Steps towards making machine learning more natural
Speaker: Mengye Ren, University of Toronto
Date: March 1, 2021, 9 a.m.
Host: Rob Fergus
Over the past decades, we have seen machine learning making great strides in AI applications. Yet, most of its success relies on training models offline on a massive amount of data and evaluating them in a similar test environment. By contrast, humans can learn new concepts and skills with very few examples, and can easily generalize to novel tasks. In this talk, I will highlight three key steps towards making machines learning more human-like, and these steps will unlock the next generation of technologies. The first step is to make machines learn new concepts continually and incrementally using limited labeled data. The second step is to develop flexible representations that can generalize well to novel concepts under different contexts. Finally, I’ll show how to make abstract and compositional reasoning given visual inputs. I will then conclude with an outlook of future directions towards building a more general and flexible AI.
Mengye Ren is a PhD student in the machine learning group of the Department of Computer Science at the University of Toronto. He was also a research scientist at Uber ATG working on self-driving cars from 2017 to 2021. His research focuses on making machines learn in more naturalistic environments with less labeled data. He has won a number of awards including two NVIDIA research pioneer awards and the Alexander Graham Bell Canada Graduate Fellowship.