Touch is a crucial sensor modality for both humans and robots, as it allows us to directly sense object properties and interactions with the environment. Recently, touch sensing has become more prevalent in robotic systems, thanks to the increased accessibility of inexpensive, reliable, and high-resolution tactile sensors and skins. Just as the widespread availability of digital cameras accelerated the development of computer vision, we believe that we are rapidly approaching a new era of computational science dedicated to touch processing. We believe that AI/ML will play a critical role in successfully processing touch as a sensing modality. However, this raises important questions regarding which computational models are best suited to leverage the unique structure of touch, similar to how convolutional neural networks leverage spatial structure in images. The development and advancement of touch processing will greatly benefit a wide range of fields, including tactile and haptic use cases. For instance, advancements in tactile processing (from the environment to the system) will enable robotic applications in unstructured environments, such as agricultural robotics and telemedicine. Understanding touch will also facilitate providing sensory feedback to amputees through sensorized prostheses and enhance future AR/VR systems.