What is Multimodal AI?
Turkish: Çok Modlu Yapay Zeka (Multimodal AI)
Multimodal AI can understand and generate across data types such as text, images, audio, video, and structured tables.
What is Multimodal AI?
Multimodal AI refers to models that are not limited to one data type. A system can read text, interpret an image, analyze values in a table, or extract meaning from an audio recording.
For example, a support application can evaluate a customer’s written description, screenshot, and system log together to classify the issue more accurately. NLP handles the text, while computer vision handles visual content.
How It Works
Multimodal systems move different inputs into a shared representation space. Images, text, and audio may pass through separate encoders; the model then learns relationships across those representations. Some systems only interpret input, while others can also generate image, text, or audio output.
In document processing, OCR remains an important layer for extracting text from scanned invoices or forms. A multimodal model can then evaluate the extracted text together with visual layout and context.
Business Use
Multimodal AI can support invoice checks, product image analysis, quality inspection, call center summaries, training material generation, and field service reports. In critical workflows, model outputs should be verified against the source image, text, or recording.
Related Terms
Computer vision combines AI and image processing to extract objects, text, defects, or motion from photos, video, and camera feeds.
LLM (Large Language Model)An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.
NLP (Natural Language Processing)NLP is the AI field that processes human language as text or speech for tasks such as classification, search, summarization, and generation.