Human-machine Intelligence: A Design Paradigm
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Date
2019-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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Distribution Rights granted to UMBC by the author.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Abstract
In this age of artificial intelligence, we are witnessing the power of human-machine collaboration in transforming the way we live, work, and solve different problems. Humans and machines can complement each other in resolving intractable and sophisticated issues that are hard or impossible for computers alone. The collaboration achieved great results addressing the problems of digitizing books, detecting star clusters, and transcribing audio and video, etc. Researchers investigated these problems in isolation. There is no clear guideline about why and when human intelligence can be useful and, if so, what design pattern to follow. Integrating humans will add human knowledge, which can help to solve complex, open-ended, and uncertain problems. However, this will also bring the human limitations of less automation, less precision, and biased opinion. Analyzing the tread-off of integrating humans is necessary before designing a collaborative system. In this dissertations, we have addressed the issues described above and propose a collaborative system design paradigm. Analyzing the general architecture of such a system, we found that human intelligence can help at three different functional positions - data preprocessing, feature extraction, and decision making. In all these functional areas, humans can help to improve the performance of a system. We also provide the conditions that will help a system to get rid of humans in the long run. We have developed four different systems that represent all four conditions mentioned above and provide detailed guidelines. We provided detailed steps of integrating humans in decision making, feature extraction, and preprocessing through our weed identification system, resource localization, and group conversation analysis system, respectively. We also explained the conditions and steps to reduce human contribution in the long run through the object detection system. In each system, we showed - a) why humans over computer intelligence are necessary? b) what are the steps to integrate human knowledge to overcome the difficulties? c) what are the trade-off on integrating humans instead of machines?