Theory 5: The Failure of Data-Grounded Models, Generative AI and traditional Machine Learning models are fundamentally incapable of reaching AGI. Because these models rely on finite datasets to represent entities of infinite dimensionality, they inevitably succumb to the Curse of Dimensionality. Consequently, they cannot truly generalize to the universal world, leading instead to overfitting and rote memorization rather than genuine understanding.
Theory 6: The Analog Imperative To achieve AGI, we must move beyond digital architectures. AGI requires a model that mirrors the inherent uniqueness of the physical world. This is only achievable through analog systems, which naturally reflect the continuous and unique state of reality without the need for discrete classifiers or digital approximation. @Zaelani l