Genuine Intelligence will never in trillion years emerge from neural networks.

Genuine intelligence will never emerge from neural networks, regardless of scale, according to the author's argument. The post outlines several key points that support this claim. First, the author defines genuine intelligence as the ability to form new concepts from first principles, reason about causal mechanisms unseen before, solve problems outside of past experience, recognize when knowledge is lacking, model other minds accurately, understand language like a human, and integrate these abilities into a unified, comprehending agent.

This definition is grounded in human understanding, not AGI or science fiction. The author then argues that neural networks cannot achieve this definition due to their inherent structural incompatibility. Neural networks are functions that map input vectors to output vectors through linear transformations and nonlinear activations.

While this allows them to approximate continuous functions to arbitrary precision, this does not equate to genuine intelligence. Understanding requires the ability to reason about the mechanisms generating the data, not just interpolate its values. Neural networks lack this capacity because they cannot distinguish causal chains from patterns in data.

Even if neural networks are scaled up significantly, they will still operate as functions, not as agents with true understanding. The architecture of neural networks is fundamentally incompatible with the mechanisms that produce genuine intelligence, making this distinction the core of the argument.