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This essay breaks down the term “world model” by tracing it to the POMDP perception-action loop and identifying its key components—renderers, simulators, planners, and their connecting loop. It shows how different AI fields project parts of this loop to build spatial and temporal understanding beyond language models.
- The term "world model" is overloaded—video generators with impossible physics and precise physics engines both claim the label, which confuses the field.
- Decomposing the POMDP loop into renderer (state→observation), simulator (action→state), and planner (observation→action) explains why different fields define "world models" so differently.
- Computer vision, robotics, and generative AI each project only one slice of this same underlying loop, rather than building genuinely different concepts.
Computer scientist Yann LeCun discusses the nature of intelligence as a learning process in a recent interview. He explores the implications of AI's predictive capabilities and the ethical considerations surrounding its development, while also sharing insights into the current state and future of artificial intelligence.
- LeCun argues current LLMs are fundamentally limited because they lack world models and can't plan or reason like humans/animals do
- He predicts today's autoregressive LLM approach will be largely obsolete within a few years, replaced by systems trained on video/sensory data to build predictive world models
- He downplays near-term AGI/superintelligence fears, framing intelligence as requiring grounded learning from the physical world rather than just scaling text-based models