Keynotes
Research and Education at the Convergence of Frontier Technologies
By Prof. Vikram Kapila
This talk will review the trajectory of education and research activities that began with control technology, transitioned to mechatronics, evolved into robotics, and now has led to convergence research on frontier technologies (robotics, AI, AR/VR, and blockchain).
Vikram Kapila is a Professor of Mechanical and Aerospace Engineering at NYU Tandon School of Engineering, where he directs a Mechatronics, Controls, and Robotics Laboratory; a Research Experience for Teachers Site in Mechatronics and Entrepreneurship; a DR K-12 and an ITEST STEM education research project; all funded by NSF. His research interests are in control system technology, mechatronics, robotics, and STEM education, with current focus on research @ the convergence of frontier technologies (e.g., robotics, AI, AR/VR, blockchain, among others). He is an author or co-author of more than 220 peer-reviewed scholarly publications, including edited books, book chapters, journal papers, and conference proceeding articles. He has received five teaching awards and a leadership award, all at NYU Tandon. Moreover, he is a recipient of 2014-2015 University Distinguished Teaching Award at NYU. He directs K-12 education, training, mentoring, and outreach programs that annually enrich the STEM education of over 1,000 students.
Not deep learning but open innovation learning; Concept model for humanitarian machine learning
By JinHyo Joseph Yun
What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This study builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.
