Deep Learning Breakthrough (2012): The 2012 ImageNet Large Scale Visual Recognition Challenge marked a turning point: AlexNet, a deep convolutional neural network developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, slashed error rates from 26% to 15%, far surpassing traditional computer vision methods. Trained on GPUs and using ReLU activation, dropout regularization, and massive data, AlexNet demonstrated deep learning’s superiority in pattern recognition. This victory triggered an AI renaissance, with industry and academia pivoting to neural networks. Companies like Google, Facebook, and Baidu rapidly adopted deep learning for speech, translation, and recommendation systems. The success proved that scale—data, compute, and model depth—could unlock unprecedented performance. Since then, deep learning has powered nearly all major AI advances, from self-driving cars to medical imaging, cementing its role as the engine of modern artificial intelligence.
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