What is multimodal data?
Information that exists in multiple formats at once. A YouTube video contains spoken words (audio), visual elements (video frames), written descriptions (text), timestamps (metadata), and sometimes captions or comments. Each of those is a different "mode" of data.
Why does it matter?
Because most valuable content is multimodal, but our systems have traditionally processed each mode separately. We'd transcribe audio OR analyze images OR read text. Rarely all three together, and almost never in a way that understands how they relate to each other.
What changed?
Modern AI models can process multiple data types simultaneously and understand the connections between them. A model can now "watch" a video and understand that the person speaking is pointing at a chart while saying "revenue increased," and connect all three elements together.
Why couldn't we do this before?
We could technically extract data from multiple sources, but we couldn't understand the relationships. Old systems might tag a video with "person, chart, office" but couldn't tell you that the person was explaining the chart or that their tone suggested concern about the data.
What's the practical difference?
Search becomes actually useful. Instead of searching for "Q3 earnings video" you can search for "moments where someone expressed concern about revenue decline." Or "scenes where our product appears on screen for more than 5 seconds with positive sentiment."
What unlocks this now?
Transformer models and attention mechanisms. These AI architectures can process different types of data in parallel and weigh which elements are relevant to each other. They can see a frame, hear the audio, read the caption, and understand how all three work together to convey meaning.
Is this just for video?
No. Podcast episodes with show notes, PDFs with images and tables, websites with embedded videos, presentations with speaker notes. Anywhere you have information in multiple formats, multimodal processing makes it more useful.
What's still hard?
Accuracy at scale. These systems are good, but not perfect. And processing large volumes of multimodal content requires serious compute resources. The technology works, but making it fast and affordable for everyday use is still being figured out.
Here’s a follow on for how multimodal AI works, and is likely to become an important topic imminently: https://www.ibm.com/think/topics/multimodal-ai
