A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

  • Steve Kelling Cornell University
  • Jeff Gerbracht Cornell University
  • Daniel Fink Cornell University
  • Carl Lagoze Cornell University
  • Weng-Keen Wong Oregon State University
  • Jun Yu Oregon State University
  • Theodoros Damoulas Cornell University
  • Carla Gomes Cornell University

Abstract

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this 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. In this paper we explore how Human-Computer Learning Networks can 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.

Author Biographies

Steve Kelling, Cornell University
Cornell Lab of Ornithology
Jeff Gerbracht, Cornell University
Cornell Lab of Ornithology
Daniel Fink, Cornell University
Cornell Lab of Ornithology
Carl Lagoze, Cornell University
Information Science
Weng-Keen Wong, Oregon State University
School of Electrical Engineering and Computer Science
Jun Yu, Oregon State University
School of Electrical Engineering and Computer Science
Theodoros Damoulas, Cornell University
Department of Computer Science
Carla Gomes, Cornell University
Department of Computer Science
Published
2012-12-06
Section
Articles