The article explores the ongoing trend in artificial intelligence towards larger models with thousands of billions of parameters, arguing that while these models offer broad capabilities, they often come with high costs and inefficiencies. It highlights the growing recognition among companies that specialized AI models tailored to specific tasks can achieve superior results at lower costs.
Research indicates that increasing model parameters is three times more important than expanding training data, yet businesses are discovering that smaller models can perform just as well or better for specific tasks. This shift in perspective challenges the dominance of large language models (LLMs), suggesting that for routine business tasks, smaller models offer a more precise, efficient, and economical solution. The article argues that organizations should prioritize understanding their specific needs before investing in large models, as smaller models can significantly enhance productivity and outcomes without the associated high costs of their larger counterparts.
👉 Pročitaj original: CIO Magazine
