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Crowd-Powered Systems

Date and Time
Wednesday, February 22, 2012 - 4:30pm to 5:30pm
Computer Science Small Auditorium (Room 105)
CS Department Colloquium Series
Rebecca Fiebrink
Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone.

Unfortunately, crowd work is error-prone and slow, making it difficult to incorporate crowds as first-order building blocks in software systems. I introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. These techniques advance crowdsourcing into a platform that is reliable and responsive to the point where crowds can be used in interactive systems.

In this talk, I will present two crowd-powered systems to illustrate these ideas. The first, Soylent, is a word processor that uses paid micro-contributions to aid writing tasks such as text shortening and proofreading. Using Soylent is like having access to an entire editorial staff as you write. The second system, Adrenaline, is a camera that uses crowds to help amateur photographers capture the exact right moment for a photo. It finds the best smile and catches subjects in mid-air jumps, all in realtime. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.

Michael Bernstein is a PhD candidate in Computer Science at the Massachusetts Institute of Technology. His research in human-computer interaction focuses on crowdsourcing and social computing systems. He has been awarded Best Student Paper at UIST 2010, Best Paper at ICWSM 2011, the NSF graduate research fellowship and the Microsoft Research PhD fellowship. His work has appeared in venues like the New York Times, Slate, CNN and The Atlantic. He earned his masters in Computer Science at MIT, and a bachelors degree in Symbolic Systems at Stanford University.

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