Introduction
Large Language Models (LLMs) have revolutionized many aspects of technological development. However, these advancements are not without issues, especially for the Free/Libre Open Source Software (FLOSS) ecosystem. How do these models impact our commons, and what can we do to protect them?
The Impact of LLMs
LLMs, such as GPT-4, are powerful tools capable of autonomously generating text. These technologies require enormous amounts of data to train, raising concerns about privacy and data usage. According to a 2023 study, training a model the size of GPT-3 requires approximately 570 TeraFLOPS per second over several weeks, representing a colossal energy consumption.
The Hidden Costs of LLMs
LLMs are not only costly for the companies developing them but also externalize their costs onto society. Hardware prices increase, energy consumption skyrockets, and the environmental impact is significant. For instance, research from the University of Massachusetts revealed that training a language model can emit as much CO2 as a transatlantic flight per passenger.
Implications for FLOSS
Using open source project data to train LLMs raises ethical and legal questions. FLOSS projects are often created to be shared freely, but this does not mean their creators consent to their work being used to enrich commercial models. Codeberg e.V., a popular platform for hosting open source projects, recently voted to ban the use of its data for training LLMs to protect these core values.
Protecting Our Projects
To protect FLOSS projects, it is crucial to review privacy policies and terms of use. Platforms can commit to not using user data to train AIs, as Codeberg has done. Additionally, developers might consider licensing their projects in ways that restrict commercial use or LLM training.
Conclusion
LLMs present a significant challenge to the FLOSS community, but with proactive action, we can protect our commons. The decisions made today will shape the future of open source for years to come.
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