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Open-Source Software/Artificial Intelligence – How open-source software shapes AI policy (Brookings)

Alex Engler

Open-source software quietly affects nearly every issue in AI policy, but it is largely absent from discussions around AI policy—policymakers need to more actively consider OSS’s role in AI. Open-source software (OSS), software that is free to access, use, and change without restrictions, plays a central role in the development and use of artificial intelligence (AI). Across open-source programming languages such as Python, R, C++, Java, Scala, Javascript, Julia, and others, there are thousands of implementations of machine learning algorithms. OSS frameworks for machine learning, including tidymodels in R and Scikit-learn in Python, have helped consolidate many diverse algorithms into a consistent machine learning process and enabled far easier use for the everyday data scientist. There are also OSS tools specific to the especially important subfield of deep learning, which is dominated by Google’s Tensorflow and Facebook’s PyTorch. Manipulation and analysis of big data (data sets too large for a single computer) were also revolutionized by OSS, first by the Hadoop ecosystem and later by projects like Spark. These are not simply some of the AI tools—they are the best AI tools. While proprietary data analysis software may sometimes enable machine learning without the need to write code, it does not enable analytics that are as well developed as those in modern OSS.

How open-source software shapes AI policy (brookings.edu)