The fundamental thread connecting artificial intelligence, human cognition, and the nature of physical reality across the provided sources is information compression and dimensionality reduction.

1. Artificial Intelligence and Machine Learning AI systems learn by distilling chaotic, high-dimensional data into simple, predictive rules. The Information Bottleneck principle explains that deep neural networks succeed by compressing input data—stripping away irrelevant noise and retaining only the features necessary to accurately predict an output. Similarly, tools like Sparse Autoencoders (SAEs) help disentangle complex neural network activations into human-understandable, monosemantic (single-concept) features. This drive for simplicity is also embodied by the "AI Physicist" model, which uses "Occam's Razor" and the Minimum Description Length  ...