I feel the most rewarding, when users say they really like using our service. I felt really proud when I saw a user’s comment that wondered if a review prepared by AI was ‘really written by a machine or not.'
However, you get to experience a series of troubles until you could commercialize a service targeting the public based on research results, because a large number of exceptions occur, when the service is to be applied to reality. Commercialization means to be able to respond to the aforementioned exceptions that rarely occur. For instance, let’s talk about the weather forecast published through Yonhap News. There is not much data for a machine to learn in case a forecast is to be made about a once-in-a-decade heavy rain fall or heat wave. On PAIGE, a human being may talk about something that the machine has never heard of. However, even in this case, AI should not stay silent. We need to decide an appropriate answer to respond to such a case.
However, these problems are difficult to be discovered in case if we stop our work after publishing a paper or filing a patent application. The difficulties that we experience while working with the data in reality may suggest a new research subject or a direction for further research. If you talk to the people that actually use the service, you get to understand where AI is the most needed. Unless it is commercialized, a technology will forever stay inside the lab.