Multimodal AI supports data fusion techniques by integrating information from various data sources, such as text, images, audio, and video, to create a more comprehensive understanding of a situation or context. Data fusion is the process of combining data from different origins to improve accuracy and inform better decision-making. Multimodal AI takes advantage of machine learning models that can analyze and interpret diverse data types together, leading to richer insights compared to using a single data modality.
For instance, in a self-driving car system, multimodal AI processes data from cameras, lidar, and radar systems. Cameras capture visual scenes, lidar provides 3D mapping of the environment, and radar detects the speed and distance of nearby objects. By fusing data from these modalities, the AI can enhance its object detection capabilities. If the camera fails to recognize a partially obscured object, the lidar can still provide crucial distance information, helping the car navigate safely. This integrated approach increases the reliability of the system and minimizes the chances of accidents.
Moreover, in healthcare applications, multimodal AI can combine patient records (text), medical images (like X-rays), and sound data (like heartbeats) to offer a holistic view of patient health. An AI model analyzing these various data types can identify diseases more accurately than analyzing any single type on its own. For example, combining textual patient histories with imaging data may reveal correlations that help in early disease detection. By supporting data fusion techniques, multimodal AI enhances analytical capabilities and enables more informed decision-making in diverse applications.