Colloquium - Nate Derbinsky, Northeastern University

We are living in a world ripe with data and computing power and so it is no surprise that the current generation of AI systems are leveraging data-driven approaches to yield often surprising advances in such problems as perceptual processing and language understanding. These systems, however, often suffer from key drawbacks, including data inefficiency and lack of interpretability. In this talk I will present diverse examples of general architectures, spanning distributed optimization and reinforcement learning, that leverage a common approach: integrating higher-order knowledge to enhance data-driven processing. Together, this work highlights how bridging the data-knowledge dichotomy may lead to exciting new applications for intelligent systems.