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Title: Evolution of Knowledge in Social Media and Their Relationship to an Evolving Real World
The physical world evolves. The cyber world evolves and grows with big data, with social media as a major component of information growth. Classic ML models are limited by their static training data with implicit Complete and Timeless Knowledge assumptions. In an evolving world, static training data suffer from knowledge obsolescence due to truly novel timely information. Knowledge obsolescence introduces a widening distance between static ML models and the evolving world, called cyber-physical gap. Periodic retraining of new models may restore their accuracy temporarily, but subsequently their performance will deteriorate with widening cyber-physical gap. Knowledge obsolescence affects statically trained models of any size, including LLMs. Two major research challenges arise from cyber-physical gap: (1) collection and incorporation of space-time aware ground truth training data, and (2) understanding and capturing of the varying speed of information and knowledge evolution when the physical and cyber worlds evolve.  more » « less
Award ID(s):
2039653
PAR ID:
10489207
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
Proceedings of 2023 IEEE International Conference on Cognitive Machine Intelligence (CogMI)
Subject(s) / Keyword(s):
machine learning, dynamic model performance, evolution of knowledge, evolving social media
Format(s):
Medium: X
Location:
Atlanta, GA, USA
Sponsoring Org:
National Science Foundation
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