Future Generative AI Models: Size Considerations

This report discusses the evolution of generative AI models, particularly focusing on the Transformer architecture that has significantly influenced large language models (LLMs) since its introduction in 2017. The document outlines the advantages of Transformer models in enhancing deep learning capabilities, including their ability to analyze data relationships more effectively than previous models. However, it also addresses the limitations of LLMs, such as hallucinations and resource constraints, which could hinder their future development. The report highlights ongoing research into small language models (SLMs) as a potential solution to these challenges, noting recent releases by Google and Microsoft that aim to deliver comparable performance to LLMs while consuming fewer resources. Furthermore, the document speculates on the future accessibility of SLMs, suggesting they may enable a new era of personal AI applications that can operate locally on devices like smartphones and PCs, thus transforming user interactions with AI technology.