The continuous advances in computing are reshaping the entire education system, and eventually all disciplines and industries worldwide, under the rapid development in machine learning algorithms, AI and neuromorphic computing, quantum computing, etc.
Workshop on Future Computing invites experts and leaders from academia, industry, and the public sector to share, elaborate, and discuss critical issues, developments, breakthroughs and new perspectives in future computing, including theories, methodologies, architectures, systems, applications, social implications, etc. The annual WFC is organized by Miin Wu School of ComputingSOC, NCKU, and the 1st WFC was held last year and very well received by attendees.
Topics of the workshop this year include Computing Architecture, high-performance & memory-centric computing, AI Robotics, Computational Biomedicine, AI-Assisted Healthcare, AI and Intelligent Mobility, Splendid AI Applications, and visions on future computing.
Miin Wu School of Computing aims to cultivate interdisciplinary and innovative talents with specific domain knowledge and computing expertise. Accordingly, these talents can use advanced computing technology both to solve major social problems and to benefit our nation. Hence, in order to encourage outstanding scholars to lead interdisciplinary research teams and cooperate with SOC, we launched the 2021 NCKU Miin Wu School of Computing AI Summer Summit. The research fields of this summit 2021 focus mainly on Future Computing, AI Robotics, and Computational Biomedicine.
Launching Ceremony of National Intercollegiate Athletic Games 2021
Seminar: Learning without Labeling for Visual Applications
Supervised training with deep Convolutional Neural Networks CNNs has achieved great success in various visual recognition tasks. However, it requires large amount of well-annotated data. Data labeling, especially for large-scale image dataset, is very expensive. How to learn an effective network without the need of training data labeling has become an important problem for many applications.
AI 趨勢跨域論壇II：Data-driven for fault detection and diagnosis
The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are temporal. Most the existing approaches for time series classification are not applicable to adaptively extract the effective feature from a large number of sensor data, accurately detect the fault, and provide the assignable cause for fault diagnosis. This talk presents different methods for fault detection and diagnosis and also extends the topics related to prognostic and health management.