World models are fast becoming popular to aid in further training of generative AI and large language models (LLMs). Doing so boosts AI. Here's the inside scoop.
In today’s column, I identify how the use of world models is radically reshaping the nature of generative AI and large language models . Here’s the deal. The usual approach to data training for generative AI and LLMs consists of using a vast corpus of data such as text found across the Internet and having the AI pattern-match on that encountered data. An additional and quickly emerging augmented approach involves establishing a so-called world model that interacts with the budding LLM.
“Training agents in the real world is even more expensive, so world models that are trained incrementally to simulate reality may prove to be useful for transferring policies back to the real world.” A famous quote from the 1970s by one of the luminaries in a field known as system dynamics well-illustrates a big weakness or limitation of world models:
The AI might simply accept as true that base runners can run at the speed of light. The AI now has a falsehood that was carried over from the world model being used to instruct the AI.Perhaps during the playing of the online video baseball game, the AI determines that a good strategy in baseball seems to be that if you hit the umpire with your bat, the ump will let you automatically take first base. The AI has gleaned something about baseball that doesn’t align with reality.
This prepares the AI and does so with essentially no risk of harm to anyone. The moment you put the AI into an actual car, there is a chance that the AI might steer wrong or otherwise drive incorrectly. The computational world model approach is a lot less risky, and a lot less costly, versus once you put the AI onto the roadway.
Generative AI Large Language Model LLM Simulation Experiments Computational Resources Artificial Neural Networks System Dynamics Jay Forrester Baseball Self-Driving Cars Openai O1 O3 Chatgpt GPT-4O
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