Program
Tentative Schedule
meeting in Union Square 13 on the 4th floor of the Hotel
8:45-9:00 Introductions
9:00-9:45 Invited talk: Ece Kamar, Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence
9:45-10:30 Invited talk: Luis Ortiz, Modern Applications of Game Theory in AI to Social and Economic Systems
10:30-11:00 coffee break in Union Square 19/20
11:00-11:30 Invited talk: Judy Goldsmith, What's Hot in AI Ethics
11:30-12:45 Career Panel: Puja Das (Apple), Ece Kamar, (Microsoft Research), Judy Goldsmith (University of Kentucky), Sheila McIlraith (University of Toronto)
12:45-2:00 lunch
2:00-2:45 Invited talk: Sheila McIlraith, AI and the Future of Automated Programming
2:45-3:30 Invited talk: Jennifer Neville, Learning in networks: How to exploit relationships to improve predictions
3:30-4:00 coffee break in Union Square 19/20
4:00-5:15 Invited talk: Marie DesJardins, Finding Balance and Joy in an Academic/Research Career in AI
5:15-5:45 Wrapping up
Marie desJardins, University of Maryland, Baltimore County
Finding Balance and Joy in an Academic/Research Career in AI
Academic careers and research positions in artificial intelligence provide wonderful opportunities to work on interesting and challenging problems, collaborate with creative and talented people, mentor and teach students and junior colleagues, and provide service to the profession and to society through a variety of activities. However, the richness of these careers also presents challenges in resolving the many competing priorities and in finding a healthy balance of activities. In this talk, I will share some insights about important skills for a successful academic or research career, including time management, handling failure, learning when to say "no" (and how to do so effectively), and long-term career planning. The talk is particularly targeted at female and minority graduate students and junior faculty, but should be useful for anyone looking to be more productive and more fulfilled in their developing careers.
Bio: Dr. Marie desJardins is the Associate Dean for Academic Affairs in the College of Engineering and Information Technology (COEIT) at the University of Maryland, Baltimore County (UMBC). She is also a Professor in UMBC’s Department of Computer Science and Electrical Engineering, where she has been a member of the faculty since 2001. She is an American Council of Education Fellow, a UMBC Presidential Teaching Professor, an inaugural Hrabowski Academic Innovation Fellow, an ACM Distinguished Member, and a AAAI Senior Member.
Dr. desJardins received her Ph.D. in 1992 from the University of California, Berkeley. Her research is in artificial intelligence, focusing on machine learning, multi-agent systems, planning, and interactive AI techniques. Dr. desJardins was the Program Cochair for AAAI-13, AAAI Liaison to CRA’s Board of Directors, Vice-Chair of ACM's SIGART, and a AAAI Councilor.
Judy Goldsmith, University of Kentucky
What's Hot in AI Ethics
This talk will introduce you to some of the ethics topics you are likely to hear discussed over the course of the conference, such as "robots that kill," and "trolley problems." The goal is to prepare you to join in theconversations, not to tell you what is right.
Ece Kamar, Microsoft Research
Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence
Historically, a common goal for the development of AI systems has been exhibiting intelligent behaviors that humans excel at. Consequently, most AI systems are designed to replace humans by completely automating well-defined tasks. Despite advances in AI, machines still have limitations in accomplishing tasks that come naturally to humans. In this talk, I will argue that our focus should not be in designing isolated AI systems, but instead we should focus on developing hybrid systems that combine the strengths of machine and human intelligence. I will present human computation platforms as an enabling technology for the development of hybrid systems and address some scientific challenges that arise from having humans in the loop.
Bio: Ece Kamar is a researcher at the Adaptive Systems and Interaction group at Microsoft Research Redmond. Ece earned her Ph.D. in computer science from Harvard University. While at Harvard, she received the Microsoft Research fellowship and Robert L. Wallace Prize Fellowship for her work on Artificial Intelligence. She has served as area chair and program committee member for various conferences on Artificial Intelligence and was a member of the first AI 100 panel, studying how Artificial Intelligence will affect the way we live. She works on several subfields of AI; including planning, machine learning, multi-agent systems and human-computer teamwork. She is passionate about combining machine and human intelligence towards developing real-world applications. More information about her work can be found at https://research.microsoft.com/en-us/um/people/eckamar/.
Sheila McIlraith, University of Toronto
AI and the Future of Automated Programming
Wouldn't it be great if computers could program themselves, with a few simple directives from us? Or would it? The vision of automated program synthesis has been around for more than 50 years. In this talk, I'll briefly overview the history of automated programming, and discuss recent exciting advances that draw on techniques from AI, including machine learning, automated planning, and knowledge representation. We'll also discuss (together, I hope) some of the design challenges associated with program synthesis, particularly as we contemplate their use to control devices and machines that operate "in the wild".
Bio: Sheila McIlraith is a Professor in the Department of Computer Science, University of Toronto. Prior to joining U of T, she spent six years as a Research Scientist at Stanford University, and one year at Xerox PARC. She also spent a number of years building AI applications in industry. Sheila's research is in the area of knowledge representation and automated reasoning, with a focus these days on sequential decision making, automated planning, program composition and synthesis and preferences (among other things). She is a AAAI Fellow, past co-chair of KR 2012 and ISWC 2004i, and is currently serving as past-president of KR Inc., the international scientific foundation concerned with fostering research and communication on knowledge representation and reasoning.
Jennifer Neville, Purdue University
Learning in networks: How to exploit relationships to improve predictions
The popularity of social networks and social media has increased the amount of information available about users' behavior online--including current activities, and interactions among friends and family. This rich relational information can be used to improve predictions even when individual data is sparse, since the characteristics of friends are often correlated. Although this type of network data offer several opportunities to improve predictions about users, the characteristics of online social network data also present a number of challenges to accurately incorporate the network information into machine learning systems. This talk will outline some of the algorithmic and statistical challenges that arise due to partially-observed, large-scale networks, and describe semi-supervised learning methods that address some of the major challenges.
Luis Ortiz, University of Michigan Dearborn
Modern Applications of Game Theory in AI to Social and Economic Systems
The use of game theory to solve "classical" AI problems goes back to the "birth" of AI itself. In particular, creating intelligent computer systems to play parlor board and card games is still a hallmark of the field, inspiring research work and advances to this day. More recently, there has been steadily growing interest in the expanded use of game theory in conjunction with AI-inspired frameworks and models. Some applications tackle technological problems in the design and development of engineered autonomous multi-agent systems. Others address broader classical and emerging problems in society, as traditionally studied in the economic and social sciences. This presentation will provide a brief, general introduction to the latter, more modern use of game theory in AI, including exciting opportunities for future research. No prior knowledge of game theory, or the specific research area within AI, is necessary. Workshop participants should expect a flexible presentation encouraging open,informal discussions, and hopefully generating conversations and increasing awareness about this research area within AI.