Inferthermic

But also the new generative models, the ones that start with pure noise and gradually denoise toward structure. Alex remembers reading about diffusion models last year and thinking they were just another trick, another architecture to memorize for interviews. He didn't see the deeper pattern: they're literally implementing entropy reduction, teaching a system to walk backward from chaos toward meaning. The arrow of time points toward disorder, but intelligence can run backward, can reconstruct the photograph from the static, can find the signal in the noise. Complex dynamics. Chaos theory, strange attractors, the butterfly effect. Simple rules generating infinite complexity. Alex reads about the logistic map, that simple equation x_{n+1} = r x_n (1 - x_n), and how changing one parameter transforms stable fixed points into oscillations into full chaos. He thinks about learning rates, about how neural network training goes through phase transitions - stable convergence, then oscillation, then divergence. There's a critical point, a sweet spot where computation happens, where information flows and structures emerge. Too ordered, nothing happens. Too chaotic, nothing persists. The fields blur together in his mind, a constellation of concepts orbiting the central insight: intelligence isn't magic, it's physics. It's the universe's way of fighting its own heat death, local pockets of entropy reduction that can bootstrap themselves into understanding.