Prediction Machines: The Simple Economics of Artificial Intelligence

Ajay Agrawal, Joshua Gans, and Avi Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business Review Press, 2018.

Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future. 

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. 

When AI is framed as cheap prediction, its extraordinary potential becomes clear:

• Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions.
• Prediction tools increase productivity--operating machines, handling documents, communicating with customers.
• Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete.

Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

蒸汽機的出現，並沒有立即提高生產力。同樣的道理，必須了解經濟學的原理、產生新的商業模式、流程的改變，才能實現人工智慧的預期改變。這一本書便是利用經濟學和管理的分析，幫助企業和組織達成此目標。

第一部分說明其精準的預測能力， 第二部分利用決策樹，說明 AI 如何幫助決策，第三部分從流程的觀點說明 AI 如何輔助部分工作，第四部分的說明公司的策略，包含可能犧牲短期利益，以提高長期的預測精準度，最後說明對社會的影響。

以精準的預測能力為例，可以做為企業決策的基準，藉以改變生產或行銷的方式，例如 Zara。