Keynotes
In this talk, we will examine the possible impossibilities of AI (e.g., the fundamental limits of transformers), the impossible possibilities of AI (i.e., what seemingly impossible paths might be promising) and the paradox and the dark matter of intelligence. This talk will be purposefully imaginative and inevitably controversial.
Yejin Choi
Yejin Choi is Brett Helsel professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research director at AI2 overseeing the project Mosaic. Her research investigates a wide variety of problems across NLP and AI including commonsense knowledge and reasoning, neural language (de-)generation, language grounding with vision and experience, and AI for social good. She is a MacArthur Fellow and a co-recipient of the NAACL Best Paper Award in 2022, the ICML Outstanding Paper Award in 2022, the ACL Test of Time award in 2021, the CVPR Longuet-Higgins Prize (test of time award) in 2021, the NeurIPS Outstanding Paper Award in 2021, the AAAI Outstanding Paper Award in 2020, the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, IEEE AI's 10 to Watch in 2016, and the ICCV Marr Prize (best paper award) in 2013. She received her Ph.D. in Computer Science at Cornell University and BS in Computer Science and Engineering at Seoul National University in Korea.
Climate change is a societal and political problem whose impact could be mitigated by technology. Underlying many technical challenges is a surprisingly simple yet challenging problem; modeling the interaction of atoms. Approaching this problem from the perspective of a computer vision researcher has the potential to offer new insights into this growing and impactful field.
Larry Zitnick
Larry Zitnick is a research director on the Fundamental AI Research team at Meta. He is currently focused on scientific applications of AI and machine learning, such as the discovery of new catalysts for renewable energy applications. Previously, his research in computer vision covered many areas such as the FastMRI project to speed up the acquisition of MRIs, and the COCO and VQA datasets to benchmark object detection and visual language tasks. He developed the PhotoDNA technology used by industry and various law enforcement agencies to combat illegal imagery on the web. Before joining FAIR, he was a principal researcher at Microsoft Research. He received the PhD degree in robotics from Carnegie Mellon University.
Many computer vision ideas have been revisited again and again and again, including current modern computer vision based on neural computation. This round has led to incredible developments in computational hardware. Might such powerful computation breathe life into older neglected ideas?
Rodney Brooks
Rodney Brooks came to the US from Australia in 1977. His PhD (1981) work at the Stanford Artificial Intelligence Lab was in model based computer vision in the "hand-eye group". After post-docs at CMU and MIT he joined the faculty at Stanford for one year, then joined the MIT faculty in 1984. There he formed a robotics research group that developed mobile and humanoid robots, many of which were vision-based. In 1987 he and Takeo Kanade founded the International Journal of Computer Vision. He became director of the MIT Artificial Intelligence Lab in 1997 and in 2003 he became the founding director of MIT CSAIL (Computer Science and Artificial Intelligence Lab). Along the way he has founded six startups, including iRobot, Rethink Robotics, and now Robust AI, which is developing a vision based collaborative mobile robot for existing cluttered warehouses.