[Artificial Intelligence] Year Started : 2019

Affiliation and job title should automatically appear from the information that a researcher registered with researchmap.
Data may be outdated or undocumented.
When there is not a connection via the internet, data are not displayed.

Masaaki Iiyama

FishTech for Sustainable Fishery Model

Research Director
Collaborators
Yusuke Tanaka
Yutaka Kurita
Outline

“FishTech” is a new technology which is a collaboration with fishery science, ocean science and informatics. Our goal is to develop FishTech for sustainable fishery which balances economic efficiency with resource management. We develop a new pattern recognition and data assimilation technology which employ domain knowledge of ecology of fish and oceanography, and analyze environmental data acquired in a process of fishing activities. Our technology supports short-term and long-term fishing operation providing suitable fishing spots, oceanographic conditions and fishery management plans

Yoichi Ochiai

Design and Deployment of a xDiversity AI platform for Audio-Visual-Tactile Communication towards an inclusive society

Research Director
Collaborators
Yusuke Sugano
Ken Endo
Tatsuya Honda
Outline

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.

Shimpei Kato

Risk and Anomaly Prediction in Fully Autonomous Driving

Research Director
Collaborators
Kazuya Takeda
Kenji Kono
Outline

This research contributes to production of autonomous driving systems that continuously improve intelligence by run after run. The scope of intelligence includes not only a basic automation capability, such as perception, planning, and control, but also a prediction capability for risk of driving scenes and anomaly of the running system. We believe that risk and anomaly prediction is becoming the most significant capability to ensure safety and comfort of emerging autonomous driving technology. This research develops its platform.

Shin’ichi Satoh

UNDERPIN: Understanding Psychiatric Illness through Natural Language Processing and Media aNalysis

Research Director
Collaborators
Taishiro Kishimoto
Kiyoharu Aizawa
Toshihiko Yamasaki
Kano Yoshinobu
Outline

Psychiatric disorders such as depression, schizophrenia and dementia are diagnosed through patients’ behavior. In order to choose accurate treatments, it is crucial to extract the characteristics of these diseases and understand them thoroughly through patients’ behavior. In this project, we will be utilizing a natural language processing along with multimedia analysis-based approach to digitize/quantify disease characteristics. By doing so, we will expand our understanding of the diseases and eventually lead to better prevention and/or early diagnosis of psychiatric disorders.

Koichi Shinoda

Fast and cost-effective deep learning algorithm platform for video processing in social infrastructure

Research Director
Collaborators
Matsuoka Satoshi
Masaki Onishi
Rio Yokota
Tsuyoshi Murata
Hiroki Nakahara
Suzuki Taiji
Outline

We aim to establish a high-performance real-time deep learning algorithm basis for detecting objects and anomalies from a large amount of high definition videos recorded by drive recorders, surveillance cameras. Computer science researchers specializing in different levels from architectures to applications including GPU fast computation, parallel computation, machine learning, and compactization, collaborate together.

Goichiro Hanaoka

Social Implementation of Privacy-Preserving Data Analytics

Research Director
Collaborators
Shiho Moriai
Kiyoshi Asai
Seiichi Ozawa
Sugawara Takahiro
Outline

It is widely considered to apply advanced artificial intelligence technologies to big data which are collected from IoT and other advanced information systems, but at the same time, exposure of sensitive data is a serious concern. In this research, based on the privacy-preserving data processing technologies from the small phase of this CREST project, we develop engines for universal server-aided secure computation and privacy-preserving machine learning. Furthermore, we aim to deploy these engines in society via industries which participate in our project.

Quick Access

Program

  • CREST
  • PRESTO
  • ACT-I
  • ERATO
  • ACT-X
  • ACCEL
  • ALCA
  • RISTEX
  • manual
  • AIP Network Lab
  • JST ProjectDB
  • Global Activities
  • Diversity
  • SDGs
  • OSpolicy
  • Yuugu
  • Questions