A Human/Computer Learning Network to Improve Biodiversity Conservation and Research
A Human/Computer Learning Network to Improve Biodiversity Conservation and Research
复制标题
改善生物多样性保护和研究的人机学习网络
DOI：
10.1609/aimag.v34i1.2431
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
C. Gomes
中科院分区：
文献类型：
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作者：
S. Kelling;Jeff Gerbracht;D. Fink;C. Lagoze;Weng;Jun Yu;T. Damoulas;C. Gomes
在本文中，我们描述了 eBird，这是一个公民科学项目，它利用人类的观察能力来识别鸟类的物种，然后用于准确地表示鸟类在广泛的空间和时间范围内的出现模式。 eBird 采用机器学习等人工智能技术，通过人类计算和机械计算之间的协同作用来提高数据质量。我们称之为人机学习网络，其核心是人类和机器之间的主动学习反馈循环，可以显着提高两者的质量，从而不断提高整个网络的有效性。在本文中，我们探讨了人机学习网络如何利用广泛招募的人类观察者的贡献，并使用人工智能算法处理他们贡献的数据，从而获得远远超过各个部分总和的计算能力。
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.