Towards General and Robust Deep Learning at Scale

Modern AI systems have achieved impressive results in many specific domains, from image and speech recognition to natural language processing and mastering complex games such as chess and Go. However, they often remain inflexible, fragile and narrow, unable to continually adapt to a wide range of changing environments and novel tasks without "catastrophically forgetting" what they have learned before, to infer higher-order abstractions allowing for systematic generalization to out-of-distribution data, and to achieve the level of robustness necessary to "survive" various perturbations in their environment - a natural property of most biological intelligent systems, and a necessary property for successfully deploying AI systems in real-life applications. In this talk, I will provide a brief overview of our recent efforts towards making AI more broad (i.e., general/versatile) and more robust, focusing on continual learning, invariance and adversarial robustness. I will also emphasize the importance of developing an empirical science of AI behaviors, and focus on rapidly expanding field of neural scaling laws, which allow us to better compare and extrapolate behavior of various algorithms and models with increasing amounts of data, model size and computational resources.