The Gap Between Expert World Models and LLM Word Models: Why AI Needs Better Reasoning Systems
By
aaronng91
Toasted golden, schmeared with insight. Top of the rack.
Summary
The article discusses the distinction between expert world models and LLM word models, arguing that true expertise involves understanding complex systems, hidden states, and multi-agent interactions rather than just producing probable artifacts. It explores different approaches to world modeling in AI, including 3D video world models (like Google's Genie 3 and Waymo's World Model) and the Meta school's representation learning approaches (JEPA, V-JEPA, EchoJEPA). The core thesis is that LLMs need to evolve beyond single-shot artifact generation to incorporate world models that can reason about hidden variables, agent interactions, and complex systems to achieve true intelligence.
Key quotes
· 4 pulledMost expert work isn't 'produce a probable artifact'; it's 'choose a good move considering other agents, guessing hidden state'
LLMs default to single-shot artifacts and need World Models to progress
There are 3 kinds of World Models conversations today: The first and most common are 3D video world models like Fei Fei Li's Marble and General Intuition's upcoming model
The Meta school of thought comprising JEPA, V-JEPA, EchoJEPA and Code World Models pursuing Platonic representation
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