@jimmy_d thinks that Dojo is custom hardware for training neural networks that Tesla will keep at its HQ (where Tesla currently keeps GPUs for training neural networks). So, it’s sort of like Tesla’s version of Google’s TPUs, which accelerate neural network training. In Jimmy’s words:

Dojo isn’t going to be a training computer deployed into car, it’s going to be training infrastructure that is optimized to perform unsupervised learning from video at scale. Tesla is probably going to produce custom silicon to enable this because available commercial hardware is inadequate to the task

In addition to unsupervised learning (which is also known as self-supervised learning), I’m curious if other learning approaches could make use of the same training hardware. What about semi-supervised learning, in which there is a mix of human-labelled data and unlabelled data? What about weakly supervised learning using weak labels from human drivers? What about end-to-end reinforcement learning or end-to-end imitation learning?