Multimodal

Why It Matters

Multimodal AI is the direction the field is heading. Models that only understand text are increasingly limited compared to systems that can see, hear, and communicate across modalities. GPT-4V, Gemini, and Claude all have multimodal capabilities.

This matters practically because real-world applications often involve multiple data types: analyzing documents with charts, understanding videos with speech, creating presentations with images and text. Multimodal models handle these naturally rather than requiring multiple specialized systems.

For AI engineers, multimodal capabilities are becoming table stakes. Understanding how to integrate vision, audio, and text processing into applications opens up entirely new product categories.

Implementation Basics

Multimodal Architectures

Early Fusion:

• Combine modalities early in processing
• Single model processes all modalities together
• Examples: Flamingo, GPT-4V

Late Fusion:

• Process each modality separately
• Combine representations later
• Easier to train, potentially less powerful

Encoder-based:

• Separate encoder per modality
• Shared decoder/language model
• Examples: LLaVA, BLIP-2

Modality Encoders

• Vision: CLIP, SigLIP, ViT
• Audio: Whisper encoder, CLAP
• Video: Frame sampling + vision encoder

Common Approaches

Vision-Language Models:

1. Encode image with vision model (e.g., CLIP)
2. Project visual tokens into language model space
3. LLM processes visual and text tokens together
4. Generate text output

Audio-Language:

1. Encode audio to spectrograms or embeddings
2. Transcribe with speech model or pass embeddings
3. Process with language model

Practical Capabilities

• Image understanding and description
• Document analysis (text + visual layout)
• Chart and graph interpretation
• Video summarization
• Visual question answering
• Audio transcription and analysis

Working with Multimodal APIs

• GPT-4V: Pass images via URL or base64
• Claude: Similar image input support
• Gemini: Native multimodal from the start
• Local: LLaVA, CogVLM

Limitations

• Higher latency than text-only
• More expensive (vision tokens add cost)
• Visual understanding still imperfect
• Hallucination risk with visual content
• Complex images challenge current models

Best Practices

• Resize images appropriately (too large = slow, too small = detail loss)
• Provide context about what to look for
• Validate visual analysis for critical applications
• Consider cost, vision adds significant token overhead