[curiouscube]

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.

[cantor_S_drug]

Learning Algorithm Of Biological Networks

https://www.youtube.com/watch?v=l-OLgbdZ3kk

In this video we explore Predictive Coding – a biologically plausible alternative to the backpropagation algorithm, deriving it from first principles.

Predictive coding and Hebbian learning are interconnected learning mechanisms where Hebbian learning rules are used to implement the brain's predictive coding framework. Predictive coding models the brain as a hierarchical system that minimizes prediction errors by sending top-down predictions and bottom-up error signals, while Hebbian learning, often simplified as "neurons that fire together, wire together," provides a biologically plausible way to update the network's weights to improve predictions over time.

[intalentive]

Good summary. For those interested in more details, check out the book Surfing Uncertainty.

[sgt]

So... that seems like possible path towards AGI. Doesn't it?

[Vegenoid]

Only if you also provide it with a way for it to richly interact with the world (i.e. an embodiment). Otherwise, how do you train it? How does a world model verify the correctness of its model in novel situations?

[HarHarVeryFunny]

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.