AlexNet's groundbreaking performance in the 2012 ImageNet competition marked a turning point in artificial intelligence, demonstrating the power of deep learning and sparking a revolution in the field. Here are key aspects of AlexNet's impact on AI:

• Outperformed traditional methods: AlexNet significantly outperformed previous approaches to computer vision, proving the effectiveness of deep neural networks for image classification tasks.1

• Scaled with data and compute: AlexNet's success validated the theory that neural network performance would scale with larger datasets and more computing power.2

• Inspired widespread adoption: Before AlexNet, few researchers used neural networks. After its success, neural networks became the dominant approach in machine learning.2

• Catalyzed industry growth: AlexNet's breakthrough led to a flood of innovation and capital investment in AI, as it proved the practical potential of neural networks.3

• Convergence of key factors: AlexNet's success was enabled by the maturation of large-scale labeled datasets, GPU computing, and improved training methods for deep neural networks.4

• Symbolic moment: Fei-Fei Li, creator of the ImageNet dataset, described AlexNet's success as a symbolic convergence of the fundamental elements of modern AI.4

AlexNet's revolutionary impact transformed AI from a largely theoretical field to one with immense practical applications, setting the stage for the development of today's advanced AI systems and generative models.1

The release of AlexNet's source code on GitHub by the Computer History Museum (CHM) and Google provides unprecedented access to a pivotal moment in AI history. The codebase, remarkably compact at just 200KB, combines Nvidia CUDA code, Python script, and C++12. This historic release is comparable to making the original blueprint of the Model T public, offering researchers, developers, and historians the opportunity to examine and study the groundbreaking model that sparked the deep learning revolution23. The source code represents the 2012 version of AlexNet, which was designed to classify images into one of 1,000 object categories45.

The path to releasing AlexNet's source code was a lengthy process, initiated by Hansen Hsu, curator of the CHM Software History Center, in 20201. Due to Google's acquisition of DNNresearch (the company formed by Hinton, Sutskever, and Krizhevsky) in 2013, the rights to the source code belonged to Google, necessitating a complex negotiation2. This five-year effort, spearheaded by Geoffrey Hinton and involving both the CHM and Google, culminated in the public release on March 20, 202534. Jeff Dean, Google's chief scientist, expressed the company's delight in contributing the groundbreaking work to the museum, underscoring the collaborative nature of this historic release35.

The release of AlexNet's source code serves as a crucial historical artifact, preserving a pivotal moment in AI development for future generations to study and build upon. This unprecedented access allows researchers and enthusiasts to gain valuable insights into the foundations of modern AI systems, potentially inspiring new innovations in the field12. The Computer History Museum's decision to make AlexNet's code publicly available aligns with their previous releases of other historic source codes, including APPLE II DOS, IBM APL, Apple MacPaint and QuickDraw, Apple Lisa, and Adobe Photoshop, further cementing the museum's role in preserving technological heritage3.