Part 9/14:
One of the most technical aspects covered was the enormous input dimensionality Tesla faces. Their vehicles process multiple high-resolution camera streams (7-8 cameras, 5 MP each), at 36 frames per second, resulting in a context window of billions of tokens—equivalent to learning from 30 seconds of driving history with billions of data points.
Despite the high input complexity, Tesla's neural networks condense this information into a low-dimensional control output (steering angle, throttle, brakes). Achieving this causal understanding—mapping billions of inputs to two or three outputs—is a remarkable feat of deep learning, and Tesla’s vast data stores give them a significant edge.