published: Oct. 6, 2014, recorded: December 2013, views: 1941
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Abstract: 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.
Bio: Michael Bernstein is an Assistant Professor of Computer Science at Stanford University, where he directs the Human-Computer Interaction group and is a Robert N. Noyce Family Faculty Scholar. His research in human-computer interaction focuses on the design of crowdsourcing and social computing systems. This work has received Best Paper awards and nominations at premier venues in human-computer interaction and social computing (ACM UIST, ACM CHI, ACM CSCW, AAAI ISWSM). Michael was awarded the George M. Sprowls Award for best doctoral thesis in Computer Science at MIT. He holds Ph.D. and masters degrees in Computer Science from MIT, and a B.S. in Symbolic Systems from Stanford University.
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