Keynote Speech I
Privacy-preserving Precision Medicine with AI, VR, and Blockchain
Edward Y. Chang
HTC Health Care, Taiwan
ABSTRACT This talk presents how effective utilization of AI, VR and Blockchain can help advance precision medicine. The talk addresses three deployment challenges. First, given that in the healthcare domain big data is typically not available, this talk evaluates remedies to the small data issue. Second, since data privacy must be safeguarded, this talk presents DeepLinQ, a multi-layer blockchain architecture, and explains how DeepLinQ can facilitate regulation (e.g., HIPAA) compliance. Third, given sufficient data for training a classifier for disease diagnosis, we present how training can be performed economically while producing an energy-efficient and accurate model. We use clinical cases to illustrate how AI, VR, and Blockchain can together advance precision medicine in a privacy-preserving manner.
BIOSKETCH Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC and as a visiting professor at UC Berkeley. Ed’s most notable recent work includes co-leading the DeepQ project to win the XPRIZE medical IoT context in 2017 with a 1M USD prize. The AI architecture powering DeepQ is the same as that applied to powering Vivepaper, an AR product Ed’s team launched in 2016 to support immersive AR experiences. Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, and Google Q&A. His 2006-2011 contributions in data-driven machine learning (US patents 8798375 and 9547914) and his ImageNet sponsorship helped fuel the success of AlexNet and the recent resurgence of AI. His developed open-source projects in parallel SVMs, parallel LDA, parallel spectral clustering, and parallel frequent itemset mining (adopted by Berkeley Spark) have been collectively downloaded over 30,000 times. Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara. He joined UCSB in 1999 after receiving his PhD from Stanford University. Ed is an IEEE Fellow for his contributions to scalable machine learning.
Keynote Speech II
AI Democratization in Taiwan
Academia Sinica, Taiwan
ABSTRACT Some day in March 2017, I encountered Prof. HT Kung from Harvard University in the campus of Academia Sinica which is located in Taipei, Taiwan. We were both excited about emerging deep learning technology and looking forward to its potentials in industry technology upgrade in a variety of ways.
We started our journey by visiting more than a dozen of manufacturing companies in Taiwan and collecting the challenges they face when adopting AI in the different aspects of business and manufacturing processes. The second step involved assembling an AI consulting team called Project Theta in order to address the common technical challenges faced by manufacturers. Within the next six months, Project Theta did a wonderful job in resolving those common challenges using deep learning technology. However, how to scale our efforts became the biggest challenge of our own.
Thus, Taiwan AI Academy was founded in January 2018 to address the scaling problem, after we finally identified that the lack of AI talents is the most fundamental and critical issue in Taiwan in this AI era. Taiwan AI Academy is unique in many ways, in particular its close collaborations with industry in order to empower domain experts from various fields by machine learning and deep learning techniques within a short 3-4 month.
In this talk, I will elaborate the story — starting from the achievements of Project Theta — to the foundation of Taiwan AI Academy, and how we will transform the AI talent development in Taiwan starting from these efforts.
BIOSKETCH Dr. Sheng-Wei Chen (a.k.a. Kuan-Ta Chen) is the Chairman of Taiwan Data Science Association, the Director of Artificial Intelligence Foundation, the CEO of Taiwan AI Academy, and the CTO of E.SUN Financial Holding Company. He is also a Research Fellow at the Institute of Information Science and the Research Center for Information Technology Innovation (joint appointment) of Academia Sinica. He He was an Assistant Research Fellow from 2006 to 2011 and an Associate Research Fellow from 2011 to 2015 at the Institute of Information Science, Academia Sinica. He received his Ph.D. in Electrical Engineering from National Taiwan University in 2006, and his B.S. and M.S. in Computer Science from National Tsing Hua University in 1998 and 2000, respectively. Prior to taking his academic path, he was active as a programmer specialized in Windows system programming, a technical writer, and a freeware/shareware developer.
His research interests span in various application domains of AI and data science, including social computing, crowdsourcing, and computational social science, quality of experience (QoE), and multimedia systems. He received the Best Paper Award in IWSEC 2008 and K. T. Li Distinguished Young Scholar Award from ACM Taipei/Taiwan Chapter in 2009. He also received the Outstanding Young Electrical Engineer Award from The Chinese Institute of Electrical Engineering in 2010, the Young Scholar’s Creativity Award from Foundation for the Advancement of Outstanding Scholarship in 2013, and IEEE ComSoc MMTC Best Journal Paper Award in 2014. He was an Associate Editor of IEEE Transactions on Multimedia (IEEE TMM) during 2011 to 2014 and has been an Associate Editor of ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM) since 2015. He organized ACM Multimedia Systems 2017 in Taiwan and served the lead program chair of ACM Multimedia 2017. He is a Senior Member of ACM and a Senior Member of IEEE.
Keynote Speech III
The Age of Human-Robot Collaboration
Stanford University, USA
ABSTRACT Robotics is undergoing a major transformation in scope and dimension with accelerating impact on the economy, production, and culture of our global society. The generations of robots now being developed will increasingly touch people and their lives. They will explore, work, and interact with humans in their homes, workplaces, in new production systems, and in challenging field domains. The emerging robots will provide increased support in mining, underwater, hostile environments, as well as in domestic, health, industry, and service applications. Combining the experience and cognitive abilities of the human with the strength, dependability, reach, and endurance of robots will fuel a wide range of new robotic applications. The discussion focuses on design concepts, control architectures, task primitives and strategies that bring human modeling and skill understanding to the development of this new generation of collaborative robots.
BIOSKETCH Oussama Khatib received his Doctorate degree in Electrical Engineering from Sup’Aero, Toulouse, France, in 1980. He is Professor of Computer Science at Stanford University. His work on advanced robotics focuses on methodologies and technologies in human-centered robotics including humanoid control architectures, human motion synthesis, interactive dynamic simulation, haptics, and human- friendly robot design. He is Co-Editor of the Springer Tracts in Advanced Robotics series, and has served on the Editorial Boards of several journals as well as the Chair or Co-Chair of numerous international conferences. He co-edited the Springer Handbook of Robotics, which received the PROSE Award. He is a Fellow of IEEE and has served as a Distinguished Lecturer. He is the President of the International Foundation of Robotics Research (IFRR). Professor Khatib is a recipient of the Japan Robot Association (JARA) Award in Research and Development. In 2010 he received the IEEE RAS Pioneer Award in Robotics and Automation for his fundamental pioneering contributions in robotics research, visionary leadership, and life-long commitment to the field. Professor Khatib received the 2013 IEEE RAS Distinguished Service Award in recognition of his vision and leadership for the Robotics and Automation Society, in establishing and sustaining conferences in robotics and related areas, publishing influential monographs and handbooks and training and mentoring the next generation of leaders in robotics education and research. In 2014, Professor Khatib received the 2014 IEEE RAS George Saridis Leadership Award in Robotics and Automation.
Keynote Speech IV
Simulation for Intelligent Systems
ABSTRACT Why is our understanding of sensorimotor control behind our understanding of perception? I will talk about structural differences between perception and control, and how these differences can be mitigated to help advance sensorimotor control systems. Judicious use of simulation can play an important role and I will describe some simulation tools that we have built and deployed. I will also present recent work on autonomous driving, navigation, and transfer of sensorimotor skills from simulation to the physical world.
BIOSKETCH Vladlen Koltun is a Senior Principal Researcher and the director of the Intelligent Systems Lab at Intel. His lab conducts high-impact basic research on intelligent systems. Vladlen received a PhD in 2002 for new results in theoretical computational geometry, spent three years at UC Berkeley as a postdoc in the theory group, and joined the Stanford Computer Science faculty in 2005 as a theoretician. He joined Intel in 2015 to establish a new lab devoted to basic research.
Keynote Speech V
Towards Evidence Based Education with Educational Big Data and Artificial Intelligence
Kyoto University, Japan
ABSTRACT The multi-disciplinary research approach of Learning Analytics (LA) has provided methods to understand learning logs collected during varied teaching-learning activities and potentially enrich such experiences. This talk will explain how technology can help to extract evidence of effective teaching-learning practices by applying the knowledge base of LA and developing novel techniques. It focuses discussions on realizing a technology-enhanced evidence-based education and learning (TEEL) system. This talk will propose the Learning Evidence Analytics Framework (LEAF) and draw a research road-map of educational big data-driven evidence-based education system. Teachers can refine their instructional practices, learners can enhance learning experiences and researchers can study the dynamics of the teaching-learning process with it. While LA platforms gathers and analyses the data, there is a lack of specific design framework to capture the technology-enhanced teaching-learning practices. Finally, this talk will present the research challenges towards smart evidence based education.
BIOSKETCH Hiroaki Ogata is a Professor at the Academic Center for Computing and Media Studies, and the Graduate School of Informatics, Kyoto University, and an associate member of Science Council of Japan. His research includes Computer Supported Ubiquitous and Mobile Learning, CSCL, CALL, and Learning Analytics. He has published more than 300 peer-reviewed papers including SSCI Journals and international conferences. He has received several Best Paper Awards and gave keynote lectures in several countries. He is an associate editor of IEEE Transactions on Learning Technologies. RPTEL and IJMLO, and also an editorial board member of IJCSCL, IJAIED, and JLA. He is an EC member of SOLAR and APSCE societies.