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HCIL-2010-09

Quinn, A., Bederson, B., Yeh, T., Lin, J. (May 2010)
CrowdFlow: Integrating Machine Learning with Mechanical Turk for Speed-Cost-Quality Flexibility
HCIL-2010-09

Humans and machines have competing strengths for tasks such as natural language processing and image understanding. Whereas humans do these things naturally with potentially high accuracy, machines offer greater speed and flexibility. CrowdFlow is our toolkit for a model for blending the two in order to attain tighter control over the inherent tradeoffs in speed, cost and quality. With CrowdFlow, humans and machines work together to do a set of tasks at a user-specified point in the tradeoff space. They work symbiotically, with the humans providing training data to the machine while the machine provides first cut results to the humans to save effort in cases where the machine’s answer was already correct. The CrowdFlow toolkit can be considered as a generalization of our other domain-specific efforts aimed at enabling cloud computing services using a variety of computational resources to achieve various tradeoff points.



Keshif:  Simplicity Driven Visual Faceted Browser Screenshot

Keshif: Simplicity Driven Visual Faceted Browser
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