AI & Tech
Physical World Model in Data Flywheel
When machines perform precise tasks in the physical world, competitive advantage comes not from optimizing a single algorithm, but from continuously collecting, labeling, and iterating on multimodal interaction data—including tactile, visual, and force feedback. This creates a self-reinforcing cycle: real-world scenarios → data → model improvements → better task execution → higher-quality data. The foundation of this cycle is embodiment—machines must actually perform work in the physical world to generate valuable data.
Read the daily articles behind this idea on the Chinese edition.