Train your robot
Agent, you're training an AI model. Choose examples that teach it what a cat truly looks like. Pick well! Wrong data makes the platform unstable.
START
QUESTION 01 OF 05
Start Simple. Choose the example that actually teaches the model what a cat looks like.
QUESTION 01 OF 05
RIGHT!
Good start. You picked a clear example of a cat. The model begins learning.
NEXT QUESTION
OH NO!
This has no cat-like patterns. Noise weakens the model.
TRY AGAIN
QUESTION 02 OF 05
think wider. select the example that teaches the model more than it already knows.
QUESTION 02 OF 05
RIGHT!
Great choice. Variety helps the model understand cats from different viewpoints.
NEXT QUESTION
OH NO!
This pattern isn't real. Models can learn the wrong texture if the example is misleading.
TRY AGAIN
QUESTION 03 OF 05
some choices look close... but only one really helps. Spot the true cat pattern.
QUESTION 03 OF 05
RIGHT!
Nice. Mixing in cats of different colors and shapes helps the model understand that "cat" comes in many forms, not just one.
NEXT QUESTION
OH NO!
The shapes might be similar, but it's still wrong. The model starts mixing categories.
TRY AGAIN
QUESTION 04 OF 05
Every image shows a cat... but one offers a much cleaner signal than the rest.
QUESTION 04 OF 05
RIGHT!
Good call, Agent. You selected the clearest cat image, giving the model a solid pattern to lock onto.
NEXT QUESTION
OH NO!
You chose a real cat, but the details are hard for the model to pick up. Choose a clearer pattern.
TRY AGAIN
QUESTION 05 OF 05
edge cases matter. select the one that teaches the model to handle the unexpected.
QUESTION 05 OF 05
RIGHT!
Excellent. Showing edge cases strengthens the model and reduces bias.
NEXT
OH NO!
Similar texture, wrong species. This sample adds no real signal, only noise the model can’t use.
TRY AGAIN
RESULTS
CONGRATULATIONS!
Great work. Now that you've learned how the platform reacts to your choices, it's time to explore what happens when the model meets Digital City's data.
Train your robot
San-Shan Huang
Created on November 28, 2025
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Transcript
Train your robot
Agent, you're training an AI model. Choose examples that teach it what a cat truly looks like. Pick well! Wrong data makes the platform unstable.
START
QUESTION 01 OF 05
Start Simple. Choose the example that actually teaches the model what a cat looks like.
QUESTION 01 OF 05
RIGHT!
Good start. You picked a clear example of a cat. The model begins learning.
NEXT QUESTION
OH NO!
This has no cat-like patterns. Noise weakens the model.
TRY AGAIN
QUESTION 02 OF 05
think wider. select the example that teaches the model more than it already knows.
QUESTION 02 OF 05
RIGHT!
Great choice. Variety helps the model understand cats from different viewpoints.
NEXT QUESTION
OH NO!
This pattern isn't real. Models can learn the wrong texture if the example is misleading.
TRY AGAIN
QUESTION 03 OF 05
some choices look close... but only one really helps. Spot the true cat pattern.
QUESTION 03 OF 05
RIGHT!
Nice. Mixing in cats of different colors and shapes helps the model understand that "cat" comes in many forms, not just one.
NEXT QUESTION
OH NO!
The shapes might be similar, but it's still wrong. The model starts mixing categories.
TRY AGAIN
QUESTION 04 OF 05
Every image shows a cat... but one offers a much cleaner signal than the rest.
QUESTION 04 OF 05
RIGHT!
Good call, Agent. You selected the clearest cat image, giving the model a solid pattern to lock onto.
NEXT QUESTION
OH NO!
You chose a real cat, but the details are hard for the model to pick up. Choose a clearer pattern.
TRY AGAIN
QUESTION 05 OF 05
edge cases matter. select the one that teaches the model to handle the unexpected.
QUESTION 05 OF 05
RIGHT!
Excellent. Showing edge cases strengthens the model and reduces bias.
NEXT
OH NO!
Similar texture, wrong species. This sample adds no real signal, only noise the model can’t use.
TRY AGAIN
RESULTS
CONGRATULATIONS!
Great work. Now that you've learned how the platform reacts to your choices, it's time to explore what happens when the model meets Digital City's data.