Self-supervised AI learns physics to reconstruct microscopic images from holograms
Researchers have unveiled an artificial intelligence-based model for computational imaging and microscopy without training with experimental objects or real data. The team introduced a self-supervised AI model nicknamed GedankenNet that learns from physics laws and thought experiments. Informed only by the laws of physics that universally govern the propagation of electromagnetic waves in space, the researchers taught their AI model to reconstruct microscopic images using only random artificial holograms — synthesized solely from ‘imagination’ without relying on any real-world experiments, actual sample resemblances or real data.
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 understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural…
Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning tasks. Supervised Reinforcement Learning (SRL) reformulates problem-solving as a sequence of logical “actions,” providing rich learning signals during the training process.This approach enables…
Some weeks ago, I posted a set of experiments to "understand" the physical knowledge of MiniMax H3 (original post here). The idea was simple: get an open video of somebody pouring water and replace the water with various liquids. No external references were used. In this set of experiments, I…