Human computation /

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Bibliographic Details
Author / Creator:Law, Edith.
Imprint:San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2011.
Description:1 electronic text (xi, 105 p.) : ill., digital file.
Language:English
Series:Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; # 13
Synthesis lectures on artificial intelligence and machine learning, # 13.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8512910
Hidden Bibliographic Details
Other authors / contributors:Von Ahn, Luis
ISBN:9781608455171 (electronic bk.)
9781608455164 (pbk.)
Notes:Series from website.
Includes bibliographical references (p. 77-103).
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Summary:Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. With the growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of people via the Web to perform complex computation.There are various genres of human computation applications that exist today. Games with a purpose (e.g., the ESP Game) specifically target online gamers who generate useful data (e.g., image tags) while playing an enjoyable game. Crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) are human computation systems that coordinate workers to perform tasks in exchange for monetary rewards. In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that optical character recognition (OCR) programs fail to recognize with certainty. This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future.
Standard no.:10.2200/S00371ED1V01Y201107AIM013

MARC

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245 1 0 |a Human computation /  |c Edith Law and Luis von Ahn. 
260 |a San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :  |b Morgan & Claypool,  |c c2011. 
300 |a 1 electronic text (xi, 105 p.) :  |b ill., digital file. 
336 |a text  |b txt  |2 rdacontent  |0 http://id.loc.gov/vocabulary/contentTypes/txt 
337 |a computer  |b c  |2 rdamedia  |0 http://id.loc.gov/vocabulary/mediaTypes/c 
338 |a online resource  |b cr  |2 rdacarrier  |0 http://id.loc.gov/vocabulary/carriers/cr 
490 1 |a Synthesis lectures on artificial intelligence and machine learning,  |x 1939-4616 ;  |v # 13 
538 |a Mode of access: World Wide Web. 
538 |a System requirements: Adobe Acrobat Reader. 
500 |a Series from website. 
504 |a Includes bibliographical references (p. 77-103). 
505 0 |a Preface -- Acknowledgments -- 
505 8 |a 1. Introduction -- 1.1 Computation: now and then -- 1.2 What is human computation? -- 1.2.1 Explicit control -- 1.3 Tackling AI problems: from vision to biology -- 1.3.1 A matter of perception -- 1.3.2 The language barrier -- 1.3.3 Intuition into computationally intensive problems -- 1.4 Overview -- 
505 8 |a Part I. Solving computational problems -- 2. Human computation algorithms -- 2.1 A definition of algorithms -- 2.2 Building blocks of algorithms -- 2.2.1 Operations, controls and program synthesis -- 2.3 Programming frameworks -- 2.4 Evaluating human computation algorithms -- 2.4.1 Correctness -- 2.4.2 Efficiency -- 2.5 Summary -- 
505 8 |a 3. Aggregating outputs -- 3.1 Objective versus cultural truth -- 3.2 Classification -- 3.2.1 Latent class models -- 3.2.2 Learning from imperfect data -- 3.3 Beyond classification -- 3.3.1 Ranking and voting -- 3.3.2 Clustering -- 3.3.3 Structured outputs -- 3.3.4 Beliefs -- 3.4 Summary -- 
505 8 |a 4. Task routing -- 4.1 Push versus pull approaches -- 4.2 Push approach -- 4.2.1 Allocation -- 4.2.2 Matching -- 4.2.3 Inference -- 4.3 Pull approacH -- 4.3.1 Search and visualization -- 4.3.2 Task recommendation -- 4.3.3 Peer routing -- 4.4 Evaluation criteria -- 4.5 Summary -- 
505 8 |a Part II. Design -- 5. Understanding workers and requesters -- 5.1 Markets -- 5.1.1 Mechanical Turk and paid crowdsourcing -- 5.1.2 Security and access -- 5.1.3 Gamers -- 5.1.4 Citizen science -- 5.1.5 Learners -- 5.1.6 Temporary markets -- 5.2 Supporting end users -- 5.2.1 Workers -- 5.2.2 Requesters -- 5.3 Summary -- 
505 8 |a 6. The art of asking questions -- 6.1 Designing tasks -- 6.1.1 Information -- 6.1.2 Granularity -- 6.1.3 Independence -- 6.1.4 Incentives -- 6.1.5 Quality control -- 6.2 Eliciting truthful responses -- 6.2.1 Human computation games -- 6.2.2 Leveraging communication -- 6.2.3 Explicitly preventing bad outputs -- 6.2.4 A brief survey of games and mechanisms -- 6.3 Summary -- Part III. Conclusion -- 
505 8 |a 7. The future of human computation -- 7.1 Research directions -- 7.1.1 Interweaving human and machine intelligence -- 7.1.2 Fostering long-term relationships -- 7.1.3 Designing organizations and task markets -- 7.2 Conclusion -- 
505 8 |a Bibliography -- Authors' biographies. 
506 |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. 
520 3 |a Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. With the growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of people via the Web to perform complex computation.There are various genres of human computation applications that exist today. Games with a purpose (e.g., the ESP Game) specifically target online gamers who generate useful data (e.g., image tags) while playing an enjoyable game. Crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) are human computation systems that coordinate workers to perform tasks in exchange for monetary rewards. In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that optical character recognition (OCR) programs fail to recognize with certainty. This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future. 
650 0 |a Bionics.  |0 http://id.loc.gov/authorities/subjects/sh85014250 
650 0 |a Human-computer interaction.  |0 http://id.loc.gov/authorities/subjects/sh88003229 
650 0 |a Machine learning.  |0 http://id.loc.gov/authorities/subjects/sh85079324 
650 0 |a Artificial intelligence.  |0 http://id.loc.gov/authorities/subjects/sh85008180 
650 0 |a Social media  |x Economic aspects.  |0 http://id.loc.gov/authorities/subjects/sh2010113589 
653 |a human computation 
653 |a human-in-the-loop algorithms 
653 |a output aggregation 
653 |a active learning 
653 |a latent class models 
653 |a task routing 
653 |a labor markets 
653 |a games with a purpose 
653 |a task design 
653 |a crowdsourcing 
653 |a mechanism design 
653 |a incentives 
650 7 |a Human computation.  |2 fast  |0 http://id.worldcat.org/fast/fst01894065 
700 1 |a Von Ahn, Luis  |0 http://id.loc.gov/authorities/names/no2011116377  |1 http://viaf.org/viaf/173143967 
830 0 |a Synthesis lectures on artificial intelligence and machine learning,  |x 1939-4616 ;  |v # 13.  |0 http://id.loc.gov/authorities/names/no2008023636 
856 4 0 |3 Abstract with links to full text  |u http://dx.doi.org/10.2200/S00371ED1V01Y201107AIM013 
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