What types of data can be used in multimodal AI?

Multimodal AI systems process and combine multiple types of data to improve decision-making or generate outputs. The most common data types include text, images, audio, video, and sensor data. Each modality provides unique information, and integrating them allows models to understand context more effectively. For example, a self-driving car might use camera images (visual data), lidar scans (spatial data), and traffic sign text (language data) to navigate safely. Developers can leverage these data types individually or in combination, depending on the problem they aim to solve.

Text data is widely used for tasks like natural language processing (NLP), sentiment analysis, or translation. Models like BERT or GPT process text as sequences of tokens, often using embeddings to represent words numerically. Image data, represented as pixel arrays, is used in computer vision tasks like object detection (e.g., YOLO models) or facial recognition. Audio data, such as speech or environmental sounds, is often converted into spectrograms or waveforms for tasks like speech-to-text (e.g., Whisper) or emotion detection. Video combines sequential image and audio data for applications like action recognition or video captioning. Sensor data, such as accelerometer readings or temperature measurements, provides time-series information for applications like predictive maintenance or health monitoring.

Combining these data types requires careful alignment and preprocessing. For instance, a medical AI system might correlate MRI scans (images) with patient notes (text) and vital signs (sensor data) to diagnose diseases. Techniques like cross-modal attention in transformers or fusion layers in neural networks help integrate these inputs. Challenges include handling mismatched data formats (e.g., aligning video frames with subtitles) and managing computational complexity. Tools like PyTorch or TensorFlow provide libraries for multimodal workflows, such as loading paired datasets or synchronizing temporal data. By leveraging diverse data types, developers can build robust systems that mimic human-like understanding across domains.