Dealing with the flood of data that confronts researchers is the fundamental challenge of 21st century research. In many areas of research, the relentless growth of data sets has led to the adoption of increasingly automated and unsupervised methods of classification. In many cases, this has led to degradation in classification quality, with machine learning and computer vision unable to replicate the successes of human pattern recognition. Web-scale citizen science has provided a temporary solution to this problem however the solution is a short-term one. In this presentation I will outline a potential strategy for combining a large web community and significant compute resources to create a scalable, intelligent classification engine.
Dr Arfon Smith is Director of Citizen Science at the Adler Planetarium in Chicago and Technical Lead of the Zooniverse (www.zooniverse.org). He leads a team of developers, educators and scientists who build citizen science projects across a range of disciplines including astrophysics and papyrology. He gained a PhD in Astrochemistry from The University of Nottingham (2006) and subsequently worked as a senior software developer in the production software group at The Wellcome Trust Sanger Institute in Cambridge (UK). In 2008 he joined the Zooniverse team at University of Oxford and has coordinated the development of more than 20 citizen science projects and grown the Zooniverse community to more than 650,000 volunteers.
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