The 2024 Nobel Prize in Physics was awarded to Geoffrey Hinton and John Hopfield for their research in artificial intelligence (AI) technology, particularly neural networks. This was unusual for a physics prize and generated considerable discussion among AI researchers. Hinton proposed a learning algorithm for hierarchical neural networks, which forms the basis of modern generative AI, while Hopfield developed a statistical mechanical model for the convergence of network neural networks. The fact that these studies were selected for the Nobel Prize in Physics suggests that the selection criteria for the prize are changing significantly. In recent years, the Nobel Prize has increasingly focused on applied technologies, especially those that have a significant impact on human life, rather than on theory.

However, the names of Japanese researchers, particularly Shunichi Amari and Kunihiko Fukushima, are barely mentioned in this report. Although they laid the foundations of neural networks earlier than Hinton and Hopfield, their contributions seem to be underappreciated. In fact, when I attended Professor Amari's lectures as an undergraduate, I mistakenly assumed that the principles of neural networks were simple and unimportant. This article highlights the importance of not just publishing research results, but also making efforts to disseminate them.

Furthermore, the Nobel Prize in Chemistry was awarded to researchers who contributed to protein structure prediction using AI technology, demonstrating the significant role AI technology is playing in the fields of physics and chemistry. How will AI technology develop in the future?

In any case, it is clear that a major movement is occurring in the field of science and technology research. I believe that the CUC Research Center must also quickly publish and disseminate useful research results.