Today we will dive into one of the most fascinating concepts in AI – world models. Imagine you are playing a video game. Before you take the next step, you replay in your head what might happen: “If I jump there, will I fall? If I use this item, will the door open?” A world model in artificial intelligence does something similar. It is not merely a program reacting to commands but a system that builds its own internal simulation of the world it operates in. It learns how things work, what the relationships between them are, and what will happen next. That is what lets it plan and decide better in unfamiliar situations, much as we do.
Technical definition
World models are a type of artificial intelligence model whose purpose is to build and maintain an internal, often simplified, representation (simulation) of their environment. Unlike models that learn only a direct mapping from input to output (classifying an image, say), world models learn to predict future states of the environment on the basis of current observations and hypothetical actions. This ability lets them understand the dynamics, causal relationships and physical regularities of the world in question.
The architecture of a world model typically comprises three main components. The first is a perception module, which processes raw sensory data (images, text, sensor readings) and encodes it into a compact latent representation. The second is the predictive model itself, often implemented as a recurrent neural network, which predicts the future state in latent space from the current state and a proposed action. The third component is usually a controller (agent), which uses this internal simulation to plan a sequence of actions leading to the goal without having to carry them out in the real world.
World models such as Meta’s JEPA allow agents to learn more efficiently and with fewer real interactions, since they can “imagine” the consequences of their actions in an internal simulation. The approach is very promising for fields such as robotics, autonomous driving and strategic planning. An agent can, for example, learn to drive a car by simulating thousands of scenarios and their outcomes within its model, which enables it to respond better to unforeseen events in real traffic.