AAAI-20 / IAAI-20 / EAAI-20 Invited Speaker Program
Join us for a special event featuring the 2018 Turing Award Winners!
This special two-hour event will feature individual talks by each speaker, followed by a panel session.
ACM named Yoshua Bengio, Geoffrey Hinton, and Yann LeCun recipients of the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. Bengio is Professor at the University of Montreal and Scientific Director at Mila, Quebec’s Artificial Intelligence Institute; Hinton is VP and Engineering Fellow of Google, Chief Scientific Adviser of The Vector Institute, and University Professor Emeritus at the University of Toronto; and LeCun is Professor at New York University and VP and Chief AI Scientist at Facebook.
AAAI Presidential Address
Yolanda Gil (USC Information Sciences Institute)
Sunday, February 9, 8:30 – 9:20 AM
Monday, February 11, 6:15 – 7:15 PM
AI History Panel: Advancing AI by Playing Games
Moderator: Amy Greenwald (Brown University)
Panelists: Murray Campbell (IBM), Michael Bowling (University of Alberta), and Hiroaki Kitano (Sony)
Tuesday, February 12, 4:45 – 6:15 PM
AAAI-20 Invited Speakers
Susan Athey (Stanford University, USA)
Tuesday, February 11, 3:50 – 4:40 PM
Aude Billard (EPFL – Ecole Polytechnique Federale de Lausanne, Switzerland)
Monday, February 10, 8:30 – 9:20 AM
Stuart Russell (University of California, Berkeley, USA)
Wednesday, February 12, 8:50 – 9:50 AM
Robert S. Engelmore Memorial Award Lecture
Henry Kautz (University of Rochester)
Monday, February 10, 5:20 – 6:10 PM
IAAI/AAAI Joint Invited Talk
Tuesday, February 11, 8:30 – 9:20 AM
IAAI-20 Invited Speaker
David Cox (MIT-Watson AI Lab)
Tuesday, February 11, 4:00 – 5:00 PM
EAAI Outstanding Educator Award Lecture
Marie desJardins (Simmons University, USA)
Ben Shapiro (University of Colorado Boulder, USA)
AAAI 2020 Invited Talk
Talk Title: The Economic Value of Data for Targeted Pricing
Abstract: This presentation reviews recent research about consumer choices in shopping, for example in supermarkets. Historically a large literature in economics and marketing studied consumer choices among brands, considering one product category at a time. A series of recent papers makes use of advances in computation and techniques from matrix factorization to study consumer responses to price changes using observational data from consumer transactions. One question that arises is the value of data (for example, the increase in profit from using additional data), comparing the value of different types of data, e.g. more consumers or longer retention for each consumer.
Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business. She received her BA from Duke University and her PhD from Stanford. She previously taught at MIT and Harvard. Her research focuses on the economics of digitization, marketplace design, and machine learning. She previously served as consulting chief economist for Microsoft, and now serves on the boards of Expedia, Lending Club, Rover, Turo, Ripple, and Innovations for Poverty Action. She is the director of the Golub Capital Social Impact Lab at Stanford GSB, and associate director of the Stanford Institute for Human-Centered Artificial Intelligence.
Mila (Quebec AI Institute)
Abstract: Artificial Intelligence (AI) research has been transformed in fundamental ways by the development and success of deep learning. Whereas symbolic approaches to AI focused on human-provided formal knowledge presented as logical rules and facts, much of what humans know is not accessible to them consciously and is thus difficult to communicate with computers. Machine learning bypasses this problem by allowing the computer to acquire that knowledge from data, observations and interactions with an environment. Neural networks and deep learning are machine learning methods inspired by the brain in which information is not represented by symbolic statements but instead where concepts have distributed representations, patterns of activations of features which can overlap across concepts, making it possible to quickly generalize to new concepts. When we make it possible to compose modules which process such distributed representations (either recursively or through layers of processing) it is possible to represent very rich functions compactly and obtain even better generalization. More recently, deep learning has gone beyond its traditional realm of pattern recognition over vectors or images and expanded into many self-supervised methods and generative models able to capture complex multi-modal distributions, into models with attention which can process graphs and sets, leading to breakthroughs in speech recognition and synthesis, computer vision and machine translation, for example. The talk closes with a discussion of current limitations and forward-looking research directions towards human-level AI.
Yoshua Bengio is recognized as one of the world’s artificial intelligence leaders and a pioneer of deep learning. Professor since 1993 at the Université de Montréal, he received the A.M. Turing Award 2018, considered like the Nobel prize for computing, with Geoff Hinton and Yann LeCun. Holder of the Canada Research Chair in Statistical Learning Algorithms, he is also the founder and scientific director of Mila, the Quebec Institute of AI–the world’s biggest university-based research group in deep learning. In 2018, he collected the largest number of new citations in the world for a computer scientist and earned the prestigious Killam Prize from the Canada Council for the Arts. Concerned about the social impact of AI, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.
EPFL – Ecole Polytechnique Federale de Lausanne, Switzerland
Abstract: Robots have got out of the secure and predictable environment of factories and start to face the complexity and unpredictability of our daily environments. To avoid that robots fail lamely at the task they are programmed to do, robots now need to adapt on the go. I will present techniques from machine learning to allow robots to learn strategies to enable them to react rapidly and efficiently to changes in the environment, and applications of these techniques for rapid and robust manipulation of objects.
Aude Billard is full professor and head of the LASA laboratory at the School of Engineering at the Swiss Institute of Technology Lausanne (EPFL). She was a faculty member at the University of Southern California, prior to joining EPFL in 2003. She holds a B.Sc and M.Sc. in Physics from EPFL (1995) and a Ph.D. in Artificial Intelligence (1998) from the University of Edinburgh. Her research spans the fields of machine learning and robotics with a particular emphasis on learning from sparse data and performing fast and robust retrieval. Her work finds application to robotics, human-robot / human-computer interaction and computational neuroscience. This research received best paper awards from several venues, among which IEEE Transactions on Robotics, RSS, ICRA and IROS.
Simmons University, USA
EAAI Outstanding Educator Award Lecture
Talk Title: #AIForAll: A 64-Year Perspective on AI, Computing, Inclusion, and Diversity
Abstract: As the AI community prepares to celebrate the 2^8 anniversary of the Dartmouth Summer Research Project on Artificial Intelligence that launched AI as a field, it is an appropriate time to look back over the last 64 years to consider how far we have progressed. This presentation will focus particularly on trends in education, diversity, and inclusion in AI and in computing more generally. The talk will also include recommendations for the field, including an increased emphasis on ethical computing, best practices for inclusive classroom and work environments, and how to be an effective ally for underrepresented groups.
Marie desJardins is the Dean of the College of Organizational, Computational, and Information Sciences at Simmons University in Boston. She was previously a professor at the University of Maryland, Baltimore County, where she was a UMBC Presidential Teaching Professor, Academic Innovation Fellow, Honors Faculty Fellow, and Associate Dean of UMBC’s College of Engineering and Information Technology. She is a AAAI Fellow, an ACM Distinguished Member, and the recipient of the A. Richard Newton Educator ABIE Award, the UC Berkeley Distinguished Alumni Award in Computer Science, and mentoring awards from CRA-E and NCWIT. Dr. desJardins is known for her research in artificial intelligence, her work in expanding access to K-12 computer science education, and her leadership as a mentor, teacher, and champion for diversity in computing. While at UMBC, she advised 12 Ph.D. students, 26 M.S. students, and over 100 undergraduate researchers.
Google and The Vector Institute
Abstract: An object can be seen as a geometrically organized set of interrelated parts. A system that makes explicit use of these geometric relationships to recognize objects should be naturally robust to changes in viewpoint, because the intrinsic geometric relationships are viewpoint-invariant. We describe an unsupervised version of capsule networks, in which a neural encoder, which looks at all of the parts, is used to infer the presence and poses of object capsules. The encoder is trained by back-propagating through a decoder, which predicts the pose of each already discovered part using a mixture of pose predictions. The parts are discovered directly from an image, in a similar manner, by using a neural encoder, which infers parts and their affine transformations. We learn object- and part-capsules on unlabeled data, and then cluster the vectors of presences of object capsules. When told the names of these clusters, we achieve state-of-the-art results for unsupervised classification on MNIST.
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie-Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto where he is now an Emeritus Distinguished Professor. He is also a Vice President & Engineering Fellow at Google and Chief Scientific Adviser of the Vector Institute. Geoffrey Hinton was one of the researchers who introduced the backpropagation algorithm and the first to use back propagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.
University of Rochester
Talk Title: The Third AI Summer
Abstract: The first AI summer was based on optimism about the power of general power solving, and the second on the power of knowledge engineering. Advances in machine learning have brought us into the third AI summer. This time, however, the stakes are incalculably higher than in the past. The danger is not just, as before, that marketplace hype and an overly narrow scientific focus will lead to disillusionment and retrenchment; but rather that AI now works well enough that it can be used – and is already being used – to eliminate human freedom and dignity. A dystopian future is not inevitable; progress in AI might instead usher in an era of unprecedented prosperity, knowledge, and freedom. This talk will explore the scientific, social, and geopolitical forces at play in the third AI summer.
Henry Kautz is currently serving as director for the division for Information & Intelligent Systems at the National Science Foundation. He a professor of computer science and founding director of the Goergen Institute for Data Science at the University of Rochester. He has been a researcher at AT&T Bell Labs in Murray Hill, NJ, and a full professor at the University of Washington, Seattle. In 2010, he was elected President of AAAI, and in 2016 was elected Chair of the AAAS Section on Information, Computing, and Communication. His interdisciplinary research includes practical algorithms for solving worst-case intractable problems in logical and probabilistic reasoning; models for inferring human behavior from sensor data; pervasive healthcare applications of AI; and social media analytics. In 1989 he received the IJCAI Computers & Thought Award, which recognizes outstanding young scientists in artificial intelligence, and 30 years later received 2018 ACM-AAAI Allen Newell Award for career contributions that have breadth within computer science and that bridge computer science and other disciplines.
Facebook AI Research & New York University
Yann LeCun is VP and Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute and the Center for Data Science. He was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received an EE Diploma from ESIEE (Paris) in 1983, a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU in 2003 after a short tenure at the NEC Research Institute. In late 2013, LeCun became Director of AI Research at Facebook, while remaining on the NYU Faculty part-time. He was visiting professor at Collège de France in 2016. His research interests include machine learning and artificial intelligence, with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is best known for his work in deep learning and the invention of the convolutional network method which is widely used for image, video and speech recognition. He is a member of the US National Academy of Engineering, a Chevalier de la Légion d’Honneur, a fellow of AAAI, the recipient of the 2014 IEEE Neural Network Pioneer Award, the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award, the 2016 Lovie Award for Lifetime Achievement, the University of Pennsylvania Pender Award, and honorary doctorates from IPN, Mexico and EPFL. He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for “conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.”
R. Benjamin Shapiro
University of Colorado Boulder
Abstract: Computer science is a field of remarkable breadth, with problems in human-computer interaction alone spanning natural language processing, visual, audible, and tangible interfaces, accessible design, social computing, art-making. Machine learning is now being applied in every one of these domains. Bruner claimed that “any subject can be taught effectively in some intellectually honest form to any child at any stage of development.” Computing education must take up this call, including offering developmentally-appropriate machine learning education. I will present a vision for how this could unfold, share progress on my team’s efforts to develop machine learning education for youth, and discuss ongoing challenges.
R. Benjamin Shapiro is an Assistant Professor of Computer Science at the University of Colorado Boulder. He is also faculty, by courtesy, in Learning Sciences & Human Development (School of Education) and the Department of Information Science (College of Media, Communication, and Information). His research group, the Laboratory for Playful Computation (LPC), investigates the design of experiences and technologies for young people to learn computer science through collaborative, creative expression and through their own design of interactive technologies to solve problems in their homes and communities.