Why Can't Some AIs "See" Pictures?
You've probably run into this: you send an AI a photo and it replies "sorry, please describe it in words." You've also seen ones that can read an entire recipe out of a photo. Both are AI — what's the difference?
Because "speaking" and "seeing" are two independent skills. A language model natively understands only text; to make it see, you have to fit it with an extra pair of "eyes." The ones that have them are called multimodal models — the ones that don't can only apologize to your photo.
"Sorry, I can't view images. If you describe what's in the picture, I'll be happy to help analyze it."
"An orange cat is lounging on a bay window in the sun, right front paw resting on a ball of yarn. Judging by the physique… this one might need to watch its diet 😄"
Step 1: chop the image up
The "eyes" (a vision encoder) slice the image into many small squares, translating each into "words" the model understands — a picture becomes a special kind of "text."
Step 2: continue it like a sentence
Once translated, an image is no different from a passage of text to the model, and it "finishes the sentence" as usual. So under the hood, image understanding is still that same sentence-finishing machine.
Why not fit eyes on everything?
Fitting eyes means retraining on massive amounts of paired image-and-text data — expensive, and the model gets bigger and pricier. Many use cases never need vision, and text-only models are faster and cheaper.
✅ What this page wants to share with you
- Speaking ≠ seeing: vision requires extra "eyes" (a vision encoder)
- How the eyes work: chop the image up, translate it into "special text," then finish the sentence as usual
- No eyes ≠ outdated: faster and cheaper, and plenty for many use cases
- Seeing and drawing are two different things: an all-in-one app is really several models dividing the work