Multimodal AI
Multimodal AI is artificial intelligence that can process and connect more than one type of data, such as images, video, audio and text, within a single model or system. For video, this means the model can relate what is said to what is shown on screen. A multimodal model can, for example, read a slide, hear the speaker and summarize both together.
Why it matters
Many video problems cannot be solved from one signal. A harmless image can carry hateful audio, and a slide can say what the speaker skips. Multimodal models catch these cases, improving moderation, search and summaries without stitching separate tools together.
How it works
- Each input type, such as frames, audio and text, passes through its own encoder (a model that turns data into numbers).
- The encoders map their outputs into a shared space so related images, sounds and words line up.
- A single model reasons over the combined inputs to answer, classify or generate.
- The output can be text, labels or new media such as an image or video clip.
What is an example of multimodal AI?
A model that watches a recorded lecture and writes a summary using both the slides and the spoken words is multimodal. Other examples include text-to-video generators, which turn a written prompt into moving images, and assistants that answer a question about a photo. In each case, the model connects more than one type of data.
Multimodal AI vs. a large language model: what is the difference?
A classic large language model only reads and writes text. Multimodal AI adds other inputs or outputs, such as images, audio or video. Many newer assistants are multimodal language models, meaning a language model has been extended with encoders for pictures and sound. For video work, that extension is what lets the model relate speech to visuals.
Where it's used
For generative AI video companies, it turns text prompts into clips with matching sound. Creator platforms use it to catch harmful audio over harmless images, and SaaS products use it to answer questions about recordings.
By the numbers
- OpenAI's CLIP, a model that links images and text in one shared space, was trained on 400 million image and text pairs collected from the internet. (Learning Transferable Visual Models From Natural Language Supervision (arXiv))
