It has been nearly half a century since I first entered the world of research. During that time, I have witnessed several major changes in the way research is conducted while working on research and development in the fields of systems and information, as well as in the fields of social and business.

The first thing that surprised me was that it became possible to update a computer's operating system online. This was around the time the Internet began to take hold in the mid-1980s. The hardware in question was a LISP machine, the most cutting-edge AI-dedicated machine at the time, and it was around this time that automatic software updates, which are commonplace today, began. The second thing that impressed me was when, at an international conference in Europe in the early 1990s, an email service was provided over a row of expensive workstations. One of the Japanese participants downloaded a Japanese language processing function onto one of the workstations, making it possible to exchange emails in Japanese. This was the first time I truly felt that we were immigrants to Earth.

The third environmental change was just before the 21st century, when web browsers became widespread and Google search engines became available to everyone. And today, as we all know, the barrier between foreign languages and Japanese is gradually disappearing. However, the biggest change I have experienced so far is the change that has occurred in the past year. Yes, in the research field related to us, the sudden spread of generative AI including Chat-GPT has had an enormous impact. It seems that the research methodology itself has changed completely.

Just as the spread of the Internet has transformed business, as advances in measurement technology have transformed physics and astronomy, and as statistics and big data have transformed quantitative research methods in economics and marketing, new technologies are always transforming approaches to research. Moreover, over the past year, generative AI has transformed our world of research, including the field of qualitative research.

Of course, training generative AI requires huge costs and data, and generative AI's output often contains "lies" called hallucinations. However, students and researchers who have conducted research activities in a certain field will not be able to survive unless they master low-threshold cutting-edge technology such as generative AI. In cutting-edge research fields, a large amount of results are published much faster than the speed at which humans can read.

Of course, beginners need to realize what kind of lies are contained in the information generated by generative AI. Fortunately, our university announced guidelines for the use of generative AI in April 2023, and I think it was wise to promote its active use. I hope that new initiatives will be taken in future research at CUC Research Institute. Of course, I used Microsoft Copilot to put this article together. It is a tool that helps me improve my abilities!