AI CAN SEE YOU You can now download the source code that sparked the AI boom CHM releases code for 2012 AlexNet breakthrough that proved "deep learning" could work. Benj Edwards Mar 24, 2025 6:14 pm | 0 Credit: ArtemisDiana via Getty Images Credit: ArtemisDiana via Getty Images Story textSizeSmallStandardLargeWidth *StandardWideLinksStandardOrange* Subscribers only Learn moreOn Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet, the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 by proving that "deep learning" could achieve things conventional AI techniques could not.Deep learning, which uses multi-layered neural networks that can learn from data without explicit programming, represented a significant departure from traditional AI approaches that relied on hand-crafted rules and features.The Python code, now available on CHM's GitHub page as open source software, offers AI enthusiasts and researchers a glimpse into a key moment of computing history. AlexNet served as a watershed moment in AI because it could accurately identify objects in photographs with unprecedented accuracycorrectly classifying images into one of 1,000 categories like "strawberry," "school bus," or "golden retriever" with significantly fewer errors than previous systems.Like viewing original ENIAC circuitry or plans for Babbage's Difference Engine, examining the AlexNet code may provide future historians insight into how a relatively simple implementation sparked a technology that has reshaped our world. While deep learning has enabled advances in health care, scientific research, and accessibility tools, it has also facilitated concerning developments like deepfakes, automated surveillance, and the potential for widespread job displacement.But in 2012, those negative consequences still felt like far-off sci-fi dreams to many. Instead, experts were simply amazed that a computer could finally recognize images with near-human accuracy.Teaching computers to seeAs the CHM explains in its detailed blog post, AlexNet originated from the work of University of Toronto graduate students Alex Krizhevsky and Ilya Sutskever, along with their advisor Geoffrey Hinton. The project proved that deep learning could outperform traditional computer vision methods.The neural network won the 2012 ImageNet competition by recognizing objects in photos far better than any previous method. Computer vision veteran Yann LeCun, who attended the presentation in Florence, Italy, immediately recognized its importance for the field, reportedly standing up after the presentation and calling AlexNet "an unequivocal turning point in the history of computer vision." As Ars detailed in November, AlexNet marked the convergence of three critical technologies that would define modern AI.According to CHM, the museum began efforts to acquire the historically significant code in 2020, when Hansen Hsu (CHM's curator) reached out to Krizhevsky about releasing the source code due to its historical importance. Since Google had acquired the team's company DNNresearch in 2013, it owned the intellectual property rights.The museum worked with Google for five years to negotiate the release and carefully identify which specific version represented the original 2012 implementationan important distinction, as many recreations labeled "AlexNet" exist online but aren't the authentic code used in the breakthrough.How AlexNet workedWhile AlexNet's impact on AI is now legendary, understanding the technical innovation behind it helps explain why it represented such a pivotal moment. The breakthrough wasn't any single revolutionary technique, but rather the elegant combination of existing technologies that had previously developed separately.The project combined three previously separate components: deep neural networks, massive image datasets, and graphics processing units (GPUs). Deep neural networks formed the core architecture of AlexNet, with multiple layers that could learn increasingly complex visual features. The network was named after Krizhevsky, who implemented the system and performed the extensive training process.Unlike traditional AI systems that required programmers to manually specify what features to look for in images, these deep networks could automatically discover patterns at different levels of abstractionfrom simple edges and textures in early layers to complex object parts in deeper layers. While AlexNet used a CNN architecture specialized for processing grid-like data such as images, today's AI systems like ChatGPT and Claude rely primarily on Transformer models. Those models are a 2017 Google Research invention that excels at processing sequential data and capturing long-range dependencies in text and other media through a mechanism called "attention."For training data, AlexNet used ImageNet, a database started by Stanford University professor Dr. Fei-Fei Li in 2006. Li collected millions of Internet images and organized them using a database called WordNet. Workers on Amazon's Mechanical Turk platform helped label the images.The project needed serious computational power to process this data. Krizhevsky ran the training process on two Nvidia graphics cards installed in a computer in his bedroom at his parents' house. Neural networks perform many matrix calculations in parallel, tasks that graphics chips handle well. Nvidia, led by Jensen Huang, had made their graphics chips programmable for non-graphics tasks through their CUDA software, released in 2007.The impact from AlexNet extends beyond computer vision. Deep learning neural networks now power voice synthesis, game-playing systems, language models, and image generators. They're also responsible for potential society-fracturing effects such as filling social networks with AI-generated slop, empowering abusive bullies, and potentially altering the historical record.Where are they now?In the 13 years since their breakthrough, the creators of AlexNet have taken their expertise in different directions, each contributing to the field in unique ways.After AlexNet's success, Krizhevsky, Sutskever, and Hinton formed a company called DNNresearch Inc., which Google acquired in 2013. Each team member has followed a different path since then. Sutskever co-founded OpenAI in 2015, which released ChatGPT in 2022, and more recently launched Safe Superintelligence (SSI), a startup that has secured $1 billion in funding. Krizhevsky left Google in 2017 to work on new deep learning techniques at Dessa.Hinton has gained acclaim and notoriety for warning about the potential dangers of future AI systems, resigning from Google in 2023 so he could speak freely about the topic. Last year, Hinton stunned the scientific community when he received the 2024 Nobel Prize in Physics alongside John J. Hopfield for their foundational work in machine learning that dates back to the early 1980s.Regarding who gets the most credit for AlexNet, Hinton described the project roles with characteristic humor to the Computer History Museum: "Ilya thought we should do it, Alex made it work, and I got the Nobel Prize."Benj EdwardsSenior AI ReporterBenj EdwardsSenior AI Reporter Benj Edwards is Ars Technica's Senior AI Reporter and founder of the site's dedicated AI beat in 2022. He's also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC. 0 Comments