Unveiling the Dynamics of Open-Source AI Models: Development Trends, Industry Applications, and Challenges

Abstract

In this study, we analyze open-source AI model development and utilization from 2012 to 2024, using data from Hugging Face and Scopus. Our findings reveal a significant surge in model development post-2020, particularly in text processing tasks, likely due to transformer model advancements. However, audio and image processing domains have grown more slowly. User engagement metrics indicate that the top 1% of models, especially in text processing, vastly outperform others, suggesting concentrated interest in specific tasks. Some popular models demonstrate versatility across tasks like image classification and reinforcement learning. The software industry leads in AI usage, followed by healthcare and education. Our study underscores the need for standardized documentation and protocols to improve model transparency and academic rigor, as many models lack clear task associations and training information. These insights provide an overview of the evolving open-model development landscape, highlighting trends, user preferences, and areas for future research and standardization.