Image: seventyfourimages/Envato Elements AlexNet, which was released in 2012, is widely credited with sparking the modern AI revolution, particularly in the field of computer vision. Last week, the Computer History Museum in collaboration with Google made the source code for AlexNet publicly available on GitHub; this move gives researchers, developers, and AI enthusiasts a chance to dive into the foundational code that helped shape today’s AI landscape. What is AlexNet, and why does it matter? AlexNet was the deep-learning model that proved neural networks could significantly outperform traditional image recognition methods. Developed by Alex Krizhevsky, Ilya Sutskever, and their advisor Geoffrey Hinton at the University of Toronto, the model leveraged deep convolutional neural networks (CNNs) to classify images with unprecedented accuracy. The secret to AlexNet’s success wasn’t just its architecture — it was also the massive dataset (ImageNet) it was trained on and the use of GPUs for acceleration. At the time, neural networks were considered impractical due to high computational demands, but by harnessing NVIDIA’s CUDA-enabled GPUs, AlexNet changed that perception. When it entered …