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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A learning track for your goal
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.
Foundation lesson progress
Loading progress…A correct answer is a start. Regular reviews help you remember. Take a break whenever you like.
Follow one neural calculation
Trace input, weighted sum and activation with fixed parameters.
A simple neural unit multiplies inputs by weights, adds a bias and applies an activation function. Networks combine units into layers. This example uses fixed weights and ReLU; changing inputs performs a forward calculation, not training.
A manually configured unit: weights w₁=2 and w₂=−1, bias b=1. Sliders change inputs, not parameters.
Weighted sum
z = 2×(1) − 2 + 1 = 1
ReLU output in this example
max(0, 1) = 1
ReLU maps negative values to 0 here. Networks combine many units and may use other activations. This output is a number, not a factual accuracy score.
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