Multimodal AI is designed to process and analyze information from multiple sensory inputs—such as text, images, audio, and video—simultaneously. By integrating these different data types, it can generate more comprehensive insights and make better-informed decisions. For instance, when analyzing a video, multimodal AI can evaluate the visual content alongside the spoken dialogue and any background sounds, creating a more holistic understanding of the situation depicted.
To achieve this, multimodal AI systems typically utilize separate models for each mode of input that then feed into a central model. For example, a common approach is to use convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) or transformers for text and audio. These models extract relevant features from their respective inputs, which are then combined. This integration might involve aligning the outputs through mechanisms like attention layers or pooling strategies, allowing the system to focus on the most relevant information from each modality when making predictions or generating outputs.
A practical application of multimodal AI can be found in smartphone assistants that analyze voice commands while considering context from the user's location or visual inputs from the camera. For example, when a user asks for a restaurant recommendation while holding the phone in front of a menu, the AI can process the spoken request, read the text on the menu, and consider the restaurant's location relative to the user's position. This capability not only enhances user experience but also allows for more accurate and context-aware responses.