Algorithms create prevent fake (27 résultats)

Etat : New.

Etat : New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

Paperback or Softback. Etat : New. How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, Gpt-3, and More. Book.

Etat : As New. Unread book in perfect condition.

Etat : New.

Paperback. Etat : Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Etat : New. 1st ed. edition NO-PA16APR2015-KAP.

Etat : New.

Etat : New. 2021. 1st ed. Paperback. . . . . .

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Paperback. Etat : Brand New. 247 pages. 9.25x6.10x0.56 inches. In Stock.

Etat : New.

Etat : New. 2021. 1st ed. Paperback. . . . . . Books ship from the US and Ireland.

Etat : New.

Etat : New.

Etat : New. In English.

Paperback. Etat : New.

Etat : New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Etat : New.

Taschenbuch. Etat : Neu. How Algorithms Create and Prevent Fake News | Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More | Noah Giansiracusa | Taschenbuch | xii | Englisch | 2021 | Apress | EAN 9781484271544 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Etat : gut. 2021. How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More In englischer Sprache. pages.

Etat : new. Questo è un articolo print on demand.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -'It's a joy to read a book by a mathematician who knows how to write. [.]There is no better guide to the strategies and stakes of this battle for the future.'---Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank.'By explaining the flaws and foibles of everything from Google search to QAnon-and by providing level-headed evaluations of efforts to fix them-Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media.'-Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor ofThe AtlanticFrom 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.What You Will LearnThe ways that data labeling and storage impact machine learning and how feedback loops can occurThe history and inner-workings of YouTube's recommendation algorithmThe state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so farThe algorithmic tools available to help with automated fact-checking and truth-detectionWho This Book is ForPeople who don't have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work. 248 pp. Englisch.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'It's a joy to read a book by a mathematician who knows how to write. [.]There is no better guide to the strategies and stakes of this battle for the future.'---Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank.'By explaining the flaws and foibles of everything from Google search to QAnon-and by providing level-headed evaluations of efforts to fix them-Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media.'-Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor ofThe AtlanticFrom 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 todayis 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.What You Will LearnThe ways that data labeling and storage impact machine learning and how feedback loops can occurThe history and inner-workings of YouTube's recommendation algorithmThe state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so farThe algorithmic tools available to help with automated fact-checking and truth-detectionWho This Book is ForPeople who don't have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -'It's a joy to read a book by a mathematician who knows how to write. [.] There is no better guide to the strategies and stakes of this battle for the future.'Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank.'By explaining the flaws and foibles of everything from Google search to QAnon-and by providing level-headed evaluations of efforts to fix them-Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media.'Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor of The AtlanticFrom 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 todayis 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.What You Will LearnThe ways that data labeling and storage impact machine learning and how feedback loops can occurThe history and inner-workings of YouTube's recommendation algorithmThe state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so farThe algorithmic tools available to help with automated fact-checking and truth-detectionWho This Book is ForPeople who don't have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 248 pp. Englisch.…