- 出版社: Harvard Business Review Press (2018年4月17日)
- 精装: 272页
- 语种： 英语
- ISBN: 1633695670
- 条形码: 9781633695672
- 商品尺寸: 15.9 x 1.9 x 24.1 cm
- 商品重量: 490 g
- ASIN: 1633695670
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- 第32位 - 图书 > 进口原版书 > Computers & Technology（计算机与科技） > Computer Science（计算机科学） > Artificial Intelligence > Human Vision & Language Systems
Prediction Machines: The Simple Economics of Artificial Intelligence (英语) 精装 – 2018年4月17日
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 Relations
One 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 Management
Named 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 News
Advance 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."
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.
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The authors' basic premise is that these prediction machines have become, and are becoming, so cheap that their use has expanded, and will continue to expand, dramatically across a range of businesses. They analogize this expansion to the expansion in the use of electricity or cars during the early parts of the last century. The processes for how work was done and the skills needed to do it dramatically changed the number and type of jobs required by the economy. Jobs were both created and destroyed. It took time for this to occur. The authors expect the same effect from the prediction machines.
The book looks at the possible effect on the types of jobs at which humans will excel. Judgement will become more valuable to augment the input of artificial intelligence. Jobs will have to be redesigned. Work flows altered.
Strategy in the C-suite will be impacted by artificial intelligence. The occupants of top management positions will have to adjust. The book suggests how.
After reading this book, I read the July/August edition of MIT Technology Review, which states on the cover "AI and robots are wreaking economic havoc. We need more of them." There are a number of articles in the magazine that paint a cautionary picture of the prediction machines ("Confessions of an accidental job destroyer"). The authors of Prediction Machines recognize the potential adverse consequences and social risk that the current edition of MIT Technology Review addresses so the book and the magazine are not in conflict.
If you're interested in artificial intelligence and want to read a book that examines the topic dispassionately, then I recommend it highly. The authors did a fine job of making the topic highly accessible.
I'm a data scientist and reader of Gans blog so thought I'd give this book a try. The basic premise is that machine learning / AI lowers cost of predictions and this will change how we do business. Just like how the decrease cost in electricity changed how we structure the economy so will AI.
So how will it change how we structure our economy? Well the authors basically spend the rest of the book using anecdotes as to how we can possibly change processes ect. Some of these are interesting. But there is no over arching theme among them besides the fact that we will use predictions. Overall it seems like an interesting conversations to have with coworkers or a blog post not enough there to constitute a 200 page book.
1. AI is mostly prediction.
2. Ubiquitous prediction by computers is very cheap.
3. The value complement is decision making which will rise in demand.
That is about the sum of economics in this book. Most of the content is padding, providing a general overview of issues around AI in this highly prediction automated world.
While the authors acknowledge that prediction is not new, they imply that AI is going to hufely change the volume of prediction, despite the fact that almost every human activity involves prediction of some sort, including well-established methods that most businesses already use. AI prediction using deep learning has certainly upped the bar in some domains, but traditional methods are not going to disappear or be bested by AI.
This book seems to be part of the current AI hype cycle, which will inevitably lead to disillusion as current techniques prove not the magic pixie dust that many hope for. AI as a prediction tool will expand the domain, especially in pattern recognition, but how much qualitative change will occur is unclear. Most likely we will see the highly discussed techniques deployed, although if it turned out that most of the benefit was natural language processing and face and object recognition, consumers won't be applying more decision making, and nor will companies. Facebook's recent problems with data suggest that their development of AI isn't having much effect on improving management's decision making.
The flaws in this book are similar to historically breathy books on automation, computers in the workplace, and even general artificial intelligence. I would accept that their basic premise is largely correct today and for the near future, accept the economic arguments, and then read other books on the specifics.