Will open source significantly contribute to advanced AI?
Streamlit founders Amanda Kelly and Adrien Treuille
are bullish on the future of open source software, both
in terms of how it will affect generative AI and LLM
projects, and how those AI technologies will affect the
broader open source movement.
1. The open source ecosystem around generative
AI will parallel and rival the corporate ecosystem.
- Meta's open-sourcing of LLaMA and LLaMA 2 in the last six months has led to remarkable innovation in the open source community.
- The availability of these models to academics and the open source community has resulted in the rapid repurposing of Large Language Models (LLMs) for various applications.
- Anticipation of ongoing significant developments in LLMs and large generative models driven by the open source community.
- Expectation of a combination of existing models becoming open source and the emergence of new technologies like Low Rank Adaptation (LoRA).
- LoRA enables academics to fine-tune existing models more efficiently and with reduced memory consumption.
- Surprising genuine levels of innovation occurring outside corporate structures in the realm of 70 billion parameter models.
- The collaborative nature of open source projects, contributes to better results through transparency, diverse perspectives, and passionate contributions. The openness fosters more conversations and leads to better decision-making processes.
2. Generative AI will help the larger open
source movement, beyond just AI, accelerate
and democratize.
- The open source community will gain from generative AI not only in AI-specific projects but across various efforts due to the efficient elimination of tedious human tasks.
- According to Treuille, a significant cost in developing open source is the human-intensive work related to documentation, bug handling, communication, and responding to requests.
- Large Language Models (LLMs) are expected to accelerate open source development by assisting with these human-intensive tasks, ultimately making smaller teams more efficient and powerful.
