How do you get computers to understand and interact with worlds the way humans do?
For decades, creating virtual environments has meant painstakingly coding every detail – the physics, the graphics, the rules of interaction. Game developers like me spend years building these worlds, writing specific logic for how every object should behave. Want a door to open? You need to program that. Want water to flow? You need to write fluid dynamics code. This manual approach has served us well for traditional games, but it becomes a major bottleneck when we think about training artificial intelligence.
Why? Because AI systems, like humans, learn best through extensive practice in varied environments. Imagine trying to learn physics if you could only experiment in one very specific laboratory setup. Or trying to learn to cook if you only ever had access to one kitchen with one set of ingredients. This is essentially the situation we’ve been in with AI training environments – limited by how many we can manually create.
This is where world models enter the picture. Instead of hand-coding every environment, what if we could teach AI systems to generate their own training worlds? This has been a tantalizing goal, but previous attempts have run into a fundamental problem: coherence. Generating a single convincing image is one thing. Generating a consistent, interactive world that maintains its properties over time is vastly harder.
What happens when you walk around a room? When you turn away from an object and then back again, you expect it to still be there, with the same properties it had before. This capability, which developmental psychologists call object permanence, emerges in human infants around 8 months of age. It’s a foundational part of how we understand the world. Previous AI systems have struggled to maintain this kind of consistency for more than a few frames.
Source link
lol