Yi Ma: Pursuing the Nature of Intelligence

In this talk, we will try to clarify different levels and mechanisms of intelligence from historical, scientific, mathematical, and computational perspective. From the evolution of intelligence in nature, from phylogenetic, to ontogenetic, societal, and to scientific intelligence, we will try to shed light on how to understand the true nature of the seemingly dramatic advancements in the technologies of machine intelligence in the past decade. We achieve this goal by developing a principled theoretical framework to explain deductively the practices of deep representation learning from the first principle of pursuing low-dimensional structures in data distributions. This framework not only reveals true nature hence both capabilities and limitations of the current deep architectures, and but also provides principled guidelines to develop more complete and more efficient learning architectures and systems. Eventually, we will clarify the difference and relationship between Knowledge and Intelligence, which may guide us to pursue the goal of developing systems with true intelligence, at least at the level for a predictive and generative memory. If time permits, we will also showcase some of the ongoing new technological developments towards realizing intelligence within an open real physical world.