How do you implement multimodal AI systems that process text, images, and audio?
Multimodal AI processes and reasons across multiple data types simultaneously. Architecture: modality-specific encoders (text encoder, image encoder, audio encoder) produce embeddings in a shared representation space, fusion module combines information across modalities, and task-specific heads produce outputs. Models: GPT-4o (text + image + audio, API-based), Claude 3.5 (text + image), Gemini 1.5 (text + image + audio + video), LLaVA (open-source text + image), and Whisper + LLM (audio to text pipeline). Implementation patterns: (1) Early fusion: concatenate multimodal inputs before processing (send image + text to a multimodal model together). (2) Late fusion: process each modality independently, then combine results (OCR text independently, classify image independently, merge findings). (3) Cross-attention: modality-specific encoders with cross-attention layers that allow each modality to attend to the other. Use cases: document understanding (combine text extraction with layout analysis and image recognition for complex documents), visual question answering (answer questions about images or videos), meeting analysis (combine audio transcription with screen share content analysis), product analysis (process product images alongside text descriptions for quality assessment), and accessibility (describe images for visually impaired users, transcribe audio for hearing impaired). Implementation: for API-based multimodal AI, send base64-encoded images alongside text prompts; for local deployment, use models like LLaVA with llama.cpp for text+image, and Whisper + local LLM pipeline for audio+text. Challenges: modality alignment (ensuring representations from different modalities are comparable), handling missing modalities gracefully (system should work with partial input), and increased computational requirements. Evaluation: measure performance per modality and combined, test with missing modalities, and benchmark against single-modality baselines.