Addressing Practical Challenges in Artificial Intelligence from the Medical Domain to General Tasks
COM1 Level 2
SR10, COM1-02-10
Abstract:
Artificial intelligence has achieved much success thanks to abundant training data. However, most of existing algorithms are designed for idea scenario, leaving a large gap before practical implementation. For example, the description is full of ambiguous and uncertainty while the machine learning algorithm requires precise label. In general domain, the real-life scenario involves dark or over-exposure illumination, which is hard for existing computer vision techniques. In this talk, Dr Gu Lin will discuss these gaps and the solutions for it.
Biodata:
Dr. Lin Gu is a research scientist at RIKEN AIP, Japan and a special researcher at the University of Tokyo. He is with specific interest and expertise in the application of Artificial Intelligence in Medical Imaging and Computational Photography. Before moving to Japan, he was a postdoctoral research fellow at A*STAR, Singapore working on machine learning on biomedical imaging. He is now the project manager for Moonshot Program on Continuous Learning and Memory Mechanism and ACT-X Program on Gaze Assisted AI. His research interest lies in simulating human's neocortex to enhance AI, especially its application on medical fields.