Why does machine learning work at all? Neural networks power everything from image recognition to language models, yet the science behind their success remains strangely elusive. In this essay, Stephen Wolfram strips machine learning down to its barest forms-minimal models built from simple rules-and shows that even at this level, systems can learn. What emerges is a surprising picture: machine learning doesn't rely on carefully engineered structures but on the natural complexity of the computational universe.
Seen this way, machine learning is less about hidden design and more about sampling complexity. Wolfram's exploration offers not only clarity about why AI works but also perspective on its limits-why some successes resist explanation, and why the field may never yield a simple unifying theory.
