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- Development and Integration of Artificial Intelligence Technologies for Innovation Acceleration/
- [Artificial Intelligence] Year Started : 2017
Associate Professor
Faculty of Library, Information and Media Science
University of Tsukuba
Ken Endo | Researcher Sony Computer Science Laboratories, Inc. |
Yusuke Sugano | Associate Professor Graduate School of Information Science and Technology Osaka University |
Tatsuya Honda | Marketing Strategy Headquarters FUJITSU LIMITED |
This project aims to explore AI-assisted human-machine integration techniques for overcoming impairments and disabilities. By connecting assistive hardware and auditory/visual/tactile sensors and actuators with a user-adaptive and interactive learning framework, we propose and develop a proof of concept of our “xDiversity AI platform” to meet the various abilities, needs, and demands in our society. Our final goal is a social design and deployment of the assistive technologies towards an inclusive society.
Professor
Faculty of Global Informatics
Chuo University
The purpose of this research is, with AI technology, to support drafting legally effective documents such as laws and contracts called “legal texts”. In this research we develop an AI system with which users can draft legal texts by inputting the parts of templates of the texts and by which these templates are composed automatically corresponding to various cases from a large number of legal texts. Furthermore using the templates we will develop an automatic verification system for legal texts and an educational supporting system to draft legal texts.
Professor
Information Technology Center
The University of Tokyo
Yuki Kadobayashi | Professor Division of Information Science Nara Institute of Science and Technology |
Keiichi Shima | Deputy Director Research Institute IIJ Innovation Institute |
Satoshi Matsuura | Associate Professor Global Scientific Information and Computing Center Tokyo Institute of Technology |
This research aims to counterwork complex cyber attacks and predict the types of cyber threats and its impacts on managed systems by real-time analysis of cyber threat big data. The counterplan of cyber attacks is critically dependent on the persons who have the knowledge of cyber threats. In case of the organization which doesn’t have such a person, the counteraction against the cyber incidents is delayed, and it increases the problem. We propose the decision support system to help the incident response in the organization. Our contributions in this research are (1) proposing the common analysis methods of cyber threats big data, (2) providing the methodologies and algorithms for the analysis, and (3) publishing the vectorized data as open data.
Professor
Department of Electrical and Electronic Engineering
Tokyo University of Agriculture and Technology
Hidenori Sugano | Associate Professor Department of Neurosurgery Juntendo University |
This project establishes “AI-based diagnosis and treatment-aid” system that learn electroencephalograms (EEG) of epilepsy patients and diagnoses by clinicians. Japan has about one million patients of epilepsy, but there are very limited numbers (about 600) of clinicians who can read and interpret EEG of patients. Epileptic seizures sometimes cause serious traffic accidents, and thus it is highly desirable to take a social measure. In this project, we aim to create an AI that can learn the diagnosis by medical specialists in epilepsy for EEG data measured in hospitals. To do that, we develop techniques to construct a dataset of EEG and to learn the interpretation of the data.
Associate Professor
Faculty of Science and Engineering
Keio University
Masaaki Kondo | Associate Professor Graduate School of Information Science and Technology The University of Tokyo |
Yasuhiko Shioda | Executive Officer Solution Division Fixstars Corporation |
A huge amount of stream data is generated continuously by industries and network services. For automated monitoring and anomaly detection by learning such big data, tendency changes of targets should be reflected to the learned parameters immediately, though deep learning is a time-consuming task. In this project, we will develop an AI infrastructure that can reflect recent data tendency in addition to all the past data to the learned parameters. We will validate our concept through experiments targeting industries and network services.
Associate Professor
Faculty of Information Science and Electrical Engineering
Kyushu University
Eiji Ohno | Professor Faculty of Health Sciences Kyoto Tachibana University |
Hajime Nagahara | Professor Institute for Datability Science Osaka University |
Cytological diagnosis of cancer using specimen is useful to detect cancers at an early and easily curable stage. The project aim is to construct an innovative computer-aided cervical cytology with artificial intelligence for detecting cancer cells reliably. To achieve this, the project will develop technologies for 1) recovering 3D shapes of cells from multi-focus super high-resolution images and 2) identifying normal and cancer cells by deep neural networks using 3D shapes and appearance of cells.