**Abstract:** Google, in collaboration with the Computer History Museum, has made the source code of the groundbreaking "ImageNet Classification with Deep Convolutional Neural Networks" paper publicly available. Authored by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, this paper, published in 2012, introduced AlexNet, a deep convolutional neural network (CNN) that significantly advanced the field of computer vision and machine learning. The release of the code marks a significant milestone in the history of artificial intelligence, providing researchers, students, and enthusiasts with direct access to the original implementation that sparked numerous advancements in neural network architectures and deep learning techniques. **Key Events:** - **Code Release:** Google, in partnership with the Computer History Museum, has released the original source code of the AlexNet paper. - **Publication of AlexNet Paper:** The seminal paper "ImageNet Classification with Deep Convolutional Neural Networks" was published in 2012. **Key People:** - **Alex Krizhevsky:** One of the primary authors of the AlexNet paper, known for his contributions to deep learning and neural network architecture. - **Ilya Sutskever:** Co-author of the paper and a prominent figure in the field of artificial intelligence, currently the Chief Scientist at OpenAI. - **Geoffrey Hinton:** Co-author and a leading researcher in deep learning, often referred to as the "Godfather of Deep Learning," who has made significant contributions to neural networks and machine learning. **Key Locations:** - **Google:** The tech giant that has facilitated the release of the code, known for its extensive research and development in artificial intelligence and machine learning. - **Computer History Museum:** A museum dedicated to preserving and presenting the history of computing and technology, partnering with Google to make the code accessible. **Time Elements:** - **2012:** The year the AlexNet paper was published, marking a pivotal moment in the field of deep learning. - **Present Day:** The time of the code release, highlighting the ongoing importance and influence of the original work. **Summary:** The announcement by Google and the Computer History Museum to make the source code of the AlexNet paper public is a notable event in the field of artificial intelligence. The paper, "ImageNet Classification with Deep Convolutional Neural Networks," was published in 2012 by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. It detailed the creation of AlexNet, a deep convolutional neural network that achieved a significant breakthrough in image recognition by winning the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with a top-5 error rate of 15.3%, which was more than 10 percentage points lower than the second-place entry. AlexNet's architecture was revolutionary at the time, featuring eight layers with learnable parameters, five convolutional layers, and three fully connected layers. The network was trained on a large dataset of 1.2 million high-resolution images from the ImageNet database, using two GPUs and a novel rectified linear unit (ReLU) activation function. This combination of architectural innovations and effective training techniques set a new standard for deep learning models and inspired a generation of researchers to explore more complex and powerful neural networks. The public release of the code is a valuable resource for the AI community, allowing current and future researchers to study the original implementation and understand the technical details that contributed to AlexNet's success. It also serves as a historical document, preserving the foundational work that has shaped modern deep learning. The availability of the code can facilitate education, inspire new research, and foster a deeper appreciation for the evolution of AI technologies. Google's decision to partner with the Computer History Museum underscores the importance of preserving and sharing significant technological achievements. The museum, which focuses on the history of computing and technology, provides a platform for the code to reach a broader audience, including those who may not be directly involved in AI research but are interested in the historical context and impact of such innovations. This release is particularly timely as deep learning continues to evolve and find applications in various fields, from healthcare and autonomous vehicles to natural language processing and beyond. By providing access to the original AlexNet code, Google and the Computer History Museum are supporting the ongoing development and democratization of AI, ensuring that the foundational knowledge remains accessible and can be built upon by the next generation of technologists and scientists.