The Emergent Brain

When:
Tuesday, March 27, 2018, 11:00 am - 12:00 pm PSTiCal
Where:
6th floor conference room
This event is open to the public.
Type:
AI Seminar - Interview talk
Speaker:
Keith Burghardt, UC Davis
Video Recording:
https://bluejeans.com/s/c7ytc
Description:

How do groups “think”? For example, previous work has found that, in controlled settings, group intelligence is not strongly correlated with any individual’s intelligence, and that crowds can make better decisions than individuals. An ongoing problem, however, is determining what are the root causes of these differences between group intelligence and individual intelligence, as well as the robustness of these findings in real-world systems. For example, individuals influence each other, and conflicting research suggests that this may help or hurt crowd wisdom; furthermore, we are only just beginning to understand the influence mechanism. My work helps elucidate these issues through models of large datasets, colloquially known as Big Data, which tease out the factors that affect crowd wisdom, including idea visibility and perceived popularity. Furthermore, I create natural and controlled experiments using these datasets to determine what cognitive mechanisms limit or enhance crowd wisdom. In the future, we will be using simple algorithms to reduce the effect of these mechanisms as well as using these cognitive mechanisms to encourage the spread of high-quality ideas, or even reduce the impact of false information.

Biography:
Keith Burghardt is a postdoctoral researcher at the University of California at Davis who specializes in applying tools from physics and computer science to better understand emergent phenomena due to human interactions. He holds a Bachelor of Science and a Doctor of Philosophy in Physics from the University of Maryland in College Park. His work has received accolades including an Information Sciences Institute Director’s Intern Award, a Conference on Complex Systems Starred Paper Award and his most recent paper received mention in 2017 by the MIT Technology Review as one of the best papers posted that week on arXiv.

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