================================================================
AI FLUENCY — CHEAT SHEET
Multimodal AI
================================================================

Vision, speech, image generation, cross-modal embeddings, and multimodal architectures.
6 terms

----------------------------------------------------------------

* Automatic Speech Recognition (ASR)
  A model skill that converts spoken audio into a text transcript.
  e.g. Whisper transcribing a recorded meeting into text.

* CLIP (Contrastive Language–Image Pre-training)
  A model trained to align text and image embeddings in a shared vector space, enabling zero-shot image classification and guiding image generation.
  e.g. Matching the caption "a dog on a beach" to the right photo among thousands.

* Diffusion Model
  A generative model that produces images (or other data) by iteratively removing noise from a random starting point, guided by a conditioning signal like a text prompt.
  e.g. Stable Diffusion turning a text prompt into a photorealistic image.

* Multimodal Model
  An AI model that can process and/or generate more than one type of data, such as text, images, audio, or video.
  e.g. GPT-4o can accept text, images, and audio, and produce text or audio.

* Vision Transformer (ViT)
  A transformer architecture that treats an image as a sequence of patches and applies self-attention, instead of using convolutions.
  e.g. Splitting a photo into a grid of small patches before feeding it to the model.

* Vision-Language Model (VLM)
  A model that connects visual and linguistic representations to perform tasks like image captioning or visual question answering.
  e.g. Describing what's happening in a photo in a full sentence.

----------------------------------------------------------------
Tip: paste this file into your favorite AI assistant and ask to be tutored on it.