AI Can't Learn The Way Humans Do - This Could Fix That
In a Nutshell
The core problem in AI is poor sample efficiency—models need massive data while humans learn from a handful of examples—because current systems lack explicit world models that predict how actions change future states. The solution path is model-based RL using video diffusion models as world models: pretrain on abundant unlabeled video, add cheap action conditioning from limited teleop data, then train policies entirely inside the neural simulator via imagined rollouts. This approach scales to robotics and self-driving because it mirrors human neocortical world modeling, enables test-time planning, and bypasses the need for enormous real-world interaction data.
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One of the biggest open problems in AI is solving sample efficiency—how to get models to quickly learn new tasks or skills from relatively small amounts of training data. Humans learn new games, concepts, and skills after just a handful of tries, while current models often need tens of thousands of data points.
The two major remaining problems are intelligence per watt and intelligence per sample. Intelligence per sample refers to how much more intelligent a model becomes with each additional data point. The rate of skill acquisition differs from total skill acquisition. Current models are poor at getting smarter with fewer samples.
The ARC-AGI test sets demonstrate cases where humans intuitively solve puzzles with thinking and effort, but frontier AI systems cannot. Humans bring inductive bias from K-12 education and math training, whereas models compress the entire internet yet still struggle to generalize to new tasks.
In the extreme case of perfect sample efficiency, zero samples would be required. A perfect world model would eliminate the need to collect samples from the environment. Newton's second law of motion serves as an example—NASA can plan asteroid intercepts years in advance using Newtonian mechanics, allowing the system to act without collecting new environmental samples.
The real world is differentiable, enabling model predictive control. Humans build world models over years of experience, such as entrepreneurs learning what customers want or product taste—predicting what others will like. A 1967 COGSAI study by Richardson showed that participants who physically practiced basketball layups for one hour improved by approximately 24%, while blindfolded participants who only imagined the layups improved by 23%.
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