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How algorithms create and prevent fa...

Giansiracusa, Noah.

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How algorithms create and prevent fake news = exploring the impacts of social media, deepfakes, GPT-3, and more /

Record Type:	Electronic resources : Monograph/item
Title/Author:	How algorithms create and prevent fake news/ by Noah Giansiracusa.
Reminder of title:	exploring the impacts of social media, deepfakes, GPT-3, and more /
Author:	Giansiracusa, Noah.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xii, 235 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Perils of Pageview -- 2. Crafted by Computer -- 3. Deepfake Deception -- 4. Autoplay the Autocrats -- 5. Prevarication and the Polygraph -- 6. Gravitating to Google -- 7. Avarice of Advertising -- 8. Social Spread -- 9. Tools for Truth.
Contained By:	Springer Nature eBook
Subject:	Fake news - Prevention. -
Online resource:	https://doi.org/10.1007/978-1-4842-7155-1
ISBN:	9781484271551

How algorithms create and prevent fake news = exploring the impacts of social media, deepfakes, GPT-3, and more /
Giansiracusa, Noah.

How algorithms create and prevent fake newsexploring the impacts of social media, deepfakes, GPT-3, and more /[electronic resource] :by Noah Giansiracusa. - Berkeley, CA :Apress :2021. - xii, 235 p. :ill., digital ;24 cm.

1. Perils of Pageview -- 2. Crafted by Computer -- 3. Deepfake Deception -- 4. Autoplay the Autocrats -- 5. Prevarication and the Polygraph -- 6. Gravitating to Google -- 7. Avarice of Advertising -- 8. Social Spread -- 9. Tools for Truth.

From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what's real and what's not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what's at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information today is filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias - which gets amplified in harmful data feedback loops. Don't be afraid: with this book you'll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope.

ISBN: 9781484271551

Standard No.: 10.1007/978-1-4842-7155-1doiSubjects--Topical Terms:

3501579
Fake news
--Prevention.

LC Class. No.: QA76.9.A43 / G53 2021

Dewey Class. No.: 006.31

How algorithms create and prevent fake news = exploring the impacts of social media, deepfakes, GPT-3, and more /
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245 1 0 $a How algorithms create and prevent fake news $h [electronic resource] : $b exploring the impacts of social media, deepfakes, GPT-3, and more / $c by Noah Giansiracusa.
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300 $a xii, 235 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Perils of Pageview -- 2. Crafted by Computer -- 3. Deepfake Deception -- 4. Autoplay the Autocrats -- 5. Prevarication and the Polygraph -- 6. Gravitating to Google -- 7. Avarice of Advertising -- 8. Social Spread -- 9. Tools for Truth.
520 $a From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what's real and what's not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what's at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information today is filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias - which gets amplified in harmful data feedback loops. Don't be afraid: with this book you'll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope.
650 0 $a Fake news $x Prevention. $3 3501579
650 0 $a Deepfakes $x Prevention. $3 3501580
650 0 $a Computer algorithms. $3 523872
650 0 $a Machine learning. $3 533906
650 1 4 $a Algorithms. $3 536374
650 2 4 $a Engineering Ethics. $3 2183278
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Statistics, general. $3 896933
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 891007
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
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856 4 0 $u https://doi.org/10.1007/978-1-4842-7155-1
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