Sony Corporation announced that by utilizing its DL development framework "Core Library: Neural Network Libraries" in addition to the ABCI, a world-class computing infrastructure for AI processing constructed and operated by AIST, it has achieved the fastest DL speeds in the world. Please refer this page for further information.
The Geoinformation Science Research Team has created MUltiband Satellite Imagery for object Classification (MUSIC) for HotArea (HA) dataset. The HA is a system to automatically detect hotspots (e.g., fires and volcanoes) in the world in mid-resolution satellite data and displays the results on a web-based GIS system. Currently Hotarea utilizes Landsat 8 and Sentinel-2 data in global scale and in some selected regions, respectively.
The Geoinformation Science Research Team has created MUltiband Satellite Imagery for object Classification (MUSIC) to detect Golf Course (GC). There are more than 30,000 golf courses all over the world. They are good targets for research of automatic object detection due to their specific shape, world-wide distribution and common size. MUSIC for GC is the dataset to support the global survey of golf courses based on satellite imagery.
AI Bridging Cloud Infrastructure (ABCI) is the world's first large-scale Open AI Computing Infrastructure, constructed and operated by AIST. ABCI Cloud Service is open from August 2018. ABCI was placed at the 5th of a ranking list in the Top500's high-performance supercomputers, and the 8th of a ranking list in the Green500's energy-efficient supercomputers, June 2018.
Ryuhei Hamaguchi, Specialist for Specific Subject of The Geoinformation Science Research Team, won the first prize in the Building Detection Challenge Category of DeepGlobe Satellite Image Understanding Challenges on CVPR 2018.
NEC and AIST announced a new efficient search technology for the discovery of rare critical events. The technology repeats simulations while AI learns the simulation results. As a result, it efficiently discovers rare critical events that are difficult to discover at the product design stage due to the extremely low probability of their occurrence. Please refer this page for further information.
The number of photovoltaic power plants is growing so rapidly that we must rely on satellite observations and efficient machine learning methods for the global monitoring. MUltiband Satellite Imagery for object Classification (MUSIC) for P3 (Photovoltaic Power Plants) is a training and validation dataset generated by AIRC/AIST to support such a global survey of photovoltaic power plants.
The Service Intelligence Research Team has received FY2017 JSAI (The Japanese Society for Artificial Intelligence) Annual Conference Award. The award was given to Satoshi Nishimura, Takuichi Nishimura and Yo Ehara for their co-authored paper entitled "Autonomous Vehicle System based on Laws and Case Laws" (in Japanese). English version was published at 30th International Workshop on Qualitative Reasoning.
The Social Intelligence Research Team has received IEEE IRIS 2017 (International Symposium on Robotics and Intelligent Sensors) Best paper award. The award was given to Yoko Sasaki and Jirou Nitta for their co-authored paper entitled "Long-Term Demonstration Experiment of Autonomous Mobile Robot in a Science Museum".
The Computational Omics Research Team has received IIBMP 2017 (Informatics In Biology, Medicine and Pharmacology) outstanding poster presentation award. The award was given to Yutaka Saito and Toutai Mituyama for their co-authored paper entitled "Cosearge: an exploratory approach reveals spatial gene co-localization beyond topologically associated domains".