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A team from NIMS and the Tokyo University of Science has developed a novel AI device that surpasses traditional models in predicting diabetic blood glucose levels by utilizing few-molecule reservoir computing and molecular vibrations, heralding new possibilities for compact and energy-efficient AI technologies.
Progress in developing compact AI devices using molecular vibrations and confirming their functionality
A collaborative research team from NIMS and Tokyo University of Science has successfully developed a cutting-edge artificial intelligence (AI) device that executes brain-like information processing through few-molecule reservoir computing. This innovation utilizes the molecular vibrations of a select number of organic molecules. By applying this device for the blood glucose level prediction in patients with diabetes, it has significantly outperformed existing AI devices in terms of prediction
![The Deployment of Few Molecule Reservoir Computing Harnessing Surface Enhanced Raman Scattering for Predicting Blood Glucose Levels](https://scitechdaily.com/images/The-Deployment-of-Few-Molecule-Reservoir-Computing-Harnessing-Surface-Enhanced-Raman-Scattering-for-Predicting-Blood-Glucose-Levels-777x336.jpg)
The deployment of few-molecule reservoir computing harnessing surface-enhanced Raman scattering for predicting blood glucose levels. Credit: Takashi Tsuchiya National Institute for Materials Science
The outcome of this study indicates that a minimal quantity of organic molecules can effectively perform computations comparable to a computer. This technological breakthrough of conducting sophisticated information processing with minimal materials and in tiny spaces presents substantial practical benefits. It paves the way for the creation of low-power AI terminal devices that can be integrated with a variety of sensors, opening avenues for broad industrial use.
Reference: “Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating” by Daiki Nishioka, Yoshitaka Shingaya, Takashi Tsuchiya, Tohru Higuchi and Kazuya Terabe, 28 February 2024, SciTechDaily