Map the AI · Daily learning
One small lesson. One step forward.
Learn how AI handles text and generates answers through a short experiment and a question. Open the linked tutorial to explore further.
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AI Literacy
Understand what AI is, how it works, and where it fails — without writing a line of code.
The full track uses the existing knowledge graph. Short-lesson mastery currently covers the foundation units below.
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Adjust a weight and a bias
Observe how weight and bias change a linear prediction.
In the rule y=w×x+b, weight w changes the effect of x, while bias b shifts every output by the same amount. Training can adjust parameters to reduce an error measure. This small experiment lets you adjust them manually and compare predictions with target values.
A handcrafted linear example: prediction = w×x+b. Inputs stay fixed while you adjust weight w and bias b. No automatic training algorithm runs here.
x = 0
Target 1
Prediction 0
x = 1
Target 3
Prediction 1
x = 2
Target 5
Prediction 2
Mean squared error: 4.67
Square each prediction-minus-target error, then average. Smaller means a better fit to these three training points, not guaranteed performance on new data. Try w=2, b=1, then change only the bias.
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