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

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 ...

The role of machine learning and deep learning in wildfire prediction remains limited by geographic concentration, uneven ...

Overview: Generative AI adoption continues driving strong hiring demand across India’s rapidly expanding technology ...

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 ...

Stop throwing money at GPUs for unoptimized models; using smart shortcuts like fine-tuning and quantization can slash your ...

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?: ...

What are the first steps that chart the path for a cell to become a blood cell, neuron cell, or pigment cell? Scientists have ...

Net, a hybrid model that improves energy consumption prediction in low-energy buildings, enhancing accuracy and ...

Artificial intelligence (AI) has become a force to reckon with. The medical world is also inclining towards AI – be it to ...

Regulators are coming for the AI models themselves, and those models can’t answer the questions they’re about to be asked.​ ...

Over the past few decades, computer scientists have developed increasingly advanced artificial intelligence (AI) systems that ...