Part 5/11:
An important aspect of expanding the neural network size is acquiring diverse, high-quality training data. Tesla’s simulation-to-real (sim-to-real) transfer method—refining AI models in virtual environments before real-world deployment—makes this feasible at scale. Using techniques like specialized sub neural networks, Tesla can activate only the relevant portions of the AI system for specific tasks, reducing latency and computational load.
While more complex neural nets tend to be more demanding, Tesla’s architecture is designed to mitigate these challenges. The modular approach—where different “expert” sub neural networks specialize in functions like walking—enables efficient real-time responses and adaptability, even as the neural network size grows.