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Doing ai:a Business-Centric Examination Of ai Culture, Goals, and Values
[Hardback - 2021]
richard heimann
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List Price: $30
Our Price: Rs.6745 Rs.5733
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Category: Science
Additional Category: Business - General - Management
Publisher: Matt Holt | ISBN: 9781953295736 | Pages: 272
Shipping Weight: .468 | Dimensions: 6.25 x 1 x 9.31 inches

Description

Artificial intelligence (AI) has captured our imaginations—and become a distraction. Too many leaders embrace the oversized narratives of artificial minds outpacing human intelligence and lose sight of the original problems they were meant to solve.

When businesses try to “do AI,” they place an abstract solution before problems and customers without fully considering whether it is wise, whether the hype is true, or how AI will impact their organization in the long term. Often absent is sound reasoning for why they should go down this path in the first place. 
 
Doing AI explores AI for what it actually is—and what it is not— and the problems it can truly solve. In these pages, author Richard Heimann unravels the tricky relationship between problems and high-tech solutions, exploring the pitfalls in solution-centric thinking and explaining how businesses should rethink AI in a way that aligns with their cultures, goals, and values. 
 
As the Chief AI Officer at Cybraics Inc., Richard Heimann knows from experience that AI-specific strategies are often bad for business. Doing AI is his comprehensive guide that will help readers understand AI, avoid common pitfalls, and identify beneficial applications for their companies.
 
This book is a must-read for anyone looking for clarity and practical guidance for identifying problems and effectively solving them, rather than getting sidetracked by a shiny new “solution” that doesn’t solve anything.

About the Author

Richard Heimann is Chief AI Officer at Cybraics Inc. A fully managed cybersecurity company focusing on advanced threat detection, Cybraics was founded in 2014 and operationalized many years of machine learning research conducted at the Defense Advanced Research Projects Agency (DARPA). 
 
Heimann is a former chief data scientist and technical fellow at L-3 National Security Solutions; former adjunct faculty at the University of Maryland, Baltimore County, where he taught graduate-level spatial econometrics and statistical reasoning; and an instructor at George Mason University, where he taught computational social science. He continues to be an advisor at George Mason University’s DataLab and several early-stage artificial intelligence ventures. Heimann has also performed on DARPA’s Nexus 7 program supporting ISAF and 82nd Airborne Division in Kandahar Afghanistan, the Naval Research Laboratory, and also consulted at the Pentagon on various AI projects and AI strategy.

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