
Seminar Information
The AIRC at AIST holds monthly seminars on AI, by people from in and outside AIRC, to promote the exchange of information on AI. We expect them to attract a large audience, and give rise to lively debate.
These seminars are open to everyone, but registration is required. Admission is free.
These seminars are open to everyone, but registration is required. Admission is free.
Date | Title | Outline |
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2017/07/10 | 4th AIST AI Workshop, "Workshop on Container Technologies for Convergence of HPC and AI/Big Data Analysis" | At AIST, we are currently preparing to launch a cloud platform (AI Bridging Cloud, AIBC), with world-class AI processing performance, in 2018. In addition, at RWBC-OIL (AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory), in order to implement deep learning, machine learning, and big data analytics efficiently and smoothly on ABCI and Tokyo Tech’s TSUBAME supercomputer, we are working to create an open platform where HPC converges with AI and big data processing systems. In this workshop, seminars on the latest developments in HPC container technology and actual applications will be given by experts in each field, and we will share the latest trends and ongoing technical issues. |
2017/06/30 | 16th AIST Artificial Intelligence Seminar, "Monitoring the Integrity of Infrastructure Using Artificial Intelligence" | In Japan, many bridges, tunnels, and other items of public infrastructure were constructed during an era of rapid growth; now, they are all becoming superannuated concurrently. How to maintain and manage this infrastructure has become an urgent social issue; similar issues have also arisen regarding not only large structures, but also types of infrastructure such as water pipes and power poles. Moreover, in some fields, such as power-plant and industrial infrastructure, experienced engineers are in short supply; consequently, some tasks are approaching the limits of conventional labor-intensive maintenance. Under these circumstances, there is a need to shift from after-the-fact response to pre-emptive maintenance. Accordingly, expectations are high regarding the use of artificial intelligence that can monitor large volumes of sensor data. For this seminar, we have invited Professor Takehisa Yairi (University of Tokyo), a researcher at the forefront of this field. Professor Yairi will give an overview of abnormality detection methods based on machine learning, and present examples of applications. We will also present examples of the latest research by AIST. |