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The Practice of Crowdsourcing [electronic resource] / by Omar Alonso.

By: Alonso, Omar [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Information Concepts, Retrieval, and Services: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XIX, 129 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031023187.Subject(s): Computer networks  | Computer Communication NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 004.6 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- Introduction -- Designing and Developing Microtasks -- Quality Assurance -- Algorithms and Techniques for Quality Control -- The Human Side of Human Computation -- Putting All Things Together -- Systems and Data Pipelines -- Looking Ahead -- Bibliography -- Author's Biography .
In: Springer Nature eBookSummary: Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.
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Preface -- Acknowledgments -- Introduction -- Designing and Developing Microtasks -- Quality Assurance -- Algorithms and Techniques for Quality Control -- The Human Side of Human Computation -- Putting All Things Together -- Systems and Data Pipelines -- Looking Ahead -- Bibliography -- Author's Biography .

Many data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.

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