PDF Ebook Prediction Machines: The Simple Economics of Artificial Intelligence, by Joshua Gans Avi Goldfarb

PDF Ebook Prediction Machines: The Simple Economics of Artificial Intelligence, by Joshua Gans Avi Goldfarb

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Prediction Machines: The Simple Economics of Artificial Intelligence, by Joshua Gans Avi Goldfarb

PDF Ebook Prediction Machines: The Simple Economics of Artificial Intelligence, by Joshua Gans Avi Goldfarb

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Pressestimmen

Named one of the "Top Ten Technology Books of 2018" by Peter High, Forbes.com"Compared with the amount of ink spilled over the prospects of artificial general intelligence and all its accompanying fears--the singularity!--there's been much less attention to the smaller changes already happening in the realm of A.I. and their quite profound economic implications. Enter Prediction Machines." -- The New York Times"...a readily understandable guide to artificial intelligence and the immensely consequential effects it could have on our economy, our society and our political system." -- Robert E. Rubin, former U.S. Treasury secretary and co-chair Emeritus, Council on Foreign RelationsOne of "10 Great Reads For The Summer" -- Dave McKay, President & CEO at RBC"Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi Goldfarb. This 2018 book...on the timely topic of AI - tops my summer reading list. The authors...offer a compelling framework for mapping out the likely impact of AI on economies in the decades ahead. -- BlackRock Investment ManagementNamed a Hardcover Non-Fiction Bestseller by the Globe & Mail (Canada)"An excellent book on the economics of Artificial Intelligence. Steeped in both economics and AI/ML, this book steers clear of hype (or anti-hype), applying standard economic concepts to a rapidly emerging phenomenon. The book is geared to business readers not economists or policymakers but it has a lot to offer to everyone... Highly recommended." -- Jason Furman, former Chair of President Obama's Council of Economic Advisors on Goodreads"This is a timely book, well written, and accessible putting forward their insights, and is well worth reading." -- Irish Tech NewsAdvance Praise for Prediction Machines Lawrence H. Summers, Charles W. Eliot Professor, former president, Harvard University; former secretary, US Treasury; and former chief economist, World Bank--"AI may transform your life. And Prediction Machines will transform your understanding of AI. This is the best book yet on what may be the best technology that has come along."Susan Athey, Economics of Technology Professor, Stanford University; former consulting researcher, Microsoft Research New England--"Prediction Machines is a path-breaking book that focuses on what strategists and managers really need to know about the AI revolution. Taking a grounded, realistic perspective on the technology, the book uses principles of economics and strategy to understand how firms, industries, and management will be transformed by AI."Dominic Barton, Global Managing Partner, McKinsey & Company--"Prediction Machines achieves a feat as welcome as it is unique: a crisp, readable survey of where artificial intelligence is taking us separates hype from reality, while delivering a steady stream of fresh insights. It speaks in a language that top executives and policy makers will understand. Every leader needs to read this book."Kevin Kelly, founding executive editor, Wired; author, What Technology Wants and The Inevitable--"This book makes artificial intelligence easier to understand by recasting it as a new, cheap commodity--predictions. It's a brilliant move. I found the book incredibly useful."

Über den Autor und weitere Mitwirkende

Ajay Agrawal is Professor of Strategic Management and Peter Munk Professor of Entrepreneurship at the University of Toronto's Rotman School of Management. He is also cofounder of The Next 36 and Next AI, cofounder of the AI/robotics company Kindred, and founder of the Creative Destruction Lab. Ajay conducts research on technology strategy, science policy, entrepreneurial finance, and the geography of innovation.Joshua Gans is Professor of Strategic Management and the holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at Toronto's Rotman School of Management. Gans is a frequent contributor to outlets like the New York Times, Harvard Business Review, Forbes, Slate, and the Financial Times. Joshua also writes regularly at several blogs including Digitopoly.Avi Goldfarb is the Ellison Professor of Marketing at Toronto's Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, a Fellow at Behavioral Economics in Action at Rotman, and a Research Associate at the National Bureau of Economic Research. His research has been widely covered in the popular press.

Produktinformation

Gebundene Ausgabe: 250 Seiten

Verlag: Ingram Publisher Services (3. April 2018)

Sprache: Englisch

ISBN-10: 1633695670

ISBN-13: 978-1633695672

Größe und/oder Gewicht:

15,9 x 1,9 x 24,1 cm

Durchschnittliche Kundenbewertung:

4.3 von 5 Sternen

4 Kundenrezensionen

Amazon Bestseller-Rang:

Nr. 7.383 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

Having worked for global eBusiness Agencies (iXL, Pixelpark) of the first peak of the internet innovation cycle 20 years ago I’m still excited about the ongoing disruptive power of digitalization.Using a self-driving Tesla-car, Google Maps or Alexa or just your credit card everyone has had or will soon have his Artificial Intelligence (AI) moment. Therefore, everybody is speculating about the chances but also the threats by AI. But who gives substantial orientation? After reading „Life 3.0: Being Human in the Age of Artificial Intelligence“ by Max Tegmark (a very helpful hint by my former Pixelpark colleague Barbara Daliri Freyduni) I followed the recommendation of serial founder and AI-expert Stephan Uhrenbacher (FLIO.com Digital Airports, Qype, 9flats.com) to read the current bestseller “Prediction Machines” from Ajay Agrawal, Joshua Gans and Avi Golfarb. As a fellow at Creative Destruction Lab (CDL), a high profiled accelerator focused on Machine Learning in Toronto, Stephan got in personal touch with these three economists, who have cofounded CDL, and supported their publication.Based on various empiric studies and their personal insights from working with AI-pioneers at CDL Agrawal, Gans and Golfarb explain why prediction machines makes predictions better, faster and increasingly cheaper and become extremely powerful but as well with specific limitations. They conclude: The combination of humans and machines generates best results by compensating the differing weaknesses of both. How does such a collaboration translate into a business environment? In two ways: (1) The machine provides an initial prediction that humans can combine and use for their own assessment, judgement and decision making or (2) The machines provide parallel or afterwards a second opinion which helps e.g. higher-level managers to ensure high performance of their employees.Agrawal, Gans Golfarb reflect extensively also the different types of risks carried by AI, but they inspire in a pragmatic way to think about the new opportunities in an open way.A must-read for everybody who wants to understand the economic logic of AI-power behind the technical development.

It is hard to like/dislike this book. It has really strong and nice insights about machine learning field. However, some parts seem technically too abstract which may mislead the reader.

Das Buch "Prediction Machines" bietet einen guten Überblick der Leistungen und Einsatzmöglichkeiten künstlicher Intelligenz aus wirtschaftlicher Perspektive. Technische Fragestellungen und Details werden in diesem Buch nicht beleuchtet.Das Buch ist meiner Meinung nach gut geeignet für Leute, die sich einen ersten (nichttechnischen) Überblick über Künstliche Intelligenz verschaffen möchten und/oder besonderes Interesse an wirtschaftlichen Fragestellungen im Zusammenhang mit KI haben.Die Autoren stellen die Anwendungen künstlicher Intelligenz als Vorhersageautomaten dar, die bestehende Muster erkennen und daraus Informationen für die Zukunft ableiten können, deren Einordnung jedoch weiterhin menschlicher Überprüfung/Intelligenz bedarf. Das Buch enthält zahlreiche Anwendungsbeispiele für den Einsatz künstlicher Intelligenz, beispielsweise das automatische Screening von Bewerbungen, die Abschätzung des Wechselrisikos von Kunden oder die Kategorisierung von Objekten auf Fotos.Insgesamt bietet das Buch einen soliden ersten Überblick über das Thema, geht dabei jedoch wenig in die Tiefe.

Very informative

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