Modalities
Different types of information like text, sound, images, or video that AI models can understand and generate.
AI models can work with different kinds of information, called modalities. Think of it like how humans use their senses to understand the world. We can see pictures, hear sounds, read text, and even watch videos.
AI models are getting better at understanding and creating these different types of information. For example, some AI can now describe an image in text, or create an image from a text description. Others can understand spoken words and respond with speech.
Modalities refer to the different types of data streams or sensory inputs that an AI model can process or generate. Common modalities include text, images, audio, video, and increasingly, structured data or even sensor readings. Multimodal models are designed to integrate information from multiple modalities, enabling richer understanding and more versatile applications.
The integration of modalities allows models to perform tasks like image captioning, visual question answering, text-to-image generation, and speech translation. Tradeoffs involve increased computational complexity and the challenge of aligning representations across different data types.
Frontier AI labs are developing systems that can understand and generate multiple types of information. This allows AI to tackle more complex tasks, from scientific research to creative content generation, by processing data beyond simple text.
- Introducing Quine: An AI research system designed for the complexity of biologyMicrosoft · Microsoft Source ·
- One in a million: celebrating the customers shaping AI’s futureOpenAI · OpenAI News ·
- Inside NotebookLM with Raiza Martin and Steven JohnsonGoogle · Google DeepMind: The Podcast ·
- Google Gemini
- Google Gemini's NotebookLM uses AI to process documents and generate podcasts, demonstrating multimodal capabilities.
- Microsoft Copilot
- Microsoft's Quine system is designed to handle complex biological data, suggesting a focus on multimodal scientific applications.
- OpenAI ChatGPT
- OpenAI's AI is used by over a million customers for tasks that were previously impossible, implying the use of advanced, potentially multimodal, capabilities.
- Data Types
- Data types are fundamental classifications of data (e.g., integer, s
- Features
- Features are specific attributes extracted from data used for machine
- Input/Output
- Input and output refer to the data fed into or produced by a model
The concept of modalities in AI has evolved from early work in fields like computer vision and natural language processing, with the term becoming more prominent as models began integrating these distinct data types.
Try using an AI tool that can generate images from text. Describe a scene or object in detail, and see how well the AI can create a visual representation. This demonstrates the AI's ability to translate between the text modality and the image modality.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
Questions people ask
- Can AI models learn from just one type of information?
- Yes, many AI models are trained on and operate with a single modality, like text-only language models. However, the trend is towards multimodal models that can handle several types of information.
- What is an example of a multimodal AI task?
- An example is an AI that can watch a video, understand the spoken dialogue and visual actions, and then generate a written summary of the content.
First seen on Subvolts Dec 22, 2025. Updated Oct 6, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.