The merging of disparate generative AI models is gaining traction within the AI insider community, with the aim of combining the strengths of multiple models to create a more comprehensive solution. While this practice remains relatively unknown outside the AI realm, it holds the potential to revolutionize the field.
Consider the scenario where Model A excels at generating text essays and interacting in a text mode, while Model B is proficient in solving mathematical problems. By merging these models, a new Model C could be created, offering both strong text generation and mathematical problem-solving capabilities. This all-in-one solution would simplify users’ experience by eliminating the need to switch between different models.
However, merging generative AI models is not without its challenges. The process is complex and risky, with the potential to yield unsatisfactory results. The resulting Model C may end up being weak in both text generation and mathematics, rather than the intended improvement.
Despite these challenges, the allure of merging generative AI models lies in its potential benefits. It offers a faster and potentially more cost-effective alternative to building a new model from scratch. By leveraging existing models, AI developers can save time and resources while striving for an all-encompassing solution.
It is worth noting that proprietary concerns can hinder the merging of generative AI models. Companies that have invested significant resources in developing their own models may be reluctant to merge with others. Consequently, many mergers in this field are typically carried out using open-source generative AI models, where proprietary issues are less of a concern.