Event Location: NSH 1507
Bio: C. Lawrence Zitnick is a principal researcher in the Interactive Visual Media group at Microsoft Research, and is an affiliate associate professor at the University of Washington. He is interested in a broad range of topics related to visual object recognition. His current interests include object detection, semantically interpreting visual scenes, and the use of human debugging to identify promising areas for future research in object recognition and detection. He developed the PhotoDNA technology used by Microsoft, Facebook, Google, and various law enforcement agencies to combat illegal imagery on the web. Previous research topics include computational photography, stereo vision, and image-based rendering. Before joining MSR, he received the PhD degree in robotics from Carnegie Mellon University in 2003. In 1996, he co-invented one of the first commercial portable depth cameras.

Abstract: How deeply do machines understand our world? Recent advances in artificial intelligence can give the impression that machines have achieved a surprising level of understanding. However, if examined more closely we can gain insight into the limitations of current approaches and what problems still remain. A case study using the image captioning task is provided. We explore how results may be misinterpreted, and how difficulties in task evaluation can cloud our judgement of progress. We proceed by discussing new methods for learning based on visual abstraction, what machines can learn about visual humor, and new tasks for evaluating artificial intelligence using visual question answering.