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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.
Overall, Prediction Machines is a very worthwhile book, and the authors do an admirable job of simplifying some difficult concepts. However, AI is clearly a double-edged sword, and Agrawal, Gans and Goldfarb, like Mark Zuckerberg but unlike Elon Musk, choose to focus almost exclusively on its positive aspects (for the other side of the coin, see Musk in the documentary “Do You Trust This.
The power of cheap prediction: how AI is transforming tasks, business models, and strategy. This book by University of Toronto professors shows how prediction machines are changing human judgment.
Online prediction scenarios are for cases when you want to generate predictions on a one-by-one basis for each example independent of the other examples, in a low-latency environment. For example, you could use predictions to make immediate decisions about whether a particular transaction is likely to be a fraudulent transaction. AWS Documentation Amazon Machine Learning Developer Guide.
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But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.
The ideas here are adapted from our forthcoming book “Prediction Machines: The Simple Economics of Artificial Intelligence.” (Harvard Business School Press, April 2018).