Knowledge is the base of intelligence. Our research on semantic information processing is aimed for automatic acquisition of knowledge. We focus on difficulties for data processing: inconsistent forms of data, complicated ontology, implicit description, temporal change of credibility, difference of sources, and so on. We produce technologies for knowledge extraction from varieties of data.

Machine learning's capabilities in learning relationship between variables, evaluating the importance of variables, and discovering hidden important variables are indispensable for modern artificial intelligence systems. These abilities enable us to classify, discriminate, predict, and interpolate data with various properties. The Machine Learning Research Team conducts a wide range of research on the theoretical foundations, algorithms, and applications of advanced machine learning technologies, such as Bayesian modeling, kernel learning, and deep learning. Expected applications include the analysis of chemical reaction data, physical simulation data, traffic data, fisheries data, and sensory-motor data from robots. In addition, we will contribute to facilitate communication between humans and artificial intelligence systems in collaboration with other research teams.

Our team is conducting social problem-solving oriented research on utilization of artificial intelligence technology. We are now tackling mainly toward practical use of AI-aided diagnosis systems based on anomaly detection with image and acoustic analysis for social-infrastructure maintenance, medical care, and healthcare. We aim for the early technology- bridging by close collaboration with business operators in each problem domain and turning the PDCA in a short period. We make a contribution to horizontal deployment of AI technology through accumulation of know-hows for its utilization in the real-world, and extraction of their common points.

Rational social system design is required recently, because social systems are tightly coupled with interactions among multiple people and services by information technologies. To establish an engineering framework to support such design, our team focuses on technologies to sense and simulate human behaviors in social level toward evaluation and prediction of effects of new social-level services.

We contribute to develop technologies for data analysis and artificial intelligence applied to large-scale biological "omics" data to mine valuable knowledge. Our technologies include genomic/epigenomic data analysis, genetic network analysis, and protein structure/interaction simulation.

The Computer Vision Research Team conducts research to acquire information in the real environment such as 3D measurement and 3D modeling, and to develop machine learning technology with visual information such as video and 3D information for understanding the environment. We aim to improve these basic technologies and apply them to contribute to social life of people.

Our team aims to develop AI technologies that integrate data-driven and knowledge-driven AI to understand the Semantics of human activities. Human activities in daily living spaces are highly individualized and composed of various elements, including people, objects, and their interactions. Interpreting these highly individualized situations still depends heavily on human knowledge. To capture and integrate the multi-modality and heterogeneity nature of these data and knowledge, we conduct research on Knowledge Graphs, Machine Learning, Natural Language Processing, Embodied-AI, and Human-Agent-Interaction research.

Our research team is working towards the development of AI technologies that can recognize and understand various "media” such as audio, video, text, and other sensory data in an integrated manner. Through the research and development on a variety of real-world data, we will contribute to support a wide range of fields including not only the human behavior analysis but also industrial machinery/infrastructure.

The Data Knowledge Integration (DKI) research team specializes in goal-oriented knowledge graphs and ontology development, context-aware dialogue modeling for AI-based interaction management. Using such technologies, the DKI team is working on uncovering the semantics of human behaviour, and developing AI technology that can recognize and support various episodes of daily activities by integrating observation data and knowledge into a context-aware dialogue model that allows natural interaction in a variety of cases where it is necessary for humans to have dialogue interactions with other agents and with the smart environment.

We develop computational methods for conducting the knowledge discovery related to biomolecules from the diverse and vast biological data, such as genome information. We aim to apply our methods to perform biomedical research of biomolecules such as disease factor analysis and drug discovery.

The Signal Processing Research Team (SPRT) is engaged in researching and developing sensor and signal processing technologies that contribute to understanding human and environmental states, based on signal processing of various sensors, such as speech, acoustic, and tactile sensors.

The Data Platform Research Team is working towards the development of AI data platforms that can support the data life cycle through the creation of data ecosystems that combine distributed parallel processing, knowledge representation and reasoning, and machine learning techniques. In particular, we focus on Data Governance Platform that can continuously integrate heterogeneous data captured from various sensing devices and autonomously augment intelligence of human beings and things. Our platform will allow users to be able to understand real-world situations, explore related data, learn a meaningful pattern, predict the future, and finally adapt to their surrounding situation.