AI learns to model the world
"World models" are one paradigm that may dominate if language models don't
We’ve previously poked holes in the claim that the large language models dominating today’s AI development are merely “stochastic parrots” that repeat ideas from their training data. In fact, these AIs can already produce genuinely new insights, and it seems these systems could become far more powerful than humans.
If they don’t, however, AI companies will not give up their quest to build superhuman machines. They’ll keep looking for new paradigms. Today, a TIME op-ed by geopolitics professor and former banking executive Celine Herweijer highlighted one such paradigm: “world models,” in which AIs are explicitly trained to predict features of an environment rather than the next fragment of text in a passage.
To get an idea of what this means, imagine something closer to Google Maps than a typical chatbot: it learns a map of some real-world space, then predicts the flows of traffic or plots a route to a specific destination.
The term “world model” is somewhat misleading. Even an AI trained to predict text can learn to model the world, because human-written text reflects facts about the world. The text “My IKEA table needed fixing, so I drove to Lowe’s to buy a [blank]” is easier to predict if you know that IKEA uses hex keys (which people often misplace or discard) and that Lowe’s carries hardware tools. And some research shows that language models do learn to internally represent their environment in some ways, even when not explicitly trained to do so.
Misleading term or not, “world-model” AIs have made large strides in areas like biology and weather prediction by ingesting a wealth of real-world data. Herweijer argues, perhaps prematurely, that world models are the future:
If their strongest advocates are right, they will become the substrate on which the next generation of AI is built: a scientific tool for understanding systems we cannot fully specify, and the capability AI needs to reason about and act in the physical world.
Because language models still seem to be scaling extremely fast in areas like coding and AI research, I’m skeptical that the specific companies and methods Herweijer references will dominate the future. But the fact remains that, while AI remains largely unregulated, companies will continue to compete to find the algorithm that eats the world.
The analyses and opinions expressed on AI StopWatch reflect the views of the individual contributors and the sources they cover, and should not be taken as official positions of the Machine Intelligence Research Institute.