Abstract
Reflection on one’s thought process and making corrections to it if there exists dissatisfaction in its performance is, perhaps, one of the important traits of intelligence. However, such high-level abstract concepts mandatory for Artificial General Intelligence can be modelled even at the low level of narrow Machine Learning algorithms. Here, we present the self-awareness mechanism emulation in the form of an artificial neural network (ANN) observing patterns in activations of another underlying ANN in a search for indications of the high uncertainty of the underlying ANN and, therefore, the untrustworthiness of its predictions. The underlying ANN is a CNN employed for tasks of face recognition and facial expression. The self-awareness ANN has a memory region where its past performance information is stored, and its learnable parameters are adjusted during the training to optimize the performance. The same memory mechanism is used during the test phase for the continuous reassessment of the learning parameters after each consecutive test run.
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Selitskiy, S. (2023). Elements of Continuous Reassessment and Uncertainty Self-awareness: A Narrow Implementation for Face and Facial Expression Recognition. In: Nakamatsu, K., Patnaik, S., Kountchev, R., Li, R., Aharari, A. (eds) Advanced Intelligent Virtual Reality Technologies. Smart Innovation, Systems and Technologies, vol 330. Springer, Singapore. https://doi.org/10.1007/978-981-19-7742-8_5
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