Finding edges is cool — but how does an AI turn a picture into an actual answer like "that's a triangle"? Today you draw, and a real recognizer squints at your doodle, turns it into numbers, and makes its best guess — with confidence bars you can watch.
Draw with your mouse or finger. Big and clear works best!
I can't understand a giant messy picture. So first I shrink your drawing down to a tiny grid of numbers (bright = you drew there, dark = you didn't). Then I compare that grid to shapes I remember. The closest match wins!
AI isn't magic — it can be wrong, and a good AI knows when it's unsure. Go back up and draw a messy scribble, or a shape halfway between two others. If my top bar drops below 45%, you've earned the Uncertainty Detective badge.
Your mission scoreboard. Get the AI to correctly recognize each shape at least once. Draw them up in Toy 1 — this board fills in as you go.
The guesser above was a warm-up. This is ResNet-50 — a real image classifier that learned from over a million photos — running right on your device. Point your camera at a toy, a pet, a cup... or pick a photo. It gives its top guesses with confidence, just like the pros. (~25 MB one-time download; then it's cached.)
The compare-to-examples trick (kNN) is a real algorithm - but big products use trained networks:
✅ No training, easy. ⚠️ Slow with lots of data; weak on messy images.
Trained nets like ResNet, MobileNet, EfficientNet. ✅ Accurate and fast. ⚠️ Need data + training.
Huge pretrained models like CLIP classify almost anything - even with words. ✅ Super flexible. ⚠️ Big.
You saw how recognition really works: picture → shrink to numbers → compare to examples → pick the best guess, and how AI shows how sure it is.