学习随机数以实现人工智能部署后获取新知识的可追加记忆系统
Learning Random Numbers to Realize Appendable Memory System for Artificial Intelligence to Acquire New Knowledge after Deployment
AI总结：
本研究提出一种通过随机化学习数据以移除特征的方法，构建了由 Memorizer 和 Recaller 组成、无需参数更新即可在部署后获取新知识的 Appendable Memory system。
摘要：
在本研究中，我们开发了一种学习方法，用于构建能够在不更新参数的情况下记忆数据并回忆数据的神经网络系统。我们使用该方法构建的系统被称为 Appendable Memory system（可追加记忆系统）。Appendable Memory system 使人工智能（AI）即使在部署后也能获取新知识。它由两个 AI 组成：Memorizer（记忆器）和 Recaller（回忆器）。该系统是使用神经网络构建的键值存储。Memorizer 接收数据并将其存储在 Appendable Memory 向量中，该向量在 AI 获取新知识时动态更新。同时，Recaller 从 Appendable Memory 向量中检索信息。在本研究中，我们希望教给 AI 的是记忆和回忆信息的操作。然而，传统的机器学习方法让 AI 学习学习数据集中固有的特征。我们证明，我们打算创建的系统无法通过当前的机器学习方法实现，即无法通过仅重复 AI 的输入和输出学习序列来实现。相反，我们提出了一种通过完全移除学习数据集中包含的特征来教 AI 学习操作的方法。具体而言，我们将参与学习的所有数据随机化。这一措施防止了 AI 学习数据的特征。本研究提出的学习方法不同于传统的机器学习方法，为构建一个能够在有限记忆中存储信息并在日后回忆的 AI 系统提供了基础方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
