At the heart of many AI systems is something called a neural network. And here’s the exciting part: you don’t need a supercomputer to explore one.
👉 In this challenge, you’ll build a working neural network using micro:bits—and see how AI works from the inside.
🔍 What Is a Neural Network?
A neural network is a system made of connected “neurons” that pass information to each other.
It’s usually organised into layers:
- Input layer → receives data
- Hidden layer → processes information
- Output layer → produces a result
One important detail: the input layer doesn’t actually process anything—it just passes signals forward.
If you want a deeper explanation, this post breaks it down clearly:
👉 https://robotsandphysicalcomputing.blogspot.com/2021/02/explaining-tinkercad-microbit-neural.html
⚡ Make It Physical with micro:bits
Instead of just talking about neural networks, we can build one.
In this project:
- Each micro:bit acts as a neuron
- Switches act as inputs (on/off signals)
- micro:bits communicate to pass signals forward
This turns an abstract AI idea into something you can see, touch, and debug.
To understand how a single neuron works using a micro:bit, start here:
👉 https://robotsandphysicalcomputing.blogspot.com/2021/01/tinkercad-and-microbit-to-make-neuron.html
🧩 The Challenge: Build a Neural Network That Thinks
Now for the real challenge—connecting multiple micro:bits to create a simple neural network.
This network solves a classic problem in computing called XOR.
What is XOR?
It’s a rule:
- TRUE if one input is ON
- FALSE if both are the same
| Input A | Input B | Output |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 0 | 1 |
| 0 | 1 | 1 |
| 1 | 1 | 0 |
This is surprisingly tricky—it can’t be solved with just one neuron.
👉 That’s why we need a network.
Follow this build to create your own:
👉 https://robotsandphysicalcomputing.blogspot.com/2021/02/making-neural-network-in-tinkercad-from.html
⚙️ How the Network “Learns”
Your network works using three key ideas:
- Weights (w1, w2) → how strongly inputs affect the neuron
- Bias (w0) → a threshold that shifts decisions
- Activation → deciding whether the neuron outputs TRUE or FALSE
By changing weights and bias, you change how the network behaves.
👉 Try this:
- Adjust a weight
- Test different inputs
- Watch the output change
That’s essentially how real AI systems are trained—just on a much bigger scale.
🤖 Why This Matters
What you’ve built is a physical model of AI decision-making.
Real neural networks:
- Recognise speech
- Detect objects in images
- Recommend content
They all rely on the same principles:
inputs → weighted decisions → outputs
You’ve just recreated that process using simple hardware.
🔧 Now It’s Your Turn to Tinker
Don’t stop at just making it work—start experimenting:
- What happens if you change the weights?
- Can you break the network?
- Can you redesign it to solve a different problem?
This is where real learning happens—not just following instructions, but playing with the system.
🚀 Your Challenge
You’ve just built a neural network.
Now take it further.
👉 Can you improve it?
👉 Can you make it more reliable?
👉 Can you explain how it works to someone else?
AI isn’t just something you use—it’s something you can build, explore, and question.
💬 Share what you create in the comments—and tell us what you changed, improved, or discovered.
Because the best way to understand AI… is to make it yourself.
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