Human in the Loop: Crowdsourcing Visual Advertisements for Data Labeling
mer. 6 mars à 00:30
We are very excited to kick off March in partnership with the ACM to discuss data labelling and systematizing the "human in the loop" approach. Tentative Schedule:
6:30pm: Pizza + Beer networking
7:00pm: TBD by Data Scientist at Dataiku
7:30pm: Human in the Loop: Crowdsourcing Visual Advertisements for Data Labeling with Lydia Chilton, Assistant Professor in Computer Science at Columbia University Talk Abstracts: Images have the power to convey messages in striking and memorable ways. Although constructing visual messages is currently too hard for computers or novice users, by combining the intelligence of people and computers we can create compelling visual messages computationally. In this talk, we present VisiBlends, a flexible workflow for creating visual blends that follows the design process with steps involving brainstorming, synthesis, and iteration. An evaluation of the workflow shows that (1) decentralized groups of people can generate blends in independent microtasks, (2) co-located groups can collaboratively make visual blends for their own messages, and (3) VisiBlends improves novices’ ability to make visual blends. We will discuss how to decompose other complex tasks so that people and computers can collaborate in generating novel, useful and creative solutions to problems. Speaker bios:
Lydia Chilton is an Assistant Professor in Computer Science at Columbia University. She did her undergraduate work at MIT, her PhD at the University of Washington and her post-doc at Stanford. She is a member of the inaugural class of the ACM Future of Computing Academy and an early pioneer in crowdsourcing complex tasks on Mechanical Turk. Professor Chilton’s interests are in coordinating people and computers to complete complex and creative tasks that neither computers or individuals can do alone. These tasks involve conveying messages implicitly through text and images, translating and adapting research and ideas to new areas, and finding actionable insights from data.
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