Editor

Agrawal, Ajay

Gans, Joshua

Goldfarb, Avi

Place of Publication

Boston, Massachusetts

Publisher

Harvard Business Review Press

Publication Date

c2022

Series

ProQuest Ebook Central

Subjects

Artificial Intelligence

Artificial Intelligence - economics

Forecasting - methods

Decision Making

Abstract

"Named one of "The five best books to understand AI" by The Economist The impact AI will have is profound, but the economic framework for understanding it is surprisingly simple. Artificial intelligence seems to do the impossible, magically bringing machines to life?driving cars, trading stocks, and teaching children. But facing the sea change that AI brings 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 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 masterful stroke, they lift the curtain on the AI-is-magic hype and provide economic clarity about the AI revolution as well as a basis for action by executives, policy makers, investors, and entrepreneurs. In this new, updated edition, the authors illustrate how, when AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions amid 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 strategies to compete. The authors reset the context, describing the striking impact the book has had and how its argument and its implications are playing out in the real world. And in new material, they explain how prediction fits into decision-making processes and how foundational technologies such as quantum computing will impact business choices. Penetrating, insightful, and practical, Prediction Machines will help you navigate the changes on the horizon." --publisher

ISBN

9781647824679

Language

English

Material Type

Ebook

Audience

Professional

Location

Internet

Website Notes

This title is licensed for one user at a time on BC Cancer-network computers. If you are denied access while on a network computer, try again later.

This Proquest title is licensed for ONE BC Cancer staff user at a time. If you are denied access while on a network computer, please try again later.

Authentication via HealthBC sign in required. Use your BC Cancer staff username and ID when prompted.

Author

Hesse-Biber, Sharlene

Place of Publication

Ann Arbor, MI

Publisher

University of Michigan Press

Publication Date

2014

Series

EBSCOhost eBook

Subjects

Genetic Testing

Genes, BRCA1

Genes, BRCA2

Hereditary Breast and Ovarian Cancer Syndrome

Decision Making

Women's Health

Breast Neoplasms - psychology

Ovarian Neoplasms - psychology

Genetic Predisposition to Disease

Personal Narratives

Popular Work

ISBN

9780472052196

9780472120352

Language

English

Material Type

Ebook

Audience

Patient or Public

Location

Internet

Website Notes

Patients and the public of BC and the Yukon can access this title by emailing library@bccancer.bc.ca to obtain a username and password from the Library.

Author

Jonsen, Albert R

Siegler, Mark

Winslade, William J

Edition

6th ed.

Place of Publication

New York, NY

Publisher

McGraw-Hill, Inc.

Publication Date

2006

Series

R2 Library

Subjects

Ethics, Clinical

Bioethical Issues

Patient Participation

Decision Making

Ethical Analysis - methods

Quality of Life

ISBN

0071441999

Language

English

Material Type

Book

Ebook

Audience

Professional

Location

Internet

Kelowna Library

Call Number

WB60 J81 2006

Website Notes

This R2 title is licensed for one BC Cancer staff user at a time only. Authentication via HealthBC sign in required. Use your BC Cancer staff username and ID when prompted.

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