Professor Rayid Ghani University of Chicago
Rayid Ghani is the Director of the Center for Data Science and Public Policy, Chief Data Scientist at the Urban Center on Computation and Data, Research Director at the Computation Institute (a joint institute of Argonne National Laboratory and The University of Chicago), and a Senior Fellow at the Harris School of Public Policy at the University of Chicago. He is also the co-founder of Edgeflip, an analytics startup that grew out of the Obama 2012 Campaign, focused on social media products for non-profits, advocacy groups, and charities.
Ghani is currently in charge of the Eric & Wendy Schmidt Data Science for Social Good Summer Fellowship, at the University of Chicago.
Title: Data Science for Social Impact: Case Studies, Challenges, and Opportunities
Abstract: Can Data Science help reduce police violence and misconduct? Can it help prevent children from getting lead poisoning? Can it help cities better target limited resources to improve lives of citizens? We’re all aware of the data science hype right now but turning this hype into any social impact takes effort. In this talk, I’ll discuss lessons learned while working on dozens of projects over the past few years with non-profits and governments on high-impact social challenges. These lessons span from challenges these organizations face when trying to use data science, to understanding how to effectively train and build cross-disciplinary teams to do practical data science, as well as what machine learning and social science research challenges need to be tackled, and what tools and techniques need to be developed in order to have a social and policy impact with data science and machine learning.
Professor Zhi-Hua Zhou Nanjing University
Zhi-Hua Zhou received his B.Sc., M.Sc. and Ph.D. degrees in computer science from Nanjing University, China, in 1996, 1998 and 2000, respectively, all with the highest honor. He joined the Department of Computer Science & Technology of Nanjing University as an Assistant Professor in 2001, and at present he is a Professor, Deputy Dean of the Department of Computer Science and Technology, Standing Deputy Director of the National Key Lab for Novel Software Technology, and Founding Director of LAMDA (the Institute of Machine Learning and Data Mining) at Nanjing University. He is an ACM Distinguished Scientist and Fellow of the AAAI, CCF, IAPR, IEEE, IET/IEE.
Title: Towards Safe Semi-Supervised Learning
Abstract: In many real tasks there are big data but only a small amount of them are with labels, and it is crucial to exploit unlabeled data to help improve the learning performance. Semi-supervised learning is a mainstream technique for this purpose. Although there are many successful stories of semi-supervised learning, it has been found that utilizing unlabeled data may hurt the learning performance in many cases. Thus, it is desirable to have safe semi-supervised learning approaches that are able to improve the performance, and never significantly worse than pure supervised learning. In this talk we will give an introduction to some results along this line of research.
Professor Maurice Pagnucco University of NSW
Maurice Pagnucco is a Professor of Computer Science and Engieering, Deputy Dean (Education) of the Faculty of Engineering and Head of the School of Computer Science and Engineering at UNSW. He joined UNSW in 2001 as a Senior Lecturer and has held the position of Head of School since 2010 and Deputy Dean (Education) since 2015. He has also held appointments at the University of Toronto, Macquarie University and the University of Sydney. Maurice obtained his Bachelor of Science (Hons I) and PhD degrees in Computer Science from the University of Sydney. During his undergraduate studies he also spent a year at the Department of Computer Science of the University of Milan, Italy. His research is focussed on Artificial Intelligence with particular emphasis on Knowledge Representation and Reasoning, Cognitive Robotics, Belief Change and Reasoning About Actions. Maurice was the programme director of the Decision Making theme in the ARC Centre of Excellence for Autonomous Systems and a co-director of the UNSW iCinema Centre for Interactive Cinema Research. His collaboration with the UNSW iCinema Centre for Interactive Cinema Research resulted in a world-first interactive cinema piece controlled using artificial intelligence techniques that premiered at the Sydney Film Festival in 2011
Title: The rise of cognitive robots
Abstract: The fields of artificial intelligence and robotics have seen rapid developments over the past decade. Improvements in the sophistication, size and price of sensors and actuators have led to the development of robots that are being deployed in increasingly complex environments. Along with this progress, significant work has led to the development of algorithms that provide for, among other things, localisation, navigation and manipulation as well as the development of software systems—robot middleware—for controlling robots in certain applications. While there has been some work on high-level controllers for robots there is a lot of scope for improvement.
In this talk we will look at the field of cognitive robotics. Cognitive robots reason about their environment and what their sensors tell them, in order to determine their actions. In particular, we look at the experimental cognitive robotics programming languages that have grown out of the research in symbolic artificial intelligence in the areas of reasoning about action and that of planning and scheduling. These languages aim to facilitate the writing of high-level control programs that use symbolic reasoning in their execution to determine an appropriate course of action in carrying out tasks. We also look at experimental applications of this work in robotics and in interactive cinema with the control of virtual characters
Professor Takayuki Ito Nagoya Institute of Technology
Dr. Takayuki ITO is Professor of Nagoya Institute of Technology. He received the B.E., M.E, and Doctor of Engineering from the Nagoya Institute of Technology in 1995, 1997, and 2000, respectively. From 1999 to 2001, he was a research fellow of the Japan Society for the Promotion of Science (JSPS). From 2000 to 2001, he was a visiting researcher at USC/ISI (University of Southern California/Information Sciences Institute). From April 2001 to March 2003, he was an associate professor of Japan Advanced Institute of Science and Technology (JAIST). From 2005 to 2006, he is a visiting researcher at Division of Engineering and Applied Science, Harvard University and a visiting researcher at the Center for Coordination Science, MIT Sloan School of Management. From 2008 to 2010, he was a visiting researcher at the Center for Collective Intelligence, MIT Sloan School of Management. He was a board member of IFAAMAS, the PC-chair of AAMAS2013, PRIMA2009, General-Chair of PRIMA2014, IEEE ICA2016, is the Local Arrangements Chair of IJCAI2020, and was a SPC/PC member in many top-level conferences (IJCAI, AAMAS, ECAI, AAAI, etc). He received the JSAI(Japanese Society for Artificial Intelligence) Achievement Award, 2016, the JSPS Prize, 2014, the Fundamental Research Award of Japan Society for Software Science and Technology,2014, the Prize for Science and Technology (Research Category), The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science, and Technology, 2013, the Young Scientists’ Prize, The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science, and Technology, 2007, the Nagao Special Research Award of the Information Processing Society of Japan, 2007, the Best Paper Award of AAMAS2006, the 2005 Best Paper Award of Japan Society for Software Science and Technology, the Best Paper Award in the 66th annual conference of 66th Information Processing Society of Japan, and the Super Creator Award of 2004 IPA Exploratory Software Creation Projects. He was the JST PREST (Sakigake, Super Challenge Type) Research, and a principal investigator of the Japan Cabinet Funding Program for Next Generation World-Leading Researchers (NEXT Program). He is currently principal investigator of JST CREST project. Further, he has several companies, which are handling web-based systems and enterprise distributed systems. His main research interests include multi-agent systems, intelligent agents, collective intelligence, social computing, crowd science and engineering, group decision support systems, agent-mediated electronic commerce, and software engineering on offshoring.
Title: Innovating Intelligent Crowd Discussion and Consensus Support Systems
Abstract: Much attention has been focused on the collective intelligence of people worldwide. Interest continues to increase in online democratic discussions, which might become one of the next generation methods for open and public forums. To harness collective intelligence, incentives for participants are one critical factor. If we can incentivize participants to engage in stimulating and active discussions, the entire discussion will head in fruitful ways and avoid negative behaviors that encourage “flaming.” “Flaming” means a hostile and insulting interaction by Wikipedia. In our work, we developed an open web-based forum system called COLLAGREE that has facilitator support functions and deployed it for an internet-based town meeting in Nagoya as a city project for an actual town meeting of the Nagoya Next Generation Total City Planning for 2014-2018. Our experiment ran on the COLLAGREE system during a two- week period with nine expert facilitators from the Facilitators Association of Japan. The participants discussed four categories about their views of an ideal city. COLLAGREE registered 266 participants from whom it gathered 1,151 opinions, 3,072 visits, and 18,466 views. The total of 1,151 opinions greatly exceeded the 463 opinions obtained by previous real-world town meetings. We clarified the importance of a COLLAGREE-type internet based town meeting and a facilitator role, which is one mechanism that can manage inflammatory language and encourage positive discussions. While facilitators, who are one element of a hierarchical management, can be seen as a top-down approach to produce collective discussions, incentive can be seen as a bottom-up approach. In this talk, we also focus on incentives for participants and employ both incentives and facilitators to harness collective intelligence. I propose an incentive mechanism for large-scale collective discussions, where the discussion activities of each participant are rewarded based on their effectiveness. With these incentives, we encourage both the active and passive actions of participants. In this talk, I will present current results about this project.
AI 2016 