World Models differ from traditional predictive models, emphasizing intervention over passive observation.
Inductive biases play a crucial role in making World Models practical, alongside research in Causal AI and Active Inference for achieving AGI.
Building World Models faces challenges like the 'shadow problem' and avoiding pitfalls of curve fitting in machine learning.
Andre Franca highlights 'a world model is all you need,' emphasizing the significance of this concept in shaping the AI landscape.
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