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?: ...
Two new research efforts are offering deeper insight into how artificial intelligence can be made safer and more effective. Harvard physicists have developed a simplified, physics-inspired model to ...
Overfitting in ML is when a model learns training data too well, failing on new data. Investors should avoid overfitting as it mirrors risks of betting on past stock performances. Techniques like ...
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