World models, often referred to as world simulators, are emerging as a significant advancement in AI development.
Fei-Fei Li’s World Labs has secured $230 million to create “large world models,” while DeepMind has enlisted one of the developers behind OpenAI’s video generation tool, Sora, to focus on world simulators. But what exactly are these models?
World models draw inspiration from the mental frameworks humans naturally build to understand the world. Our brains take in sensory inputs and construct more concrete mental representations of the world, which we’ve referred to as “models” long before AI used the term. These mental models help shape our perception and predictions about the world around us.
In a study by AI researchers David Ha and Jürgen Schmidhuber, the example of a baseball player illustrates this concept. Batters need to make quick decisions—like swinging a bat—faster than visual signals can reach the brain. They hit a fastball because they subconsciously predict its trajectory based on internal models. These predictions allow professional players to act instinctively without consciously planning.
It’s these subconscious reasoning capabilities that some believe are crucial for achieving human-level intelligence in AI.
Modeling the World
Although the concept of world models isn’t new, they’ve gained renewed attention due to their potential applications, particularly in generating realistic video. Most AI-generated videos tend to fall into the uncanny valley, producing distorted or unrealistic outcomes. For instance, a model might predict that a basketball bounces without fully understanding why. However, a world model with a better grasp of physical principles could generate more accurate, lifelike outcomes.
To develop such insights, world models are trained on a wide range of data, including photos, videos, audio, and text. The aim is to create internal representations of how the world operates, allowing AI to reason about the consequences of actions in more sophisticated ways.
Alex Mashrabov, former Snap AI chief and CEO of Higgsfield, explained, “A viewer expects the world they see to behave like their own reality. If objects move in ways that defy this, it disrupts the experience. A strong world model removes the need for creators to manually define how things behave—it understands these dynamics inherently.”
Beyond video generation, world models could revolutionize forecasting and planning, both digitally and physically. Meta’s Yann LeCun highlighted that these models could enable AI systems to make decisions and take actions more intuitively, like cleaning a room, by reasoning through a sequence of steps—based on deeper understanding rather than pre-learned patterns.
“We need machines that understand the world, remember things, and have common sense,” LeCun said. “Current AI systems fall short in these areas.”
Challenges and Limitations
Despite the promise, several technical challenges remain. Building and running world models requires vast amounts of computational power, far beyond what is currently needed for most AI models. Even early iterations like Sora demand significant GPU resources, raising concerns about accessibility and scalability.
Additionally, world models are prone to biases and “hallucinations” based on their training data. A model trained on videos of sunny European cities might struggle to generate accurate representations of snowy conditions in Korean cities. Ensuring diverse and comprehensive training data is crucial, but that presents its own challenges.
Mashrabov warns, “If training data isn’t broad enough, we’ll see models generating biased representations—whether of race, geography, or environmental conditions.”
Cristóbal Valenzuela from AI startup Runway also pointed out data and engineering challenges, emphasizing the need for models that can generate accurate maps of environments and understand how to navigate them.
If these hurdles are overcome, however, Mashrabov believes world models could transform AI into more robust systems that seamlessly connect with the real world, leading to breakthroughs in robotics, AI decision-making, and virtual environment generation.
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