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760 points MindBreaker2605 | 1 comments | | HN request time: 0.26s | source
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numpy-thagoras ◴[] No.45897574[source]
Good. The world model is absolutely the right play in my opinion.

AI Agents like LLMs make great use of pre-computed information. Providing a comprehensive but efficient world model (one where more detail is available wherever one is paying more attention given a specific task) will definitely eke out new autonomous agents.

Swarms of these, acting in concert or with some hive mind, could be how we get to AGI.

I wish I could help, world models are something I am very passionate about.

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sebmellen ◴[] No.45897629[source]
Can you explain this “world model” concept to me? How do you actually interface with a model like this?
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curiouscube ◴[] No.45899047[source]
One theory of how humans work is the so called predictive coding approach. Basically the theory assumes that human brains work similar to a kalman filter, that is, we have an internal model of the world that does a prediction of the world and then checks if the prediction is congruent with the observed changes in reality. Learning then comes down to minimizing the error between this internal model and the actual observations, this is sometimes called the free energy principle. Specifically when researchers are talking about world models they tend to refer to internal models that model the actual external world, that is they can predict what happens next based on input streams like vision.

Why is this idea of a world model helpful? Because it allows multiple interesting things, like predict what happens next, model counterfactuals (what would happen if I do X or don't do X) and many other things that tend to be needed for actual principled reasoning.

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1. HarHarVeryFunny ◴[] No.45899559[source]
Learning from the real world, including how it responds to your own actions, is the only way to achieve real-world competency, intelligence, reasoning and creativity, including going beyond human intelligence.

The capabilities of LLMs are limited by what's in their training data. You can use all the tricks in the book to squeeze the most out of that - RL, synthetic data, agentic loops, tools, etc, but at the end of the day their core intelligence and understanding is limited by that data and their auto-regressive training. They are built for mimicry, not creativity and intelligence.