Cited 1 time in webofscience Cited 2 time in scopus

Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence

Title
Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence
Authors
Yun, JinHyo JosephLee, DooseokAhn, HeungjuPark, KyungbaeYigitcanlar, Tan
DGIST Authors
Yun, JinHyo JosephLee, DooseokAhn, Heungju
Issue Date
2016-08
Citation
Sustainability, 8(8)
Type
Article
Article Type
Article
Keywords
Autonomous LearningClosed InnovationDirect LearningOpen InnovationSustainability
ISSN
2071-1050
Abstract
What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human's cognitive learning process and firms' open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/5087
DOI
10.3390/su8080797
Publisher
MDPI AG
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology1. Journal Articles
School of Undergraduate Studies1. Journal Articles


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