For years, Artificial Intelligence (AI) has made impressive developments, but it has always had a fundamental limitation in its inability to process different types of data the way humans do. Most AI models are unimodal, meaning they specialize in just one format like text, images, video, or audio. While adequate for specific tasks, this approach makes AI rigid, preventing it from connecting the dots across multiple data types and truly understanding context.
To solve this, multimodal AI was introduced, allowing models to work with multiple forms of input. However, building these systems is not easy. They require massive, labelled datasets, which are not only hard to find but also expensive and time-consuming to create. In addition, these models usually need task-specific…