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eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research

TitleeBird: A Human/Computer Learning Network for Biodiversity Conservation and Research
Publication TypeConference Paper
Year of Publication2012
AuthorsKelling, S., J. Gerbracht, D. Fink, C. Lagoze, W-K. Wong, J. Yu, T. Damoulas, and C. Gomes
Conference NameThe Twenty-Fourth Conference on Innovative Applications of Artificial Intelligence
Date Published07/2012
Conference LocationToronto, Ontario, Canada
Keywordsartificial intelligence, biodiversity, citizen-science, learning networks, machine learning

In this paper we describe eBird, a citizen-science project that takes advantage of human observational capacity and machine learning methods to explore the synergies between human computation and mechanical computation. We call this model a Human/Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. Human/Computer Learning Networks leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.


IAAI 2012 Deployed Application Award