The author also explains the components of an AI model, including model weights, model code, and the training and fine-tuning processes. The article highlights that the behavior of all models can be entirely changed with just a few hours of fine-tuning on a single modestly-sized computer. The author concludes by emphasizing the importance of open source software for security and innovation and reiterates the need for legislation to focus on regulating the deployment of AI systems rather than the release of AI models.
Key takeaways:
- The article discusses the implications of AI safety legislation, specifically SB 1047, on open source AI model development. It argues that the legislation, as currently written, could potentially hinder open source model development.
- The author suggests that the legislation should focus on regulating the deployment of AI systems, rather than the release of AI models. This is because the release of a model, which is essentially a list of numbers and a text file, does not in itself cause harm. Harm can only occur following the deployment of a system.
- The article also highlights the difference between base models and fine-tuned models. It argues that the current wording of SB 1047 could potentially exclude base models, which are general purpose computation devices, from regulation.
- The author emphasizes the importance of understanding the technical details of AI models in order to create effective legislation. The article provides a detailed explanation of the components of a model, the process of training and fine-tuning a model, and the difference between releasing a model and deploying a system.