Friends, welcome back to Arc Tutorials. In this episode of my AI masterclass series, I’m making one more core concept super simple: what are multimodal models in AI. It’s not a spelling mistake—multimodal models are basically models that can understand more than just text. Along with language, they can take images, audio, and sometimes video as inputs, create a unified understanding, and then generate outputs like text or images based on that combined context. I explain it using my “digital intern” analogy. A normal intern can only read text, but a multimodal intern can read, look at images, and listen to audio—so now your AI has “eyes and ears,” not just a brain. For example, if you upload a UI screenshot, a multimodal model can analyze the layout, extract data, suggest improvements, and even help debug visually. Tools like ChatGPT, Google Gemini, and Copilot are popular examples of multimodal systems. Final takeaway: multimodal AI means brain + eyes + ears—an intern that can see and hear, not just read.