Physicists have developed a mathematical “toy model” using statistical physics to explain one of the great mysteries of deep learning: why massive neural networks learn patterns instead of just memorizing data. By applying renormalization theory, the team has shown how high-dimensional fluctuations stabilize learning, paving the way for more efficient and predictable artificial intelligence.
