6G cellular networks will be extremely complex systems that must meet many competing requirements in a large variety of environments and use cases. A key enabling 6G technology will be deep learning, which can unlock previously hidden system-level gains, particularly by effectively learning custom site-specific communication techniques that are optimally adapted for each cell-site. Prof. Jeffrey Andrews presented a short summary of some of his group’s recent discoveries and technologies based on deep learning (DL) that demonstrate a large potential impact in 6G. The first is a novel and practical approach for beam alignment in millimeter wave and THz bands that can achieve a phenomenal speed up, at least 10x and in some cases approaching 1000x, by sensing and exploiting unique aspects of the environment. The second is a new architecture for estimating high dimensional channels by harnessing the expressive power of deep generative networks to develop a custom model of each cell’s channel distribution. The third is a site-specific multicell optimization that rapidly learns near-optimal global settings for each base stations antenna arrays, a problem that is completely intractable using conventional techniques. It is trained and tested on AT&T’s commercial simulation platform and shows large gains over existing 3GPP approaches.