Friday, February 7, 2014

Title: Synergy of Machine Intelligence and Human Computation
Speaker: Ece Kamar (Microsoft Research)
Date: Wednesday, February 12
Time: 12-1pm
Room: GHC 8102

Abstract:
Human computation has offered new opportunities for making computer systems smarter and more capable with easy access to human intelligence on demand. Making human computation a reliable component of computer systems requires moving away from manual designs and controls towards generalizable automation techniques, algorithms, models and designs. In this talk, I will present an overview of our recent research efforts towards this goal that have emerged through our collaboration with the Zooniverse citizen science effort. I will start by showing how machine learning and decision-theoretic reasoning can be used in harmony to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks efficiently. This methodology, which we refer to as CrowdSynth, includes predictive models for inference and efficient algorithms for making effective decisions, is shown empirically to maximize the efficiency of a large-scale crowdsourcing operation. Next, I will present how predictive modeling can be used to make inferences about attention and engagement of workers. I will conclude the talk by presenting a study of how different financial incentives provided to paid workers affect their speed, quality and attention and how their performance on a difficult citizen science task compare to volunteers.

Bio:
Ece Kamar is a researcher at the Adaptive Systems and Interaction group at Microsoft Research Redmond. Ece earned her Ph.D. in computer science from Harvard University. While at Harvard, she received the Microsoft Research fellowship and Robert L. Wallace Prize Fellowship for her work on Artificial Intelligence. She currently serves in the program committee of conferences such as AAAI, AAMAS, IJCAI, WWW, UAI and HCOMP. Her research interests include human-computer collaboration, decision-making under uncertainty, probabilistic reasoning and mechanism design with a focus on real-world applications that bring people and adaptive agents together.

























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