Multimodal AI

For years, AI systems were single-modality. Text-based LLMs processed words. Computer vision models processed images. Audio models processed sound. You needed specialized pipelines: extract text from an image, send to a text model, process separately. Multimodal changes that. A single model processes text and images together, understands relationships, reasons across modalities.

GPT-4 Vision can see images and read text. Claude 3.5 Sonnet can process images. Gemini Ultra processes text, images, audio, and video. These multimodal models fundamentally change what's possible.

The power is in cross-modal reasoning. Show the model a chart and ask about data trends. The model sees the visual structure and numbers, reasons about both. Show a diagram of a system and ask how data flows. The model reads labels and understands visual relationships. Present a screenshot and ask to fix a UI bug. The model sees the visual problem and provides code changes. This cross-modal understanding is powerful.

Multimodal processing works by embedding each modality into a shared representation space. Text becomes embeddings. Images are processed by a vision model, producing embeddings. Audio is converted to embeddings. These embeddings live in the same space, allowing the model to reason across them. The mechanism is similar to how text-only models embed words and reason over them, just extended to multiple modalities.

The limitations are modality-specific. Image understanding has failure modes (adversarial examples, things in the background the model fixates on). Audio understanding struggles with accents and background noise. Video is computationally expensive. The combination is only as good as the weakest component modality.

Applications explode with multimodality. Document analysis: process PDFs with text, images, tables, scan handwritten notes, extract information. Accessibility: describe images to people who are blind, allowing equal access. Code review: show code changes with context screenshots, enable more intelligent review. Quality assurance: show product photos and defects, enable visual inspection automation. Each application couples modalities naturally.

Multimodal RAG is emerging. Instead of storing just text embeddings, store multimodal embeddings covering text, images, tables. When you query with text or image, retrieve based on multimodal similarity. A query with a diagram retrieves documents with similar diagrams and text describing related concepts.

Latency increases with multimodality. Processing an image takes additional computation. Processing multiple modalities multiplicatively increases compute. Multimodal inference is noticeably slower than text-only. For latency-sensitive applications, this matters.

Cost also increases. Multimodal models cost more to run because of additional processing. Vision encoding adds computation. Longer context required to store image information increases token usage. Multimodal inference is more expensive than text-only.

Training multimodal models is complex. Aligning different modalities in shared representation space requires massive paired datasets (images with descriptions, audio with transcriptions). The training process is more complicated than single-modality training.

The frontier is deeper multimodal reasoning. Current systems understand cross-modal relationships at the surface level. Future systems will do deeper reasoning: understanding causal relationships across modalities, reasoning about temporal changes shown in video, integrating sensory information in human-like ways.

Embodied AI (robots with sensors and actuators) is a frontier application: integrate vision, audio, touch, proprioception into unified reasoning to control robotic systems. This is where multimodal AI becomes truly embodied.