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 significant potential for enhancing the capabilities of generative AI systems.
The motivation behind merging generative AI models lies in the desire to leverage the best features of each individual model. For instance, consider Model A, which excels in generating text essays and summaries but struggles with mathematical problem-solving. On the other hand, Model B is proficient in solving algebraic equations but lacks text generation capabilities. By merging these models, a new Model C could be created, offering strong performance in both text generation and mathematics.
The benefits of such a merger are evident. Users would no longer need to switch between different models for specific tasks, simplifying their workflow and increasing efficiency. Model C would serve as an all-in-one solution, catering to both text generation and mathematical problem-solving needs.
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 not possess the desired strengths in both text generation and mathematics, leading to disappointment and frustration.
Moreover, the economic and business aspects of merging models can pose obstacles. Companies that have invested substantial resources in developing their own models may be reluctant to merge with others, as they seek to maximize profits and protect their proprietary technology. As a result, mergers often occur with open-source generative AI models, where proprietary concerns are less significant.