Prediction Machines audiobook cover - The Simple Economics of Artificial Intelligence

Prediction Machines

The Simple Economics of Artificial Intelligence

Ajay Agrawal, Joshua Gans & Avi Goldfarb

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ScienceTechnology & the Future

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Prediction Machines
Core Concept of AI+
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Quiz — Test Your Understanding

Question 1 of 5
According to the text, how should we primarily understand the function of artificial intelligence?
  • A. As a tool to perfectly replicate human consciousness and emotional intelligence.
  • B. As a technology focused on perfecting the ability to make predictions.
  • C. As a system designed to replace human physical labor in manufacturing.
  • D. As an advanced calculator that relies exclusively on traditional regression models.
Question 2 of 5
How does machine learning differ from historical approaches to prediction like traditional regression models?
  • A. Machine learning requires programmers to input rigid, rule-based instructions.
  • B. Machine learning relies solely on estimating averages from conditional data.
  • C. Machine learning draws insights directly from examples and adapts, rather than using rigid rules.
  • D. Machine learning requires significantly smaller datasets to achieve the same level of accuracy.
Question 3 of 5
What does the text suggest about slight enhancements in prediction accuracy, such as reducing an error rate from 2 percent to 0.1 percent?
  • A. It yields negligible real-world results and is rarely worth the computational cost.
  • B. It leads to vast implications, such as a drastic twentyfold drop in erroneous fraud detections.
  • C. It primarily benefits the scientific community rather than consumers or businesses.
  • D. It proves that machine learning models have achieved true, human-like intelligence.
Question 4 of 5
In the new division of labor between humans and machines, in which area do humans hold a distinct advantage?
  • A. Processing massive datasets riddled with intricate variable interactions.
  • B. Making consistent judgments in highly complex statistical scenarios.
  • C. Grasping causal relationships and crafting analogies from minimal data.
  • D. Executing routine, data-abundant tasks with high precision and speed.
Question 5 of 5
What is the 'prediction by exception' model described in the text?
  • A. A system where humans handle all predictions unless they explicitly delegate them to a machine.
  • B. A workflow where machines handle routine cases, and humans step in for outliers or unique scenarios.
  • C. A legal framework that prevents machines from making predictions in high-stakes industries.
  • D. An approach where machines are only used when human experts completely fail to reach a consensus.

Prediction Machines — Full Chapter Overview

Prediction Machines Summary & Overview

Prediction Machines (2018) delves into the transformative impact of artificial intelligence on the economics of decision-making. It highlights how AI reduces the cost of predictions, reshapes business problems, and influences decision-making amid uncertainty. The work further explores the value of data in today’s AI-driven economy and the changing dynamics between human labor and automation.

Who Should Listen to Prediction Machines?

  • Entrepreneurs looking to leverage AI in their startups
  • Business students looking at the future of industries influenced by AI
  • Tech enthusiasts curious about the intersection of AI and economics

About the Author: Ajay Agrawal, Joshua Gans & Avi Goldfarb

Ajay Agrawal is the academic director of the Centre for Innovation and Entrepreneurship at the Rotman School of Management at the University of Toronto, and the founder of the Creative Destruction Lab, specializing in the economics of innovation and artificial intelligence. He is also the co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence.

Joshua Gans holds the Jeffrey Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School of Management, and is known for his research in economic theory and business strategy.

Avi Goldfarb holds the Rotman Chair in Artificial Intelligence and Healthcare at the Rotman School of Management, and is recognized for his expertise in the implications of technology innovations on business and society.

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