2018 PredictionMachinesTheSimpleEcon

• (Agrawal, Gans and Goldfarb, 2018) ⇒ Ajay Agrawal, Joshua Gans, and Avi Goldfarb. (2018). “Prediction Machines: The Simple Economics of Artificial Intelligence.” Harvard Business Press. ISBN:9781633695689

Subject Headings: Applied Prediction; Applied Judgement.

Notes

• Publisher book site: https://store.hbr.org/product/prediction-machines-the-simple-economics-of-artificial-intelligence/10195

• It emphasizes the economic perspective that the price of (some) applied prediction systems has dramatically dropped.

Cited By

• http://scholar.google.com/scholar?q=%222018%22+Prediction+Machines%3A+The+Simple+Economics+of+Artificial+Intelligence

Quotes

Book Overview

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.

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1 Introduction: Machine Intelligence (p.1)

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Being so close to so many applications of AI forced us to focus on how this technology affects business strategy. As we’ll explain, AI is a prediction technology, predictions are inputs to decision making, and economics provides a perfect framework for understanding the trade-offs underlying any decision. So, by dint of luck and some design, we found ourselves at the right place at the right time to form a bridge between the technologist and the business practitioner. The result is this book. Our first key insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence—prediction. What Alexa was doing when the child asked a question was taking the sounds it heard and predicting the words the child spoke and then predicting what information the words were looking for. Alexa doesn’t “know” the capital of Delaware. But Alexa is able to predict that, when people ask such a question, they are looking for a specific response: “Dover.”

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8 The Value of Judgment p.83

Having better prediction raises the value of judgment. After all, it doesn’t help to know the likelihood of rain if you don’t know how much you like staying dry or how much you hate carrying an umbrella.

Prediction machines don’t provide judgment. Only humans do, because only humans can express the relative rewards from taking different actions. As AI takes over prediction, humans will do less of the combined prediction-judgment routine of decision making and focus more on the judgment role alone.

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12 Deconstructing Work Flows (p.123)

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Like classical computing, AI is a general-purpose technology. It has the potential to affect every decision, because prediction is a key input to decision making. Hence, no manager is going to achieve large gains in productivity by just “throwing some AI” at a problem or into an existing process. Instead, AI is the type of technology that requires rethinking processes in the same way that Hammer and Champy did.

Businesses are already conducting analyses that take work flows and break them down into constituent tasks. Goldman Sachs’s CFO R. Martin Chavez remarked that many of the 146 distinct tasks in the initial public offering process were “begging to be automated.”4 Many of those 146 tasks are predicated on decisions that AI tools will significantly enhance. When somebody writes about the transformation of Goldman Sachs a decade from now, a major part of the story will be about how the rise of AI played a meaningful role in that transformation.

The actual implementation of AI is through the development of tools. The unit of AI tool design is not “the job” or “the occupation” or “the strategy,” but rather “the task.” Tasks are collections of decisions (like the ones represented by figure 7-1 and analyzed in part two). Decisions are based on prediction and judgment and informed by data. The decisions within a task often share these elements in common. Where they differ is in the action that follows. (See figure 12-1.)

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13 Decomposing Decisions (p.133)

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We have now seen more than 150 AI companies in the CDL, our laboratory that helps science-based companies grow. Each one is focused on the development of an AI tool that addresses a specific task in a specific work flow. One startup predicts the most important passages of a document and highlights them. Another predicts manufacturing defects and flags them. Yet another forecasts appropriate customer service responses and answers queries. And the list goes on. Large companies are implementing hundreds if not thousands of different AIs to enhance the various tasks in their own work flows. Indeed, Google is developing more than a thousand different AI tools to help with a wide variety of tasks, from email to translation to driving.5

For many businesses, prediction machines will be impactful, but in an incremental and largely inconspicuous manner, much as how AI improves many of the photo apps on your smartphone. It sorts the pictures in a helpful way but does not fundamentally change how you use the app.

However, you are likely reading this book because you are interested in how AI can lead to fundamental change in your business. AI tools can change work flows in two ways. First, they can render tasks obsolete and therefore remove them from work flows. Second, they can add new tasks. This may be different for every business and every work flow.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 PredictionMachinesTheSimpleEcon	Ajay Agrawal Joshua Gans Avi Goldfarb			Prediction Machines: The Simple Economics of Artificial Intelligence