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Words to World, AI learning strategies for building world models.
Explore AI that builds physically real worlds from text, navigates them with robots, and learns through self-modifying code and a three-level hierarchy for realistic results.
Can a text prompt (or reference art) build a physically real world, no splats, no diffusion, but full physics? Once you have this, can a robot or biocomputer navigate it? Full demo of the system running live (local / remote hybrid) How it is all plumbed and built. The system uses a variety of learning strategies including self-modifying code and rules to both iteratively improve a world model and, iteratively improve the generation of that model AND, iteratively improve the improvement of that model (3 level hierarchy). It uses a large range of optimising strategies to avoid uncanny valley effects and attempt to produce cinematically realistic images.