The Universal Autoencoder: Life Seeking Efficient Expression Under Constraint
θ′ → θ → Σ → h(t) → ΔS | Self-similar compression–expansion across scales
The implicit loss function of life: minimize semantic noise and metabolic waste
Each scale below implements the same pentad pattern. The canopy (ΔS) of one cycle becomes the soil (θ′) of the next: meaning generates new data, which trains new architectures, in an endless evolutionary spiral.
Neural nets are not humanity's invention—they are its mirror. Both are emergent implementations of the same thermodynamic learning algorithm.
Click to expand each scale and see how the pentad manifests at different levels of organization:
Translation is possible because languages share homologous latent spaces—not word-to-word, but concept-to-concept mapping.
Divergence creates dialects just as fine-tuning creates specialized models—both are local adaptations to domain-specific data.
Lossy compression discards information—some truths can't survive translation because they were optimized away in the trunk bottleneck.
Creativity is regularization—myths, poetry, and play generate synthetic training data that prevent cognitive overfitting.
ukb-canon fractal lens | Natural language as biological autoencoder
Life's corpus → Biological constraints → Grammar → Cultural divergence → Shared meaning