Multimodal AI combines different types of data, such as text, images, audio, and video, to enhance its understanding and generate richer outputs. Instead of working with just one type of data at a time, multimodal systems process various inputs simultaneously. For example, a multimodal AI application could analyze a video by simultaneously considering the visual frames, audio track, and any spoken text in subtitles. This integrative approach enables the model to capture context and improve accuracy in tasks like image captioning or video summarization.
To achieve this combination of data, multimodal AI typically uses specialized models that can handle different data types. Each data type has its own encoder, which translates the input into a common representation or embedding that the AI can work with. For instance, a convolutional neural network (CNN) can be used for image processing, while a recurrent neural network (RNN) or transformer model can handle textual information. Once the different encoders process the data, a fusion layer merges these representations into a unified format. This allows the AI to make informed predictions or generate outputs that consider all aspects of the input.
Real-world applications of multimodal AI can be seen in various fields. For example, in healthcare, a model can analyze medical images alongside patient reports to provide a more accurate diagnosis. Similarly, social media platforms can use multimodal AI to analyze user-generated content by combining text captions, photos, and videos to better understand trends or user sentiment. By integrating and processing multiple types of data, multimodal AI enables more comprehensive insights and improves the overall effectiveness of AI systems.