Computational Wisdom of Crowds: Algorithm Development & Evaluation
|This is an ongoing project.||
Contact DetailsMatt Lease
Presently this is an unpaid opportunity only, either independent study for course credit or simply volunteering.
Crowdsourcing involves outsourcing of tasks to a large group of people instead of assigning such tasks to an in-house employee or contractor. While we have successfully automated many routine tasks, human competency still exceeds automated algorithms for many other, more complex processing tasks, such as analyzing text or imagery. Today’s Internet-based access to 24/7 online human crowds has led to the advent of crowdsourcing and a renaissance of research in using human computers once more. These new opportunities have brought a disruptive shift to research and practice for how we build intelligent systems today. On one hand, labeled data for training and evaluation can be collected faster, cheaper, and easier than ever before. While traditional scarcity of labeled data has helped to drive research on unsupervised and semi-supervised methods, strategic use of crowdsourcing now allows us to collect the labels needed on demand and at scale. With access to a human crowd “on-call” whenever the system has a question, there is tremendous potential for intelligent systems to enjoy constant, lifetime learning.
In addition to collecting labeled data, human computation is also being increasingly integrated into intelligent systems themselves, operating in concert with AI. While AI accuracies will certainly continue to improve, use of human computation in concert with automation lets us achieve greater capabilities today using hybrid systems. For example, better assistance can be provided to visually and aurally impaired persons by leveraging superior capabilities of humans to make sense of images and sound.
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