Much of the world may be on hold, but AI research is still booming. The volume of peer-reviewed AI papers has grown by more than 300 percent over the last two decades, and attendance at AI conferences continues to increase significantly, according to the Stanford AI Index. In 2020, AI researchers made exciting progress on applying transformers to areas other than natural-language processing (NLP) tasks, bringing the powerful network architecture to protein sequences modelling and computer vision tasks such as object detection and panoptic segmentation. Improvements this year in unsupervised and self-supervised learning methods meanwhile evolved these into serious alternatives to traditional supervised learning methods.
As part of our year-end series, Synced highlights 10 artificial intelligence papers that garnered extraordinary attention and accolades in 2020.
Here is a quick read: 2020 in Review | 10 AI Papers That Made an Impact