In recent months, we've seen an acceleration in the development of multimodal generative AI models , capable of simultaneously processing and generating text, images, audio, and video. Here we answer the most frequently asked questions about this new frontier of AI.
What does it mean for a model to be “multimodal”?
This means it can understand and integrate different types of data (modalities) such as text, images, video, or audio. A multimodal model can, for example, generate an image from a text description or answer questions about a photograph.
What's new about the current models compared to the previous ones?
The key difference is native integration . Models like OpenAI Sora or Google Gemini 1.5 don't work with isolated modules; instead, they understand the entire context, seamlessly combining language and vision. This enables complex tasks such as:
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Generate a video from a detailed description
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Explain what happens in an audiovisual scene
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Modify an image based on a text instruction
Where are these models already being used?
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Film and animation : To create storyboards or even entire videos from scripts.
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Medicine : Combining medical reports with radiological images for more accurate diagnoses.
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Education : Creation of multimodal interactive learning materials.
Is this the basis of a future “general AI”?
This is an important step. Multimodal AI is getting closer to the human way of understanding the world, integrating diverse channels of information. It's not yet "general-purpose," but it's certainly a step towards smarter and more versatile systems.