Post by Bitcoin Dictionary

AI has advanced enormously, but we still have a long way to go. Three fundamental technical problems remain unsolved: causal world models, continual learning, and reliable long-horizon reasoning.

The first is causal world models.

Current AI is exceptionally good at learning statistical relationships. It can predict what usually follows from what it has previously encountered. But prediction is not the same as understanding cause and effect.

An intelligent system must distinguish between “X predicts Y” and “changing X will cause Y.” It must understand interventions, hidden variables, physical constraints and counterfactuals: What will happen if I take this action? What would have happened if I had acted differently?

This matters because an agent changes the world in which it operates. Once it acts, historical correlations may no longer hold. A system that cannot construct a reliable causal model will remain brittle outside familiar situations.

Video generation is not a world model. Producing realistic-looking futures does not prove that the system understands objects, persistence, agency, physics or causation.

The real breakthrough will come when AI can enter an unfamiliar environment, discover its causal structure through limited observation and experimentation, and accurately predict the consequences of genuinely novel actions.

We cannot do that reliably yet.