The hypothesis suggests that the emergence of intelligence is inherently possible within our physical structure and can be designed by leveraging the structural methods of Transformers, particularly their predictive capabilities. The framework posits that intelligence arises from the ability to predict and interact with the environment, using a combination of feature compression and action interference. This involves creating a continuous feature space where agents can tool-ize features, leading to the development of self-boundaries and personalized desires. The ultimate goal is to enable agents to interact with spacetime effectively, forming an internal model that aligns with the universe's essence. This matters because it provides a theoretical foundation for developing artificial general intelligence (AGI) that can adapt to infinite tasks and environments, potentially revolutionizing how machines learn and interact with the world.
