Combining Minds: Coordination and Social Sensemaking
, Carnegie Mellon University
Date: Friday, February 19, 2010
Time: 1:00 PM to 2:00 PM
Refreshments: 12:45 PM
Location: Kiva Conference Room 32-G449
Host: Rob Miller, MIT CSAIL
Contact: Michael Bernstein, (617) 253-0452, firstname.lastname@example.org
Relevant URL: kittur.org
Speaker URL: None
email@example.com, firstname.lastname@example.org, email@example.com
TALK: Combining Minds: Coordination and Social Sensemaking
The amount of information available to individuals today is enormous and rapidly increasing. Continued progress in science, education, and technology is fundamentally dependent on making sense of and finding insights in overwhelming amounts of data. However, human cognition, while unparalleled at discovering patterns and linking seemingly-disparate concepts, is also limited in the amount of information it can process at once. One promising solution to this problem is through social collaboration, in which groups of individuals work together to produce knowledge and solve problems that exceed any individual's cognitive capacity.
In this talk I describe a series of studies examining the importance of coordination in harnessing the power of the crowds for complex information processing tasks in Wikipedia and beyond. I also present research into visualization and machine learning tools aimed at increasing the effectiveness of these systems. Finally, I discuss early forays into extending social collaboration to support insight and discovery.
Aniket Kittur is an assistant professor in the Human-Computer Interaction Institute at Carnegie Mellon University. He received his Ph.D. from UCLA in cognitive psychology and did his undergraduate work at Princeton University in psychology and computer science. His research focuses on understanding and augmenting how humans make sense of large amounts of information. At the group level he studies the dynamics of social collaborative systems such as Wikipedia and Amazons Mechanical Turk, and how visualization and machine learning tools can increase their effectiveness. At the individual level, his research interests center on human information processing in categorization and memory. His research employs multiple complementary techniques, including empirical experiments, statistical and computational modeling, visualization, data mining, and machine learning.
Funding for this seminar series has been provided by Yahoo!.
Created by Linda L. Julien at Wednesday, June 19, 2013 at 6:23 AM.