Multimodal AI models are designed to process and understand different types of data, such as text, images, and audio. To adapt to new data types, these models utilize various techniques that include feature extraction, pre-training on diverse datasets, and fine-tuning. Initially, the model is trained using a large dataset that includes multiple data modalities. For instance, a model might be exposed to pairs of images and their corresponding textual descriptions. This helps the model learn the relationships between different forms of information.
When new data types are introduced, the model can be updated in a few ways. One common method is fine-tuning, where the model is retrained on a smaller, specialized dataset that includes the new data type. For example, if a model initially supports text and images, and a new type such as video is added, developers can gather a dataset of videos with annotations and fine-tune the existing model to accommodate this new input. This approach helps the model adjust its parameters to better understand and generate responses based on the additional data.
Another strategy is to employ modular architectures. In this design, different components can be added or modified without retraining the entire model. For instance, you might have a text encoder and an image encoder that can remain static while you introduce a new video encoder. This modular approach ensures that the model can grow in capability while maintaining efficiency, allowing developers to adapt it to various applications without overhauling the entire system.