Part 4/13:
Better alignment with human values: Traditional explicit models struggle to encode complex human preferences, such as smoothness of ride or cautious obstacle avoidance. Neural networks can learn these preferences implicitly from vast data.
Homogeneous compute: Using a single neural network ensures predictable, deterministic latencies—crucial for real-time systems—compared to traditional branching models with variable computational delays.