TY - GEN
T1 - DIPy-AI
T2 - 14th Biologically Inspired Cognitive Architectures Meeting, BICA 2023
AU - Mishra, Amit Kumar
AU - Zhong, Yi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/2/14
Y1 - 2024/2/14
N2 - The paper proposes DIPy-AI, an agile AI architecture based on the data-knowledge-information-wisdom (DIKW) pyramid, for processing sensor data in production environments. DIKW is one of the accepted models abstracting the assimilation of sensory data by the human brain. DIPy-AI aims to address challenges related to data assimilation, quality detection, and modular information extraction. The proposed architecture consists of three layers, viz a sensor-dependent data pre-processing layer, a sensor-agnostic ML layer for converting data into information, and an application-specific layer for knowledge extraction. There are two major merits of the proposed architecture. By having a layered architecture, it can easily be repurposed for different industries. Secondly, this agility in the architecture also facilitates the changing of sensors as well as overall goals of the architecture. The work aligns well with sustainable industrial digitization goals (shared by many countries) and offers a flexible solution applicable to multiple industries, promoting sustainability, data-sharing and architecture sharing.
AB - The paper proposes DIPy-AI, an agile AI architecture based on the data-knowledge-information-wisdom (DIKW) pyramid, for processing sensor data in production environments. DIKW is one of the accepted models abstracting the assimilation of sensory data by the human brain. DIPy-AI aims to address challenges related to data assimilation, quality detection, and modular information extraction. The proposed architecture consists of three layers, viz a sensor-dependent data pre-processing layer, a sensor-agnostic ML layer for converting data into information, and an application-specific layer for knowledge extraction. There are two major merits of the proposed architecture. By having a layered architecture, it can easily be repurposed for different industries. Secondly, this agility in the architecture also facilitates the changing of sensors as well as overall goals of the architecture. The work aligns well with sustainable industrial digitization goals (shared by many countries) and offers a flexible solution applicable to multiple industries, promoting sustainability, data-sharing and architecture sharing.
KW - Brain-inspired architecture
KW - DIKW pyramid
KW - Industrial AI
UR - http://www.scopus.com/inward/record.url?scp=85186683532&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-50381-8_64
DO - 10.1007/978-3-031-50381-8_64
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:85186683532
SN - 9783031503801
T3 - Studies in Computational Intelligence
SP - 604
EP - 611
BT - Biologically Inspired Cognitive Architectures 2023 - Proceedings of the 14th Annual Meeting of the BICA Society
A2 - Samsonovich, Alexei V.
A2 - Liu, Tingting
PB - Springer Nature
Y2 - 13 October 2023 through 15 October 2023
ER -