Making the most of quite little: Improving AI training for edge sensor time series
Engineers at the Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for improving the way artificial intelligence classifiers, such as neural networks, can be trained based on limited amounts of sensor data. The emerging applications of the Internet of Things often require edge devices that can reliably classify behaviors and situations based on time series.
Physical reservoir computing (PRC) utilizing synaptic devices shows significant promise for edge AI. Researchers from the Tokyo University of Science have introduced a novel self-powered dye-sensitized solar cell-based device that mimics human synaptic behavior for efficient edge AI processing, inspired by the eye's afterimage phenomenon. The device has light intensity-controllable…
This paper was accepted at the Learning from Time Series for Health workshop at NeurIPS 2025. Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show that large language models (LLMs) can be used for late fusion for…
To provide high-quality medical care to its population — around 30% of whom are 65 or older — Japan is pursuing sovereign AI initiatives supporting nearly every aspect of healthcare. AI tools trained on country-specific data and local compute infrastructure are supercharging the abilities of Japan’s clinicians and researchers so…