Although research using AI has been advancing in the field of particle physics experiments, AI is only applied to one problem at a time and is regarded as a useful “tool” rather than a form of “intelligence.” Our ultimate research theme is to take this situation one step further and pursue how AI can behave more like intelligence. Starting with the problems of particle physics experiments, we will take on a challenge to incorporate multi-stage problems, for which many experimental particle physicists including ourselves have been working together, into a framework of machine learning with expecting better results than before. Through this research, we will explore the potential of AI that looks like “intelligence” rather than “tool.”

Although problems we encounter seem to be simple at first glance, they are often complex and multi-staged. Since an actual problem involves subproblems, the final solution may be reached only after all the subproblems have been solved. To tackle such a problem in our society, we improve efficiency to solve issues and derive high-quality solutions by assigning people who can understand the whole picture of the problem, and adjust and control its subproblems. Incorporating such a mechanism into AI is expected to improve the efficiency of AI and achieve results that conventional AI could not.

As an AI framework for coordinating individual subproblems with controlling the whole process, we have been working on the research and development of “Multi-AI,” in which the control AI learns from a group of AIs (i.e., a set of machine learning models) configured for each function and then selects an appropriate AI from them for each subproblem. So far, we have developed a framework for connecting, optimizing and selecting multi-step machine learning models. In the future, we will conduct advanced research on parameter optimization to handle complex loss functions in the multi-step processing. We will also investigate the feasibility of getting information that can be understood by humans from intermediate data between machine learning models, and solving problems by reusing trained machine learning models (e.g., multi-staged transfer learning).

Concurrently, we will apply this Multi-AI framework to the data of particle physics experiments conducted at the International Center for Elementary Particle Physics, the University of Tokyo. In our experiments, we handle big data of several hundred petabytes (1 petabyte is 1000 trillion bytes). To discover new subatomic particles, etc., from such big data, we need to solve multi-stage problems, as shown in the discovery of the Higgs boson in 2012. If working effectively, Multi-AI will not only advance research in the field of particle physics but also demonstrate its applicability. Furthermore, we aim to demonstrate the effectiveness of the framework developed in this project by applying it to problems in general society other than particle physics.

Considering two types of AI: one that solves a large problem as a single end-to-end machine learning model and the other that solves subproblems by subdividing a problem, we think that the latter is better in terms of human interpretability. Despite the (slight) performance loss that comes with subdividing and multi-staging, if interpretability can be ensured, such an AI could be used in order to solve problems that require explanation for their solutions in both natural science, such as particle physics experiments, and the real world.