How we represent signals has major implications for the algorithms we build to analyze them. Today, most signals are represented discretely: Images as grids of pixels, shapes as point clouds, audio as grids of amplitudes, etc. If images weren't pixel grids - would we be using convolutional neural networks today? What makes a good or bad representation? Can we do better? I will talk about leveraging emerging implicit neural representations for complex & large signals, such as room-scale geometry, images, audio, video, and physical signals defined via partial differential equations. By embedding an implicit scene representation in a neural rendering framework and learning a prior over these representations, I will show how we can enable 3D reconstruction from only a single posed 2D image. Finally, I will show how gradient-based meta-learning can enable fast inference of implicit representations, and how the features we learn in the process are already useful to the downstream task of semantic segmentation.