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Center for Nonlinear Studies |
Most of the data collected and stored in the world is in the form of images or video, and understanding images is critical to many computational systems. Computational systems are very good at managing large amounts of data and finding statistical patterns in huge labeled datasets. Unfortunately, machine computation is not yet very good at robust small scale contextual understanding, such as determining if a circular object in an image is a face or a wheel. In contrast, humans are exceptionally good at this kind of task. The lack of robustness at the small scale, limits the robustness of the intended large scale understanding. Our work seeks to enhance machine understanding of image based data by including human computational units as an element inside larger computational systems. By using human input as sub-routines inside a larger computational system, small scale annotation and labeling can be achieved robustly. These labels will in turn allow robust computer understanding of large datasets.
Host: Josephine Olivas