Since the invention of computers, communication through natural language
(actual human language) has been a dream technology. However, natural language
is extremely difficult to mathematically formulate, making it difficult to
realize as an algorithm without considering programming. While there have been
numerous technological developments, one cannot say that any results allowing
free utilization have been achieved thus far. In the case of language learning
in humans, for instance when learning one's mother tongue or foreign language,
one must admit that this process is similar to the adage "practice makes
perfect" in principle, even though the learning method is significant up to a
point. Deep learning has played a central role in contemporary AI technology in
recent years. When applied to natural language processing (NLP), this produced
unprecedented results. Achievements exceeding the initial predictions have been
reported from the results of learning vast amounts of textual data using deep
learning. For instance, four arithmetic operations could be performed without
explicit learning, thereby enabling the explanation of complex images and the
generation of images from corresponding explanatory texts. It is an accurate
example of the learner embodying the concept of "practice makes perfect" by
using vast amounts of textual data. This report provides a technological
explanation of how cutting-edge NLP has made it possible to realize the
"practice makes perfect" principle. Additionally, examples of how this can be
applied to business are provided. We reported in June 2022 in Japanese on the
NLP movement from late 2021 to early 2022. We would like to summarize this as a
memorandum since this is just the initial movement leading to the current large
language models (LLMs).

自计算机发明以来，通过自然语言进行沟通一直是一项梦想技术，但是自然语言非常难以进行数学表达，使得在不考虑程序设计的情况下很难将其实现为算法。然而，近年来深度学习在当代人工智能技术中发挥了核心作用，尤其是在自然语言处理领域，取得了前所未有的成果。本报告提供了前沿的自然语言处理技术如何实现 “熟能生巧” 的技术解释，并提供了其在商业应用中的示例。