Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.
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A simple physics-inspired model sheds light on how AI learns
Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily ...
Physics meets AI: Harvard scientists applied renormalization theory to a simplified model, revealing how large neural networks stabilize learning in high‑dimensional spaces. Scaling mystery solved?: ...
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 ...
Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...
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