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Talks & Seminars
Decision-Theoretic Crowdsourcing
Dr. Mausam, University of Washington
Date & Time: August 30, 2012 16:00
Venue: Conference Room, 01st Floor, Dept. of Computer Science & Engg., Kanwal Rekhi Bldg.
Abstract:
Crowdsourcing continues to rise in popularity today and is considered as the future of labor by many. Presently, crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) have enabled the construction of scalable applications for tasks ranging from product categorization and photo tagging to audio transcription and language translation. These vertical applications are typically realized with complex, self-managing workflows that guarantee quality results. But constructing and controlling such workflows is challenging, with a huge number of alternative decisions for the designer to consider. We argue the thesis that artificial intelligence methods can greatly simplify the process of creating and managing complex crowdsourced workflows. We present the design of CLOWDER, which uses decision-theoretic techniques to dynamically optimize the workflows. Preliminary evaluations suggest that these optimized workflows are significantly more economical and return a much higher quality output than those generated by human designers.
Speaker Profile:
Mausam (www.cs.washington.edu/homes/mausam) is a Research Assistant Professor of Computer Science at University of Washington, Seattle. His research explores several threads in artificial intelligence, including scaling probabilistic planning algorithms, large-scale information extraction over the Web, panlingual machine translation and enabling complex computation over crowdsourced platforms. He received his PhD from University of Washington in 2007 and a B.Tech. from IIT Delhi in 2001.
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