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Links Justin Sends 4/25
How does a multimodal model work?
Justin Trugman
April 25, 2025
Happy Friday, everyone! This week’s been slow for major AI news, so instead of our usual wrap-up, let’s dive into multimodal models—what they are, how they work, where they’re used, and how to evaluate them.
What Is a Multi-Modal Model?
Think of today’s flagship multi-modal AI models as “digital polyglots.”
Rather than learning one language of data (just text or just images), multi-modal systems are trained on aligned combinations—pictures with captions, audio with transcripts, video with screenplays—so they can look, listen, read, and speak inside a single neural core.
This alignment lets the model answer questions like “Where is the plumbing leak in this photo, and how do I fix it?” in one seamless exchange.
(See the classic survey on multimodal machine learning for the five technical challenges these models must conquer.)
Before comparing model families, it helps to know why the differences matter: choosing the wrong type can leave you blind (literally) to the data your customers send.
How Multi-Modal Models Differ From LLMs, Diffusion Models, etc.
Model family | Super-power | Blind spot multi-modal models solve |
|---|---|---|
LLMs / SLMs | Elite at generating & analyzing text | Must convert every image, sound, or video into words first. |
Diffusion & GANs | Create or edit images from text | Cannot parse long documents or follow spoken, real-time instructions. |
Audio-first models | Crystal-clear speech recognition | Need extra modules to “see” what’s on screen or page. |
Multi-Modal models | Understand & generate across modalities in one parameter set | Heavier to train/serve; quality hinges on how well modalities are aligned. |
Grounded variants even point to the evidence—highlighting the exact region in an image that backs their answer (see this grounding paper for examples).
Knowing the technical distinction is useful, but the real question leaders ask is “Where does this pay off?”
Where Are Multi-Modal Models Used & What Can They Do?
AI Service Desks – Read customer screenshots, parse spoken frustration, and draft fixes in a single flow.
Design & Productivity Tools – Turn hand-drawn UI sketches or product photos into working code, copy, or CAD files.
Compliance & Audit – Flag safety violations in video and cite the precise frame & location for auditors.
Retail & E-commerce Search – “Show me this in red, under $50,” combining photo search with chat-style advice.
Agentic Assistants – Autonomous agents that watch your screen, interpret documents, and act on your behalf across apps.
If you’re considering adding a multi-modal engine to your stack, keep these practical tips in mind.
Tips for Evaluating Multi-Modal Models
Modal Coverage – Confirm the model ingests and outputs every modality you need (text, vision, audio, video, etc.).
Grounding & Explainability – Look for features that surface evidence (bounding boxes, audio snippets, cited frames).
Latency & Cost – Real-time voice-and-vision experiences demand sub-second responses; ask vendors for concrete numbers.
Data Lineage & Licensing – Ensure the aligned training data is properly licensed and privacy-compliant.
Robustness to Missing Signals – Evaluate how gracefully the model degrades if a webcam drops or audio is noisy.
Ecosystem Momentum – Prefer models backed by active tooling (agent frameworks, fine-tuning APIs, domain adapters).
A single-sense AI can answer questions; a multi-modal AI can solve problems by perceiving the world almost like we do.
As customers, regulators, and employees communicate through a mix of words, images, and sound, businesses that harness multi-modal intelligence will deliver support that sees, analytics that listen, and products that feel almost human in how they understand intent.
Have a great weekend!
-Justin
aka the guy with great AI links
Co-founder & Head of Technology @ BetterFutureLabs