Luca Rigazio, Director of Engineering for the Panasonic Silicon Valley Laboratory, presents the "Unsupervised Everything" tutorial at the May 2017 Embedded Vision Summit.
The large amount of multi-sensory data available for autonomous intelligent systems is just astounding. The power of deep architectures to model these practically unlimited datasets is limited by only two factors: computational resources and labels for supervised learning. Rigazio argues that the need for accurate labels is by far a bigger problem, as it requires careful interpretation of what to label and how, especially in complex and multi-sensory settings. At the risk of stating the obvious, we just want unsupervised learning to work for everything we do, right now. While this has been a "want" of the AI/Machine-Learning community for quite some time, unsupervised learning has made an impressive leap in just the last year. Rigazio discusses the latest breakthroughs and highlights the massive potential for autonomous systems, as well as presenting the latest results from his team.
