Unlocking the Secrets of Neural Networks: A Leap Towards Intuitive AI

A groundbreaking study introduces the Average Gradient Outer Product (AGOP), a mathematical model that elucidates how neural networks learn and identify patterns for making predictions. This mechanism, which does not rely on traditional backpropagation, offers a unified understanding across various neural network architectures, including transformers and convolutional networks. Significantly, AGOP enables machine learning models, like kernel machines, to discern task-specific features without prior capability, marking a pivotal advancement in feature learning and machine intelligence research.