Results by title

6 books match the query 'Prediction'

cover of An Observer's Guide to Clouds and Weather: A Northeastern Primer on Prediction

An Observer's Guide to Clouds and Weather

A Northeastern Primer on Prediction

Toby Carlson, Paul Knight, and Celia Wyckoff

American Meteorological Society, 2014

Today, most people look down when they want to check the weather, peeking at cell phones or popping open a browser, instead of looking up at one of the most accessible weather predictors of all—the sky. Knowing what the atmosphere has in store without relying on technology can be a gratifying experience, and now with An Observer’s Guide to Clouds and Weather, it is also one that is easy to learn.

This informative and accessible guide walks readers through the basics of making weather predictions through understanding cloud types and sky formations. It explains, in nontechnical terms, the science behind the weather, connecting fundamental meteorological concepts with the processes that shape weather patterns. Readers will learn how to develop their powers of observation and hone their ability to make quick forecasts without complicated tools. Whether you're an amateur weather enthusiast or a beginning meteorology student, An Observer’s Guide to Clouds and Weather will help anyone who prefers looking up to looking it up.
[more]
cover of Ex Ante: Power and Prediction in the Real-Time Future

Ex Ante

Power and Prediction in the Real-Time Future

John Cheney-Lippold

University of Chicago Press, 2027

An incisive framework for understanding how predictive AI has reshaped our relationship with time.

Ex Ante argues that AI systems draw us into a new experience of time, a continuous stream of machine-generated futures that reshapes our relationship to ourselves, to capitalism, and to infrastructure. Unlike other predictive tools that help us prepare for what might come, these projections—produced by algorithms and sourced by massive flows of data—calibrate our actions moment by moment, second by second. 

In this book, John Cheney-Lippold offers a framework he calls the ex ante, Latin for “before the event,” as a way to consider how the future has become capitalism’s latest resource for exploitation. Through case studies spanning TikTok beauty filters that have fueled a surge in plastic surgery consultations, chess engines that redefine how the game is played, online sports betting platforms that commodify probability itself, hospital sepsis algorithms that transform the labor of nursing, and coordinated vehicle networks that collectively anticipate their surroundings to function, Ex Ante offers an exploration and powerful critique of our algorithmic present and the powers behind it.

[more]
cover of Looking Forward: Prediction and Uncertainty in Modern America

Looking Forward

Prediction and Uncertainty in Modern America

Jamie L. Pietruska

University of Chicago Press, 2017

In the decades after the Civil War, the world experienced monumental changes in industry, trade, and governance. As Americans faced this uncertain future, public debate sprang up over the accuracy and value of predictions, asking whether it was possible to look into the future with any degree of certainty. In Looking Forward, Jamie L. Pietruska uncovers a culture of prediction in the modern era, where forecasts became commonplace as crop forecasters, “weather prophets,” business forecasters, utopian novelists, and fortune-tellers produced and sold their visions of the future. Private and government forecasters competed for authority—as well as for an audience—and a single prediction could make or break a forecaster’s reputation. 

Pietruska argues that this late nineteenth-century quest for future certainty had an especially ironic consequence: it led Americans to accept uncertainty as an inescapable part of both forecasting and twentieth-century economic and cultural life. Drawing together histories of science, technology, capitalism, environment, and culture, Looking Forward explores how forecasts functioned as new forms of knowledge and risk management tools that sometimes mitigated, but at other times exacerbated, the very uncertainties they were designed to conquer. Ultimately Pietruska shows how Americans came to understand the future itself as predictable, yet still uncertain.
[more]
cover of Prediction and Regulation by Linear Least-Square Methods

Prediction and Regulation by Linear Least-Square Methods

Peter Whittle

University of Minnesota Press, 1963

Prediction and Regulation by Linear Least-Square Methods was first published in 1963. This revised second edition was issued in 1983. Minnesota Archive Editions uses digital technology to make long-unavailable books once again accessible, and are published unaltered from the original University of Minnesota Press editions.During the past two decades, statistical theories of prediction and control have assumed an increasing importance in all fields of scientific research. To understand a phenomenon is to be able to predict it and to influence it in predictable ways. First published in 1963 and long out of print, Prediction and Regulation by Linear Least-Square Methods offers important tools for constructing models of dynamic phenomena. This elegantly written book has been a basic reference for researchers in many applied sciences who seek practical information about the representation and manipulation of stationary stochastic processes. Peter Whittle’s text has a devoted group of readers and users, especially among economists. This edition contains the unchanged text of the original and adds new works by the author and a foreword by economist Thomas J. Sargent.
[more]
cover of The Means of Prediction: How AI Really Works (and Who Benefits)

The Means of Prediction

How AI Really Works (and Who Benefits)

Maximilian Kasy

University of Chicago Press, 2025

An eye-opening examination of how power—not technology—will define life with AI.

AI is inescapable, from its mundane uses online to its increasingly consequential decision-making in courtrooms, job interviews, and wars. The ubiquity of AI is so great that it might produce public resignation—a sense that the technology is our shared fate.
 
As economist Maximilian Kasy shows in The Means of Prediction, artificial intelligence, far from being an unstoppable force, is irrevocably shaped by human decisions—choices made to date by the ownership class that steers its development and deployment. Kasy shows that the technology of AI is ultimately not that complex. It is insidious, however, in its capacity to steer results to its owners’ wants and ends. Kasy clearly and accessibly explains the fundamental principles on which AI works, and, in doing so, reveals that the real conflict isn’t between humans and machines, but between those who control the machines and the rest of us.
 
The Means of Prediction offers a powerful vision of the future of AI: a future not shaped by technology, but by the technology’s owners. Amid a deluge of debates about technical details, new possibilities, and social problems, Kasy cuts to the core issue: Who controls AI’s objectives, and how is this control maintained? The answer lies in what he calls “the means of prediction,” or the essential resources required for building AI systems: data, computing power, expertise, and energy. As Kasy shows, in a world already defined by inequality, one of humanity’s most consequential technologies has been and will be steered by those already in power.
 
Against those stakes, Kasy offers an elegant framework both for understanding AI’s capabilities and for designing its public control. He makes a compelling case for democratic control over AI objectives as the answer to mounting concerns about AI's risks and harms. The Means of Prediction is a revelation, both an expert undressing of a technology that has masqueraded as more complicated and a compelling call for public oversight of this transformative technology.
 
[more]
cover of The Means of Prediction: How AI Really Works (and Who Benefits)

The Means of Prediction

How AI Really Works (and Who Benefits)

Maximilian Kasy

University of Chicago Press, 2025

An eye-opening examination of how power—not technology—will define life with AI.

AI is inescapable, from its mundane uses online to its increasingly consequential decision-making in courtrooms, job interviews, and wars. The ubiquity of AI is so great that it might produce public resignation—a sense that the technology is our shared fate.
 
As economist Maximilian Kasy shows in The Means of Prediction, artificial intelligence, far from being an unstoppable force, is irrevocably shaped by human decisions—choices made to date by the ownership class that steers its development and deployment. Kasy shows that the technology of AI is ultimately not that complex. It is insidious, however, in its capacity to steer results to its owners’ wants and ends. Kasy clearly and accessibly explains the fundamental principles on which AI works, and, in doing so, reveals that the real conflict isn’t between humans and machines, but between those who control the machines and the rest of us.
 
The Means of Prediction offers a powerful vision of the future of AI: a future not shaped by technology, but by the technology’s owners. Amid a deluge of debates about technical details, new possibilities, and social problems, Kasy cuts to the core issue: Who controls AI’s objectives, and how is this control maintained? The answer lies in what he calls “the means of prediction,” or the essential resources required for building AI systems: data, computing power, expertise, and energy. As Kasy shows, in a world already defined by inequality, one of humanity’s most consequential technologies has been and will be steered by those already in power.
 
Against those stakes, Kasy offers an elegant framework both for understanding AI’s capabilities and for designing its public control. He makes a compelling case for democratic control over AI objectives as the answer to mounting concerns about AI's risks and harms. The Means of Prediction is a revelation, both an expert undressing of a technology that has masqueraded as more complicated and a compelling call for public oversight of this transformative technology.
 

[more]