Demis Hassabis is not a chemist, yet he was one of three recipients of the 2024 Nobel Prize in Chemistry. The prize recognized major contributions to the study of protein structures. Hassabis, a ...
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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 ...
Harvard University physicists have developed a simplified, physics-based mathematical model to better understand how neural networks learn. The approach mirrors historical scientific breakthroughs, ...
Google's Nikola Todorovic said AI can act "like a kind of a black box" while explaining why machine learning was hard to deploy in Search.
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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AI Researchers Are Confronting Neural Networks’ Reasoning Gaps Despite Rapid Advances
In 2026, neural networks are achieving unprecedented efficiency, multimodal integration, and workflow comprehension, yet benchmarks like MLRegTest reveal persistent struggles with formal rule learning ...
The rapid ascent of large-scale artificial intelligence has provided neuroscience with a new set of powerful tools for modeling complex cognitive functions.
Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
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