  Sora from Open AI has shown exceptional performance, yet it faces scrutiny
over whether its technological prowess equates to an authentic comprehension of
reality. Critics contend that it lacks a foundational grasp of the world, a
deficiency V-JEPA from Meta aims to amend with its joint embedding approach.
This debate is vital for steering the future direction of Artificial General
Intelligence(AGI). We enrich this debate by developing a theory of productive
imagination that generates a coherent world model based on Kantian philosophy.
We identify three indispensable components of the coherent world model capable
of genuine world understanding: representations of isolated objects, an a
priori law of change across space and time, and Kantian categories. Our
analysis reveals that Sora is limited because of its oversight of the a priori
law of change and Kantian categories, flaws that are not rectifiable through
scaling up the training. V-JEPA learns the context-dependent aspect of the a
priori law of change. Yet it fails to fully comprehend Kantian categories and
incorporate experience, leading us to conclude that neither system currently
achieves a comprehensive world understanding. Nevertheless, each system has
developed components essential to advancing an integrated AI productive
imagination-understanding engine. Finally, we propose an innovative training
framework for an AI productive imagination-understanding engine, centered
around a joint embedding system designed to transform disordered perceptual
input into a structured, coherent world model. Our philosophical analysis
pinpoints critical challenges within contemporary video AI technologies and a
pathway toward achieving an AI system capable of genuine world understanding,
such that it can be applied for reasoning and planning in the future.
