A joint research team from NIMS, Tokyo University of Science, and Kobe University has developed a new artificial intelligence (AI) device that exploits ion behavior to perform information processing. The team succeeded in reducing the computational load to about 1/100 of that required for conventional deep learning. The technology is expected to contribute to enhancing the information processing performance of "edge AI" operating directly on terminal equipment (an edge device). This research was published in ACS Nano on October 14, 2025.

New research from Fujita Health University reveals that talking can subtly delay the eyes’ ability to detect and stabilize on visual information. In experiments comparing talking, listening, and control conditions, only talking caused slower reaction, movement, and fixation times during rapid eye-movement tasks. Because driving depends heavily on fast gaze shifts, these delays may impair hazard detection and slow physical responses. The findings highlight the hidden risks of engaging in conversation while driving.

A research team developed a fully automated, drone-based phenotyping workflow that can measure key peanut canopy and architecture traits at scale—without the usual manual plot tracing and time-consuming image cleanup.

A research team has developed a new hybrid artificial intelligence framework that can accurately estimate leaf nitrogen content without relying on labor-intensive field measurements.

A research team has developed a new model, PlantIF, that addresses one of the most pressing challenges in agriculture: the accurate and timely diagnosis of plant diseases.

A research team has developed CornPheno, a smartphone-based tool designed to revolutionize corn phenotyping.

A research team has developed a method for improving crop growth monitoring by accurately estimating the fraction of absorbed photosynthetically active radiation (fAPAR) throughout the entire rice growth cycle.

Humans possess a remarkable balance between stability and flexibility, enabling them to quickly establish new plans and adjust goals even in the face of sudden changes. However, "Model-Free reinforcement learning," which is widely used in robotics and exemplified by AlphaGo’s famous match against Lee Sedol, struggle to achieve these two capabilities simultaneously. KAIST's research team has discovered that the secret lies in the unique information processing method within the prefrontal cortex, a principle that could serve as the foundation for developing "Brain-like AI” that is both flexible and stable.

Medical image segmentation is a fundamental component of many clinical applications such as computer-aided diagnosis, radiotherapy planning, and preoperative planning. Its accuracy and stability directly affect subsequent quantitative analysis and clinical decision-making. Based on a systematic analysis of feature utilization in U-shaped architectures, Prof. Zhao Qi’s team at Liaoning University of Science and Technology and Prof. Shuai Jianwei’s team at the Wenzhou Institute of the University of Chinese Academy of Sciences have developed an E-shaped medical image segmentation framework that differs from conventional symmetric decoders.