Modality
Modality refers to a distinct type or channel of data that AI systems process. In machine learning and artificial intelligence, modalities represent information encoded in different formats, each requiring specialized computational methods for analysis and interpretation. Common modalities include text, images, audio, and video, though the concept extends to other data types such as sensor readings or structured tabular data.
Unimodal vs. Multimodal Systems
Traditional AI systems are often unimodal, designed to process a single type of data. A text classification model, for example, works exclusively with written language. In contrast, multimodal AI systems can process and integrate information from multiple modalities simultaneously. These systems must learn relationships between different data types—for instance, how visual content relates to accompanying text in an image captioning task.
Processing Requirements
Different modalities present distinct computational challenges. Text typically requires tokenization and embedding techniques, while images need convolutional processing or vision transformers. Audio requires time-series analysis, and video combines spatial and temporal dimensions. Multimodal systems must bridge these differences, often using shared representations or alignment mechanisms that allow the model to understand how information across modalities relates to one another.