AI has gone big, and so have AI models. 10-billion-parameter universal models are crushing 50-million-parameter task-specific models, demonstrating superior performance at solving many tasks from a single model.
AI models are also becoming multi-modal. New vision models like Microsoft’s Florence 2 and OpenAI’s GPT-4V are expanding the applications of these models to incorporate images, video, and sound, bringing the power of large language models (LLMs) to millions of new use cases.
As bigger has proven to be better in the world of model engineering, every application has undergone a similar progression:
