Abstract
An essential question about the human learning system is how humans constantly acquire information in an extremely diverse way, successfully store it and constantly retrieve it for ongoing tasks and current needs. While previous artificial intelligence studies have made significant progress in understanding human learning through visual perception, the important role of nonvisual information in building knowledge systems has not been incorporated. In this paper, we propose a theoretical framework. It aims at providing an insight into how nonvisual cues (referred as “discreet cues”) could be incorporated into current neural network architectures. These cues include multisensory information, context, state of mind, and previous experience. When combined with visual cues, they function in different capacities depending on the ongoing task, and are accordingly categorized as passive cues, active cues, and selectively active cues. We propose a hypothetical, theoretical framework that could help take a new step towards accomplishing lifelong human-inspired learning, and assimilation systems that achieve meaningful and powerful computational intelligence.
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Acknowledgment
We would like to express our sincere gratitude to our professor Dr. Monica Lopez-Gonzalez for her endless encouragement and insightful discussions which helped guide our thoughts throughout this work.
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Seth, A., Guo, W. (2023). Proposing Theoretical Frameworks for Including Discreet Cues and Sleep Phases in Computational Intelligence. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_49
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DOI: https://doi.org/10.1007/978-3-031-16075-2_49
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