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Journal Article 자율 성장 인공 지능 기술
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Authors
송화전, 김현우, 정의석, 오성찬, 이전우, 강동오, 정준영, 이윤근
Issue Date
2019-08
Citation
전자통신동향분석, v.34, no.4, pp.43-54
ISSN
1225-6455
Publisher
한국전자통신연구원
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.22648/ETRI.2019.J.340405
Abstract
Currently, a majority of artificial intelligence is used to secure big data; however, it is concentrated in a few of major companies. Therefore, automatic data augmentation and efficient learning algorithms for small-scale data will become key elements in future artificial intelligence competitiveness. In addition, it is necessary to develop a technique to learn meanings, correlations, and time-related associations of complex modal knowledge similar to that in humans and expand and transfer semantic prediction/knowledge inference about unknown data. To this end, a neural memory model, which imitates how knowledge in the human brain is processed, needs to be developed to enable knowledge expansion through modality cooperative learning. Moreover, declarative and procedural knowledge in the memory model must also be self-developed through human interaction. In this paper, we reviewed this essential methodology and briefly described achievements that have been made so far.
KSP Keywords
Big Data, Complex modal, Cooperative Learning, Data Augmentation, Efficient learning, Knowledge expansion, Knowledge inference, Memory Model, Procedural Knowledge, Small-scale, artificial intelligence
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: