Abstract

Learning to solve complex sequences of tasks—while both leveraging transfer and avoiding catastrophic forgetting—remains a key obstacle to achieving human-level intelligence. Interactive environments such as complex outdoor scenes or changing video games present a challenge for agents, since they must demonstrate robustness and adaptability. In this talk, I will discuss the role of deep neural architectures in supporting and structuring continual learning.

Speaker Bio

Raia Hadsell, a senior research scientist at Google DeepMind, has worked on deep learning and robotics problems for over 10 years. Her thesis on Vision for Mobile Robots won the Best Dissertation award from New York University, and was followed by a post-doc at Carnegie Mellon’s Robotics Institute. Raia then worked as a senior scientist and tech manager at SRI International. Raia joined DeepMind in 2014, where she leads a research team studying robot navigation and lifelong learning.