI handle one of their tasks and free up more time for the radiologist to carry out more complex tasks. This has already happened in other fields of medicine. As a result, nurses now do more procedures that doctors did previously. It is also worth remembering that technological change takes longer than you imagine. Moreover, automation may well lead to increased demand by making procedures such as radiology cheaper, permitting its benefits to be spread more widely.
This excellent book pays little attention to the political and ethical consequences of A1 but it is a superb model of how-to make data science accessible to the general public, and at the same time make it exciting. Here for example is a very brief explanation of what AI is: AI is really an algorithm, a set of step by step instructions that are very explicit so a computer can follow them. Chain many algorithms together and you get AI. Its algorithms deal with probabilities not certainties. This is a simple but prime example of the clarity of the writing in this book. It is also witty ( a robot got stuck in a shower forever because on the shampoo bottle the algorithm said, Lather, Rinse, Repeat) as well as profound. It is also full of examples of the use of machine intelligence.
AI is here like it or not. It can only get bigger and better. It will bestow immense benefits as well as problems. Dangers to privacy in particular will need watching. In seven chapters this splendid account shows what we need to understand if we want to play an informed role in the debate about AI.
Very highly recommended.