AAAI-17 / IAAI-17 Invited Speakers
AAAI-17 / IAAI-17 will feature the following series of distinguished speakers and panelists:
Sunday, February 5
- 9:05 – 10:05 AM
EAAI-17 Invited Talk: Ayanna Howard (Georgia Institute of Technology, USA)
- 2:30 – 3:30 PM
EAAI-17 Invited Panel: NSF Research Experience for Undergraduates (REU) Sites
- 8:00 – 9:00 PM
AAAI-17 Invited Talk: Lynne Parker (University of Tennessee and National Science Foundation, USA)
Monday, February 6
- 9:00 – 9:50 AM
AAAI-17 Invited Talk: Rosalind Picard (MIT and Empatica, USA)
- 10:00 AM – 11:00 AM
IAAI-17 Engelmore Award Lecture: David W. Aha
- 10:00 AM – 11:00 AM
EAAI-17 Invited Panel: AI Ethics Education
- 2:00 PM – 2:50 PM
AAAI/EAAI Outstanding Educator Award Lecture: Sebastian Thrun (Udacity, KittyHawk, Stanford, Georgia Tech)
- 2:50 PM – 3:30 PM
EAAI-17 Invited Panel: AI for Education
- 4:00 PM – 5:00 PM
AAAI-17 Invited Panel: AI for Social Good
- 5:10 PM – 6:10 PM
AAAI-17 Invited Panel: AI History: Expert Systems
Tuesday, February 7
- 8:50 – 9:50 AM
AAAI-17 Invited Talk: Steve Young (Cambridge University, UK)
- 10:00 – 11:00 AM
IAAI-17 Invited Talk: Jeremy Frank (NASA Ames Research Center, USA)
- 2:00 – 3:30 AM
AAAI-17 Invited Panel: Federal Funding Agency Information Panel — Meet and Greet
- 5:10 – 6:10 PM
AAAI-17 Invited Talk: Peter Dayan (University College London, UK)
Wednesday, February 8
- 8:50 – 9:50 AM
AAAI-17/IAAI-17 Joint Invited Talk: Dmitri Dolgov (Google, USA)
- 4:00 – 5:00 PM
AAAI-17 Invited Talk: Kristen Grauman (University of Texas at Austin, USA)
Thursday, February 9
- 8:00 – 8:45 AM
AAAI-17 Invited Panel: Advances in AI in Poker: Two Mini-Talks on Recent Breakthroughs and a Panel
- 8:50 – 9:50 AM
AAAI-17 Invited Talk: Russ Tedrake (MIT and Toyota Research Institute, USA)
IAAI-17 Engelmore Award Lecture
Goal Reasoning: Emerging Applications, a Foundation, and Prospects
David W. Aha (Naval Research Laboratory, USA)
Goal reasoning (GR) has a bright future as a foundation for research on intelligent agents and as a methodology for developing applications. GR is the study of agents that can deliberate on and self-select their goals/objectives, which is a desirable capability for deliberative autonomy. While it has been studied for several decades in multiple disciplines, GR has only recently been the focus of intense interest. In this presentation, I will introduce GR, relate it to other AI topics, summarize some emerging applications, describe our foundation for GR, and discuss some current and future research directions.
David W. Aha (UCI 1990) is a member of NRL's Navy Center for Applied Research on AI. His group conducts basic and applied research on intelligent agents, ML, case-based reasoning, and related topics; their current projects concern goal reasoning or deep learning. He has mentored 12 postdocs, served on 20 PhD committees, was a AAAI Councilor, cocreated the AAAI AI Video Competition, and created the UCI Repository for ML Databases. David has coorganized 30+ events (including AAA-17 DC, ICCBR-17, IJCAI-17 Workshop on XAI), serves on several PCs, and led or leads the evaluation team for four DARPA or ONR Programs.
AAAI-17 Invited Talk
The Consilience of Neural and Artificial Reinforcement Learning
Peter Dayan (Gatsby Unit, University College London, UK)
Animals that fail to predict or control events associated with rewards and punishments are not long for this world. Reinforcement learning thus offers a body of theory that organizes and motivates a huge wealth of work in psychology and neuroscience. Equally, these latter disciplines provide inspiration for new methods, ideas and problems in the wider field of reinforcement learning. I will discuss this consilience, illustrating the fecundity of the approaches and some of the challenges and opportunities ahead.
Peter Dayan studied mathematics at Cambridge University, did his PhD in computational neuroscience at the University of Edinburgh with David Willshaw, and postdocs with Terry Sejnowski at the Salk Institute and Geoff Hinton at the University of Toronto. After three years as an assistant professor at MIT, he helped found the Gatsby Computational Neuroscience Unit at UCL in 1998, and became director in 2002. His interests center on mathematical and computational models of neural processing, with a particular emphasis on representation, learning and decision making.
AAAI-17/IAAI-17 Joint Invited Talk
Self-Driving Cars and the Future of Mobility
Dmitri Dolgov (Waymo)
In the US, more than 35,000 people die in car accidents every year; 100 million hours are wasted every day on people's commutes. We can do better. Self-driving cars offer a promise of higher safety, efficiency, and convenience in transportation. At Waymo (formerly Google's self-driving car project), we're working to bring self-driving technology to millions of people. In 2015, we took a major step towards that goal: we performed the world's first fully driverless ride on public roads in an uncontrolled setting. This talk will cover the technology behind fully self-driving cars and how they're able to safely share the roads with pedestrians, cyclists and other road users.
Dmitri Dolgov is a distinguished engineer and the head of self-driving technology at Waymo. Prior to that, he worked on self-driving cars in Toyota and at Stanford as part of Stanford's DARPA Urban Challenge team. Dmitri received his B.S. and M.S. in physics and math from the Moscow Institute of Physics and Technology in 1998 and 2000, respectively, and his Ph.D. in computer science from the University of Michigan in 2006.
IAAI-17 Invited Talk
Enabling Autonomous Space Mission Operations with Artificial Intelligence
Jeremy Frank (Intelligent Systems Division, NASA Ames Research Center)
For over 50 years, NASA's crewed missions have been confined to the Earth-Moon system, where speed-of-light communications delays between crew and ground are practically nonexistent. This ground-centered mode of operations, with a large, ground-based support team, is not sustainable for NASA's future human exploration missions to Mars. Future astronauts will need smarter tools to make decisions on their own, without assistance from ground-based mission control. In this talk, we will describe several demonstrations of astronaut decision support tools using AI techniques including automated planning, fault diagnosis, automated reasoning and machine learning. These demonstrations show how developments in AI will enable humanity's journey to Mars.
Dr. Jeremy Frank is the group lead of the Planning and Scheduling Group, in the Intelligent Systems Division, at NASA Ames Research Center. He received his Ph.D. from the Department of Computer Science, at the University of California at Davis, in June 1997. Frank's work involves the development of space mission operations planning tools; the integration of technologies for planning, plan execution, and fault detection for space applications; and the development of technology to enable astronauts to autonomously operate spacecraft. Frank has published over 50 conference papers, nine journal papers, and three book chapters, and received over 40 NASA awards, including two Exceptional Achievement Medals, the Silver Snoopy, and the NASA Engineering and Safety Center Award.
AAAI-17 Invited Talk
Learning How to Move and Where to Look from Unlabeled Video
Kristen Grauman (University of Texas at Austin, USA)
Sponsored by Capital One
The status quo in visual recognition is to learn from batches of unrelated Web photos labeled by human annotators. Yet cognitive science tells us that perception develops in the context of acting and moving in the world — and without intensive supervision. How can unlabeled video augment computational visual learning? I'll describe our recent work exploring how a system can learn effective representations by watching unlabeled video. Fist we consider how the ego-motion signals accompanying a video provide a valuable cue during learning, allowing the system to internalize the link between "how I move" and "what I see." Next, I explore how the temporal coherence of video permits new forms of invariant feature learning, whether by capturing how object-centric regions evolve over time or by representing higher order consistency in visual changes. Incorporating these ideas into various recognition tasks, we demonstrate the power in learning from ongoing, unlabeled visual observations — even overtaking traditional heavily supervised approaches in some cases. Finally, I examine how simply having seen unlabeled human-taken videos, a system can learn to mimic human videographer tendencies, automatically creating normal field of view video out of unedited 360 degree panoramas.
Kristen Grauman is an associate professor in the Department of Computer Science at the University of Texas at Austin. Her research in computer vision and machine learning focuses on visual search and object recognition. Before joining UT Austin in 2007, she received her Ph.D. from MIT. She is a Sloan Research Fellow and recipient of NSF CAREER, ONR Young Investigator, PAMI Young Researcher, and PECASE awards, and the 2013 IJCAI Computers and Thought award. She and her collaborators were recognized with the CVPR Best Student Paper Award in 2008 for their work on hashing algorithms for large-scale image retrieval, the Marr Best Paper Prize at ICCV in 2011 for their work on modeling relative visual attributes, and the Best Application Paper Award at ACCV in 2016 for their work on automatic cinematography in 360 degree video. She serves as an associate editor in chief for Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and served as a program chair of CVPR 2015 in Boston.
AAAI-17 Invited Talk
The Creation of the US National Artificial Intelligence Research and Development Strategic Plan
Lynne Parker (University of Tennessee and National Science Foundation, USA)
Released by the White House in October 2016, the National AI R&D Strategic Plan outlines a set of AI research priorities for the U.S. Federal Government. In this talk, I will discuss the development of the report from my perspective as cochair of the interagency Task Force that generated the report. I will discuss the rationale behind the content of the Plan, as well as insights on how multiagency collaboration led to the creation of the report. I will also discuss possible future benefits that might result from the Plan. Ample time for Q&A will be provided.
Lynne Parker is a professor in the Department of Electrical Engineering and Computer Science at The University of Tennessee, Knoxville. Her research spans mobile robot cooperation, human-robot cooperation, sensor networks, robotic learning, intelligent agent architectures, and robot navigation. Parker is a Fellow of IEEE. She was the division director for the Information and Intelligent Systems (IIS) Division in the Computer and Information Science and Engineering (CISE) at NSF. During her two years of tenure at the NSF, she served as the cochair of the NITRD task force for machine learning and artificial intelligence, which developed the National Artificial Intelligence Research and Development Strategic Plan that will be described in this talk.
AAAI-17 Invited Talk
Adventures in Building Emotional Intelligence Technologies
Rosalind Picard (MIT and Empatica, USA)
Years ago, I set out to create technology with emotional intelligence, demonstrating the ability to sense, recognize, and respond intelligently to human emotion. At MIT we designed studies, gathered data, and developed signal processing and machine learning techniques to see what insights could be reliably obtained. In this talk I will highlight the most surprising findings during this adventure. These include new insights about the "true smile of happiness," discovering new ways cameras (and your smartphone, even in your handbag) can compute your biosignals, finding electrical signals on the wrist that reveal insight into deep brain activity, and learning surprising implications of wearable sensing for autism, anxiety, sleep, memory, epilepsy, and more. What is the biggest challenge for AI to solve next?
Rosalind Picard, ScD, FIEEE is founder and director of the Affective Computing Research Group at the MIT Media Laboratory, cofounder of Affectiva, providing emotional intelligence technology used by 1/3 of the Global Fortune 100, and cofounder and chief scientist of Empatica, improving lives with clinical-quality wearable sensors and analytics. Picard is the author of over 250 articles in computer vision, machine learning, signal processing, affective computing, and human-computer interaction. She is known internationally for her book, Affective Computing, which helped launch the field by that name. Picard holds bachelors in electrical engineering (EE) from Georgia Tech and masters and a doctorate degrees in electrical engineering and computer science from MIT. Picard's inventions have been twice named to "top ten" lists, including the New York Times Magazine's Best Ideas of 2006 for the Social Cue Reader, and 2011's Popular Science Top Ten Inventions for a Mirror that Monitors Vital Signs.
AAAI-17 Invited Talk
Convex and Combinatorial Optimization for Dynamic Robots in the Real World
Russ Tedrake (MIT Computer Science and Artificial Intelligence Laboratory and Toyota Research Institute, USA)
Humanoid robots walking across intermittent terrain, robotic arms grasping multifaceted objects, or UAVs darting left or right around a tree ... many of the dynamics and control problems we face today have both rich nonlinear dynamics and an inherently combinatorial structure. In this talk, Tedrake will review some recent work on planning and control methods which address these two challenges simultaneously. He will present our explorations with mixed-integer convex-, semidefinite-programming-relaxations, and satisfiability-modulo-theory(SMT)-based methods applied to hard problems in legged locomotion over rough terrain, grasp optimization, and UAVs flying through highly cluttered environments.
Russ Tedrake is a professor of electrical engineering and computer science, aeronautics and astronautics, and mechanical engineering at MIT, the Director of the Center for Robotics at the Computer Science and Artificial Intelligence Lab, and was the leader of Team MIT's entry in the DARPA Robotics Challenge. TEDRAKE is also the director of simulation and control at the new Toyota Research Institute. He is a recipient of the NSF CAREER Award, the MIT Jerome Saltzer Award for undergraduate teaching, the DARPA Young Faculty Award in Mathematics, the 2012 Ruth and Joel Spira Teaching Award, and was named a Microsoft Research New Faculty Fellow.
AAAI/EAAI Outstanding Educator Award Lecture
Democratizing Education — Why Not?
Sebastian Thrun (Udacity, KittyHawk, Stanford, Georgia Tech)
When Thrun and Norvig made Stanford's graduate-level AI course public (as the first global MOOC), 160,000 students from 195 countries signed up. This started a global wave of Massive Open Online Courses, through which tens of millions of students were able to receive world-class education. Thrun will report on progress at Udacity, the start-up company that grew out of this experiment. Udacity has become a dominant educator in areas as advanced as self-driving cars or virtual reality. Its hundreds of industrial partners willingly hire Udacity graduates, sometimes even without job interview. Thrun will lay out his vision for global education, arguing that the democratization of higher education might one day double the world's GDP.
Prof. Dr. Dr.h.c. Dr.h.c Sebastian Thrun pursues research on robotics, artificial intelligence, education, and human computer interaction. He founded Google's self driving car team, after winning the DARPA Grand Challenge. Together with Peter Norvig, he also developed the very first global MOOC with 160,000 students enrolled. His company, Udacity, has educated over 5 million students, and has been valued at more than one billion dollars. Google Scholar ranks Thrun's publication h-index #14 worldwide in all of computer science. Thrun also founded Google X, where he founded Google Glass among many other projects. He was elected into the National Academy of Engineering and the German Academy of Sciences at age 39. Fast Company named Thrun the fifth most creative person in business, and Foreign Policy touted him Global Thinker #4. He won numerous awards, including the prestigious Max Planck Research Award.
AAAI-17 Invited Talk
Statistical Spoken Dialogue Systems and the Challenges for Machine Learning
Steve Young (Cambridge University Engineering Department, UK)
This talk will review the principal components of a spoken dialogue system and then discuss the opportunities for applying machine learning for building robust high performance open-domain systems. The talk will be illustrated by recent work at Cambridge University using machine learning for belief tracking, reward estimation, multi-domain policy learning and natural language generation. The talk will conclude by discussing some of the key challenges in scaling these solutions to work in practical systems.
Steve Young is a professor of information engineering at Cambridge University and a member of the Apple Siri Development team based in Cambridge, UK. His main research interests lie in the area of statistical spoken language systems including speech recognition, speech synthesis and dialogue management. He is the recipient of a number of awards including an IEEE Signal Processing Society Technical Achievement Award, an ISCA Medal for Scientific Achievement and an IEEE James L Flanagan Speech and Audio Processing Award. He is a Fellow of the Royal Academy of Engineering and the Institute of Electrical and Electronics Engineers (IEEE).
AAAI-17 Invited Panel
AI for Social Good
Moderator: Milind Tambe (University of Southern California)
Panelists: Eric Horvitz (Microsoft Research), Peter Mockel (Worldbank IFC), Lynne Parker (National Science Foundation and University of Tennessee, Knoxville), and Gideon Mann (Bloomberg)
Conversations about future negative consequences of AI sometimes drown out discussions of its potential in helping solve complex societal problems. This panel will focus on the positive changes that AI will have on social good and its potential to benefit low resource communities, emerging markets, and solving wicked social problems. This panel will also discuss how to encourage and facilitate further research in this direction.
AI History: Expert Systems
Moderator: David C. Brock (Historian, Computer History Museum, Mountain View, California)
- Edward Feigenbaum (Kumagai Professor Emeritus, Stanford University), AAAI President, 1980-81
- Bruce Buchanan (University Professor Emeritus, University of Pittsburgh), AAAI President, 1999-2001
- Randall Davis (Professor EECS and CSAIl, MIT), AAAI President 1995-97
- Eric Horvitz (Tech. Fellow and Director, Microsoft Research), AAAI President, 2007-09
A New York Times Magazine article recently announced "The Great A.I. Awakening": The rise of Recognition Systems based on machine learning methods and neural networks, and their use by commercial firms. This view predominates inside and outside of the AI community. Yet this view neglects AI's previous awakenings.
In 1985, Allen Newell, AAAI's first President, wrote: "There is no doubt, as far as I am concerned, that the development of expert systems is the major advance in the field during the last decade.... The emergence of expert systems has transformed the enterprise of AI." Further, Computerworld reported that, "Recent advances in expert systems are putting society at the brink of a massive application of artificial intelligence."
From, roughly, 1970 to 1993, Expert Systems captivated the AI community and public as do today's Recognition Systems. Expert Systems technology was absorbed by the software industry, omnipresent but unnoted in contemporary software.
What is the history of Expert Systems, and how does it inform the development of machine learning and neural networks today? Join a panel of former AAAI presidents — all major contributors to the expert systems story — for insights and perspectives.
Advances in AI in Poker: Two Mini-Talks on Recent Breakthroughs and a Panel
Moderator: Kevin Leyton-Brown (University of British Columbia, Canada)
Panelists: Michael Bowling (University of Alberta, Canada) and Tuomas Sandholm (Carnegie Mellon University, USA)
Poker has been a challenge problem in AI and game theory for decades. As a game of imperfect information it involves obstacles not present in classic board games like chess and go, but which are present in many real-world applications, such as negotiation, security, and auctions. However, no programs had beaten professional players in large poker games. Until now! New breakthrough domain-independent advances have seen computer programs surpass human experts in the game of heads-up no-limit Texas hold'em, which has over 10160 decision points. Michael Bowling (who led the team that beat top poker pros in limit Texas hold'em in 2008) will talk about DeepStack, which late last year defeated a collection of professional poker players in heads-up no-limit. Tuomas Sandholm (who led the team that is the current two-time champion of the Annual Computer Poker Competition in heads-up no-limit) will talk about Libratus, which last month defeated a team of top heads-up no-limit specialist professionals. These two short talks will be followed by a panel discussion. Come learn about the latest scientific breakthroughs and milestones achieved in AI.
Federal Funding Agency Information Panel / Meet and Greet
Participants: James Donlon (National Science Foundation), Reid Simmons (National Science Foundation), David Gunning (Defense Advanced Research Projects Agency – DARPA), Jeremy Frank (National Aeronautics and Space Administration – NASA)
In the first hour, program officers from federal agencies, including NSF, DARPA, and NASA, will present information on current and anticipated funding opportunities relevant to the AI community. Afterwards, stay for informal discussion with these representatives.
EAAI-17 Invited Talk
Designing Assistive Robots and Technologies for Pediatric Care
Ayanna Howard (Georgia Institute of Technology, USA)
In recent months, there has been an upsurge in the attention given to robots and artificial intelligence and their inevitable destruction of the human race if we are not watchful. Whether your opinion sits on one side or the other, the fact remains; robots have already become a part of our society. In particular, with recent advances in robotics, therapeutic interventions using robots is now ideally positioned to make an impact. There are numerous challenges though that must still be addressed. In this talk, Howard will discuss the role of robotics and related technologies for pediatric therapy. She will present her approaches in which these technologies address real-life therapy goals for children with special needs.
Ayanna Howard, Ph.D. is the Linda Smith Endowed Chair Professor in the School of ECE at the Georgia Institute of Technology and chief technology officer of Zyrobotics. She received her B.S. in engineering from Brown University and her PhD in electrical engineering from the University of Southern California. Her area of research has a specific emphasis on developing assistive robotics and technologies for children with special needs. To date, her unique accomplishments have been highlighted through a number of awards and articles, including highlights in USA Today, Upscale, and TIME Magazine, as well as being recognized as one of the 23 most powerful women engineers in the world by Business Insider.
EAAI-17 Invited Panel
AI for Education
Moderator: Sheila Tejada (University of Southern California)
Panelists: Yolanda Gil (USC Information Sciences Institute), Ayanna Howard (Georgia Tech), Peter Norvig (Google), Mehran Salami (Stanford University), Sebastian Thrun (Udacity, KittyHawk, Stanford, Georgia Tech)
The panel will discuss how we can apply AI techniques in the domain of education to improve teaching, student evaluation, and learning. The panelists will talk about which aspects of the education problem they were able to address with AI and give insight into the education space, suggesting where other educators and researchers can apply AI tools to improve their own teaching or student learning.
EAAI-17 Invited Panel
Moderator: Sven Koenig (University of Southern California)
Panelists: Benjamin Kuipers (University of Michigan), Judy Goldsmith (University of Kentucky), Illah R. Nourbakhsh (Carnegie Mellon University CREATE Lab)
The panel will discuss how, as educators, we can incorporate ethical issues into undergraduate or graduate AI classes, such as the following questions: Do we need to worry about the reliability, robustness, and safety of AI systems? Do we need to provide oversight of their operation? How do we guarantee that their behavior is consistent with social norms and human values? Who is liable for incorrect AI decisions? How will AI technology impact standard of living, distribution and quality of work, and other social and economic aspects?
Benjamin Kuipers is a professor of computer science and engineering at the University of Michigan. He received his B.A. from Swarthmore College, and his PhD from MIT. He investigates the representation of commonsense and expert knowledge, with particular emphasis on the effective use of incomplete knowledge. His research accomplishments include developing the TOUR model of spatial knowledge in the cognitive map, the QSIM algorithm for qualitative simulation, the Algernon system for knowledge representation, and the Spatial Semantic Hierarchy models of knowledge for robot exploration and mapping. He has served as department chair at UT Austin, and is a Fellow of AAAI, IEEE, and AAAS.
Judy Goldsmith received her degrees in mathematics from Princeton University and the University of Wisconsin-Madison. She held post-doc at Dartmouth College and Boston University, an assistant professorship at the University of Manitoba, and has been in the Computer Science Department of the University of Kentucky since 1993. She is a full professor. Her research areas include many aspects of decision making, including decision making under uncertainty; computational social choice; preference elicitation, representation, and aggregation; computational learning theory, and computational complexity. Goldsmith was the recipient of the The First Annual IJCAI-JAIR Best Paper Prize, Honorable Mention, 2003. She was recognized in 2014 as a Senior Member of AAAI, the Association for the Advancement of Artificial Intelligence. In 2015, Goldsmith received an Undergraduate Research Mentor award from the Computing Research Association. In 1998, Goldsmith was recognized by the AAAS for her mentoring of members of underrepresented groups in the STEM disciplines. She has been on the editorial board of JAIR since 2008, and on the editorial board of Artificial Intelligence since 2015.
Illah R. Nourbakhsh is a professor of robotics, director of the Community Robotics, Education and Technology Empowerment (CREATE) lab, and associate director for robotics faculty at Carnegie Mellon University. In 2009, the National Academy of Sciences named him a Kavli Fellow. In 2013 he was inducted into the June Harless West Virginia Hall of Fame. He is coauthor of Introduction to Autonomous Mobile Robots, author of Robot Futures and Parenting for Technology Futures. He is a trustee of the Claude Worthington Benedum Foundation, and chairman of the board of directors of the Southwestern Pennsylvania Environmental Health Project. He is a member of the Global Future Council on the future of ai and robotics for the World Economic Forum and senior advisor to The Future Society, Harvard Kennedy School.
EAAI-17 Invited Panel
NSF Research Experience for Undergraduates (REU) Sites
Moderator: Eric Eaton (University of Pennsylvania)
Panelists: Georgios Anagnostopoulos (Florida Institute of Technology), Stephanie E. August (National Science Foundation), Zach Dodds (Harvey Mudd College), Bill Smart (Oregon State University)
This panel will discuss the selection process for NSF REU sites, provide advice about how to write successful site proposals, best practices for running sites, and how both sites and individual PIs can best mentor undergraduate summer research students.
AAAI-17 Call for Papers
Special Track on Cognitive Systems
Special Track on Computational Sustainability
Special Track on Integrated Systems
EAAI Symposium Call
IAAI-17 Call for Papers
Tutorial Forum Call
Student Abstract Call
DC Call for Applications
Senior Member Track Call