Multi-Modal
AI models that can process and generate multiple types of data - text, images, audio, and video. GPT-4V and Gemini are multi-modal.
In-Depth Explanation
Multi-modal AI systems can understand and work with multiple types of data in a unified way. Rather than separate models for text, images, and audio, multi-modal models process all inputs together, understanding relationships between modalities.
Capabilities of multi-modal models:
- Image understanding: Describe, analyse, and reason about images
- Visual question answering: Answer questions about images
- Document analysis: Process PDFs, screenshots, and scanned documents
- Chart and graph interpretation: Extract data from visual formats
- Image generation: Create images from text descriptions
- Audio processing: Transcribe, translate, and understand speech
- Video understanding: Analyse video content and answer questions
Leading multi-modal models:
- GPT-4V/GPT-4o (OpenAI): Text + images + audio
- Gemini (Google): Text + images + audio + video
- Claude 3 (Anthropic): Text + images
- LLaVA (Open source): Text + images
Business applications:
- Receipt and invoice processing
- Product defect detection
- Visual content moderation
- Accessibility improvements
- Automated documentation
Business Context
Multi-modal AI enables processing invoices with images, analysing visual content, and building richer user experiences that combine text and images.
How Clever Ops Uses This
We leverage multi-modal capabilities for Australian businesses in document processing, visual inspection, and creating more natural user interactions.
Example Use Case
"Uploading a photo of a product defect and asking AI to describe the issue, classify its severity, and suggest remediation."