  In this study, we developed a learning method for constructing a neural
network system capable of memorizing data and recalling it without parameter
updates. The system we built using this method is called the Appendable Memory
system. The Appendable Memory system enables an artificial intelligence (AI) to
acquire new knowledge even after deployment. It consists of two AIs: the
Memorizer and the Recaller. This system is a key-value store built using neural
networks. The Memorizer receives data and stores it in the Appendable Memory
vector, which is dynamically updated when the AI acquires new knowledge.
Meanwhile, the Recaller retrieves information from the Appendable Memory
vector. What we want to teach AI in this study are the operations of memorizing
and recalling information. However, traditional machine learning methods make
AI learn features inherent in the learning dataset. We demonstrate that the
systems we intend to create cannot be realized by current machine learning
methods, that is, by merely repeating the input and output learning sequences
with AI. Instead, we propose a method to teach AI to learn operations, by
completely removing the features contained in the learning dataset.
Specifically, we probabilized all the data involved in learning. This measure
prevented AI from learning the features of the data. The learning method
proposed in the study differs from traditional machine learning methods and
provides fundamental approaches for building an AI system that can store
information in a finite memory and recall it at a later date.
