Is solving video the key next breakthrough in computer vision? We’ll discuss the key challenges in applying deep learning techniques to video understanding. This will include approaches to building high quality datasets and annotating data for video is quite different than image understanding. What are the key use cases for video today and tomorrow? How do we address concerns around privacy and fears about “big brother”? Last but not least how does video advance the field of AI more towards general intelligence and common sense understanding of the physical world in machine learning models..
Roland Memisevic received his PhD in Computer Science from the University of Toronto in 2008. He subsequently held positions as research scientist at PNYLab, Princeton, as post-doctoral fellow at the University of Toronto and ETH Zurich, and as junior professor at the University of Frankfurt. In 2012 he joined the MILA deep learning group at the University of Montreal as assistant professor. He has been on leave from his academic position since 2016 to lead the research efforts at Twenty Billion Neurons, a German-Canadian AI startup he co-founded. Roland is Fellow of the Canadian Institute for Advanced Research (CIFAR).