Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization

Title
Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization
Publication Date
2020
Author(s)
de Oliveira, Caterine Silva
Sanin, Cesar
( author )
OrcID: https://orcid.org/0000-0001-8515-417X
Email: cmaldon3@une.edu.au
UNE Id une-id:cmaldon3
Szczerbicki, Edward
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Elsevier BV
Place of publication
The Netherlands
DOI
10.1016/j.procs.2020.09.179
UNE publication id
une:1959.11/61787
Abstract

Current computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning, combined with cognitive abilities that can broadly generalize to many tasks. However, in practice, perceiving the environment and adapting to unforeseen changes remains elusive, especially for real time applications that has to deal with high-dimensional data processing with strictly low latency. The challenge is not only to extract meaningful information from this data, but to gain knowledge and also to discover insight to optimize the performance of the system. We envision to tackle these difficulties by bringing together the best of machine learning and human cognitive capabilities in a collaborative way. For that, we propose an approach based on a combination of Human-in-the-Loop and Knowledge Discovery in which feedback is used to discover knowledge by enabling users to interactively explore and identify useful information so the system can be continuously trained to gain previously unknown knowledge and also generate new insights to improve human decisions.

Link
Citation
Procedia Computer Science, v.176, p. 3093-3102
ISSN
1877-0509
Start page
3093
End page
3102
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International

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