The ability to generalize knowledge across similar environments is a key aspect of human intelligence. Unlike humans, current artificial intelligence (AI) models struggle to extract structured, transferable knowledge from dynamic environments. DeepMind’s breakthrough results on Atari games have demonstrated that pure sub-symbolic approaches, such as deep reinforcement learning, lack true understanding: they optimize policies based on raw pixel inputs but fail to generalize when minimal changes are introduced to the environment. This brittleness—where even trivial modifications, such as recoloring a paddle or shifting an object’s position, require retraining from scratch—suggests that these models do not learn in the human sense but instead rely on fragile heuristics.