Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Physicists at Harvard University have developed a simplified, physics-inspired mathematical model to better understand how neural networks learn, potentially explaining why large AI systems often ...

Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a "black box." To better ...

Physicists at Harvard University have created a simplified, physics-inspired mathematical model to better understand how neural networks learn without overfitting. The model, based on ridge regression ...