Title: The Future of AI Research: Will Machines Soon Rule the World? 人工智能研究的未来-智能机器人会很快成为世界的主宰吗？
Speaker: Jianbo Shi, Professor, University of Pennsylvania
Professor Shi Jianbo, a prominent AI researcher in Computer Vision and Machine Learning will discuss what AI research is about, where AI research is at, how AI is used in many industries with promises to fundamentally transform those industries, and most importantly what the limitations AI has in applications?
Jianbo Shi studied Computer Science and Mathematics as an undergraduate at Cornell University where he received his B.A. in 1994. He received his Ph.D. degree in Computer Science from University of California at Berkeley in 1998. He joined The Robotics Institute at Carnegie Mellon University in 1999 as a research faculty, where he lead the Human Identification at Distance(HumanID) project, developing vision techniques for human identification and activity inference. In 2003 he joined University of Pennsylvania where he is currently a Professor of Computer and Information Science. In 2007, he was awarded the Longuet-Higgins Prize for his work on Normalized Cuts. From 2013-2014, he worked with Intel to develop Realsense Snapshot depth camera. His current research focuses on first person human behavior analysis and image recognition-segmentation. His other research interests include image/video retrieval, 3D vision, and vision-based desktop computing. His long-term interests center around a broader area of machine intelligence, he wishes to develop a visual thinking module that allows computers not only to understand the environment around us, but also to achieve cognitive abilities such as machine memory and learning. According to Google Scholar, his works have been cited over 32,000.
Title: Deep Learning: from Theory to Applications
Speaker: Pierre Baldi, Chancellor’s Professor, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems, University of California Irvine
The process of learning is essential for building natural or artificial intelligent systems. Thus, not surprisingly, machine learning is at the center of artificial intelligence today. And deep learning–essentially learning in complex systems comprised of multiple processing stages–is at the forefront of machine learning. In the last few years, deep learning has led to major performance advances in a variety of engineering disciplines from computer vision, to speech recognition, to natural language processing, and to robotics.
In this talk, we will first address some fundamental theoretical issues about deep learning through the theory of local learning and deep learning channels. We will then describe inner and outer algorithms for designing deep recursive neural architectures to process structured, variable-size, data such as biological or natural language sequences, phylogenetic or parse trees, and small or large molecules in biochemistry. Finally, we will present various applications of deep learning to problems ranging from the prediction of chemical reactions to the detection of cancer in biomedical images. If time permits, we will discuss the black box problem.
Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Chancellor’s Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long-term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of deep learning, and developed and applied deep learning methods for problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the prediction of protein secondary and tertiary structure in biology. He has written four books and over 300 peer-reviewed articles. He is the recipient of the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and a 2014 Google Faculty Research Award. He is an Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB and has co-founded several startup companies.
Title: Semantic technology to enable “open science” and the enhanced management and reuse of experimental datasets
Speaker: Mark A. Musen, Professor, Head, Stanford Center for Biomedical Informatics Research, Stanford University
When left to their own devices, scientists do a terrible job creating the metadata that describe the experimental datasets that make their way in online repositories. The lack of standardization makes it extremely difficult for other investigators to find relevant datasets, to perform secondary analyses, and to integrate those datasets with other data. At Stanford, we are leading the Center for Expanded Data Annotation and Retrieval (CEDAR), which has the goal of enhancing the authoring of experimental metadata to make online datasets more useful to the scientific community. CEDAR technology includes methods for managing a library of templates for representing metadata, and interoperability with a repository of biomedical ontologies that normalize the way in which the templates may be filled out. CEDAR uses a repository of previously authored metadata from which it learns patterns that drive predictive data entry, making it easier for metadata authors to perform their work. Ongoing collaborations with several major research projects are allowing us to explore how CEDAR may ease access to scientific data sets stored in public repositories and enhance the reuse of the data to drive new discoveries.
Dr. Musen is Professor of Biomedical Informatics and of Biomedical Data Science at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Dr. Musen conducts research related to open science, metadata for enhanced annotation of scientific data sets, intelligent systems, reusable ontologies, and biomedical decision support. His group developed Protégé, the world’s most widely used technology for building and managing terminologies and ontologies. He is principal investigator of the National Center for Biomedical Ontology, one of the original National Centers for Biomedical Computing created by the U.S. National Institutes of Heath (NIH). He is principal investigator of the Center for Expanded Data Annotation and Retrieval (CEDAR). CEDAR is a center of excellence supported by the NIH Big Data to Knowledge Initiative, with the goal of developing new technology to ease the authoring and management of biomedical experimental metadata. Dr. Musen directs the World Health Organization Collaborating Center for Classification, Terminology, and Standards at Stanford University, which has developed much of the information infrastructure for the authoring and management of the 11th edition of the International Classification of Diseases (ICD-11).
Dr. Musen was the recipient of the Donald A. B. Lindberg Award for Innovation in Informatics from the American Medical Informatics Association in 2006. He has been elected to the American College of Medical Informatics, the Association of American Physicians, the International Academy of Health Sciences Informatics, and the National Academy of Medicine. He is founding co-editor-in-chief of the journal Applied Ontology.
Title: Building and Interacting with Domain-Specific Knowledge Bases
Speaker: Yunyao Li, Research Manager, Research Staff Member, IBM Research Almaden
Title: Architecture Innovations in AI Era
Speaker: Yuan Xie, Professor, University of California Santa Barbara
This talk will review the recent progress on computer architecture innovations for hardware accelerators in machine learning, and analyze the future challenges for architecture designs for AI applications. Heterogeneous computing, hardware/software codesign, and addressing memory wall challenges are the keys for the future success of architecture innovations in AI era.
Yuan Xie is a professor in UCSB. He has worked for IBM and AMD, as well as Pennsylvania State University before joining UCSB in 2014. His research areas include architecture, EDA, and VLSI. He received many awards including NSF CAREER award, induced to the Hall of Fame in top architecture conferences (ISCA/HPCA/MICRO). He is an IEEE Fellow.
Title: The social and policy dimensions of AI: A comparative perspective
Speaker: Hong Shen, Research Scientist, Carnegie Mellon University
With the rapid development of AI technologies, there has been a growing scholarly and industrial interest on its social and policy dimensions. National governments and transnational corporations have been consistently calling for accelerating the development of their own AI industries as well as the formation of related social norms and best business practices. From a comparative perspective, this talk first reviews the recent AI policy developments in China and the United States, which are considered as the two most important players in the field, and then discusses the social dimensions of these new movements. These policy and social dimensions, in turn, will have important implications for making investment decisions on AI industry and for understanding the future directions of AI research.
Hong Shen (PhD, University of Illinois at Urbana-Champaign) is a Research Scientist at the Department of Engineering and Public Policy at Carnegie Mellon University. Her research focuses on information industry and policy (with an emphasis on China), and social and policy implications around emerging digital technologies. She was selected as a Google Policy Fellow in 2016.