Proposal: Multimodal AI & Representation Learning module

[mszsorondo]

Proposal: module — Multimodal AI & Representation Learning

Motivation

The site's progression currently goes prompting → RAG → agents → computer vision. Several threads running through those modules — embeddings in RAG, CLIP-style alignment in open-vocab detection, text conditioning in Stable Diffusion, ViT patches as tokens — don't yet have a home that ties them together. A dedicated module on multimodal systems and representation learning would:

• unify those threads under one mental model ("every modality → vector → shared space")
• extend naturally to audio, time series, tabular, and graph data, which the site doesn't currently cover
• stay applied and AI-engineer-focused rather than becoming a self-supervised-learning survey

Proposed structure

AI-engineer-focused throughout. Each page anchored on a concrete pattern an engineer would actually ship:

1. Overview
2. Embeddings as a universal interface — one pattern: every modality → vector → shared space for search/classify/rank/cluster. Provider landscape (OpenAI, Cohere, Voyage, Jina, Nomic, Vertex, SigLIP, OSS CLIP) and how to pick.
3. Text + image embeddings in practice — CLIP/SigLIP hands-on: text→image search, reverse search, zero-shot classification via text probes, dedup. When to train a small projection head vs leave embeddings frozen.
4. Multimodal RAG & fusion patterns — extending the RAG module for corpora with figures, slide decks, screenshots. Caption-and-embed vs native multimodal embeddings vs hybrid; early / late / LLM-as-fusion when combining retrieved chunks across modalities.
5. Vision-language models as building blocks — GPT-4o / Claude / Gemini vision for structured extraction, OCR replacement, chart and document parsing. Cost/latency/quality trade-offs, when a small specialized model beats a general VLM.
6. Audio & speech in the stack — Whisper, diarization, TTS, CLAP embeddings. Transcribe → embed → search across call recordings; voice-first agents.
7. Multimodal agents — extending the agents module: tools that return images/audio, MCP with binary payloads, screenshot → action loops, vision-enabled browser automation.
8. Beyond text/image/audio — time series, tabular, graphs, code-as-modality; embedding APIs for structured data; when a dedicated encoder still beats dropping everything into an LLM's context.
9. Hands-on exercise — end-to-end: multimodal search + agent indexing a mixed corpus (docs with figures, call recordings, a metrics table) and answering questions by retrieving and fusing across modalities.
10. Recap & resources

Open design questions

• Provider posture — multi-provider (matching the rest of the site) or pick one as the spine?
• Page 8 scope — keep broad (time series + tabular + graphs + code) or drop code-as-modality to go deeper on the other three?
• Depth on self-supervised pretraining (contrastive / masked / DINO / MAE) — skim as background or dedicated page? Leaning toward skim, since the module is applied.

Happy to draft a detailed page-by-page outline for review before writing content. Would be a natural follow-up to the Computer Vision module proposed in a separate PR.