The Will To Predict
Download The Will To Predict PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The Will To Predict book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Predictive Analytics
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
How to Predict Everything
Author: William Poundstone
language: en
Publisher: Simon and Schuster
Release Date: 2019-06-06
How do you predict something that has never happened before? There's a useful calculation being employed by Wall Street, Silicon Valley and maths professors all over the world, and it predicts that the human species will become extinct in 760 years. Unfortunately, there is disagreement over how to apply the formula, and some argue that we might only have twenty years left. Originally devised by British clergyman Thomas Bayes, the theorem languished in obscurity for two hundred years before being resurrected as the lynchpin of the digital economy. With brief detours into archaeology, philology, and overdue library books, William Poundstone explains how we can use it to predict pretty much anything. What is the chance that there are multiple universes? How long will Hamilton run? Will the US stock market continue to perform as well this century as it has for the last hundred years? And are we really all doomed?
The Will to Predict
Author: Eglė Rindzevičiūtė
language: en
Publisher: Cornell University Press
Release Date: 2023-05-15
In The Will to Predict, Eglė Rindzevičiūtė demonstrates how the logic of scientific expertise cannot be properly understood without knowing the conceptual and institutional history of scientific prediction. She notes that predictions of future population, economic growth, environmental change, and scientific and technological innovation have shaped much of twentieth and twenty-first-century politics and social life, as well as government policies. Today, such predictions are more necessary than ever as the world undergoes dramatic environmental, political, and technological change. But, she asks, what does it mean to predict scientifically? What are the limits of scientific prediction and what are its effects on governance, institutions, and society? Her intellectual and political history of scientific prediction takes as its example twentieth-century USSR. By outlining the role of prediction in a range of governmental contexts, from economic and social planning to military strategy, she shows that the history of scientific prediction is a transnational one, part of the history of modern science and technology as well as governance. Going beyond the Soviet case, Rindzevičiūtė argues that scientific predictions are central for organizing uncertainty through the orchestration of knowledge and action. Bridging the fields of political sociology, organization studies, and history, The Will to Predict considers what makes knowledge scientific and how such knowledge has impacted late modern governance.