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micro:bit CreateAI is a free, web-based tool
that makes it easy for students to explore AI
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through movement and machine learning, and take
it into the real world with the BBC micro:bit.
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Add AI to your micro:bit learning
experience, by training a machine
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learning model with your own movement data
and use it in your micro:bit projects.
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You’ll need a computer with the Google
Chrome or Microsoft Edge web browsers
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to access micro:bit CreateAI.
Click on ‘Get started’ to begin.
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First you need to collect some training data.
You do this in the ‘data samples’ page.
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Click ‘Connect’ to connect a micro:bit
to CreateAI on your computer.
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This is the micro:bit you will
move, either holding it in your
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hand or attaching it to something that moves.
We call this the data collection micro:bit.
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If your computer has Bluetooth
enabled, you just need 1 micro:bit.
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If it doesn’t have Bluetooth, you can use
a second micro:bit to act as a radio link.
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micro:bit CreateAI will show you the best way
to connect the data collection micro:bit to your computer.
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Just follow the instructions on screen.
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As you move the data collection micro:bit,
you’ll see live movement data from its
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accelerometer sensor in a graph
at the bottom of your computer screen.
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Choose at least two different movements you want CreateAI to learn to recognise.
We call these movements ‘actions’.
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Waving and clapping are
good actions to start with.
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Name your first action.
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Click the ‘Record’ button to collect your first sample of data.
Each sample lasts 1 second.
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You can record one sample at
a time or multiple samples.
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Collect at least three
samples of your first action.
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And do the same for at least one other action.
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Can you see similarities between the graphs of the waving data?
And differences between waving and clapping?
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Next, click on ‘train model’.
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micro:bit CreateAI analyses your samples of data and creates a set of rules so
it can estimate what actions you’re making.
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These mathematical rules make up
the machine learning, or ML, model.
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Now you can test the ML model
in the ‘Testing model’ page.
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This shows which action the model
estimates that you’re making.
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The higher the percentage number,
the more confident the model is
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that you’re making a particular action.
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Wearing or holding the data collection micro:bit,
do each of your actions in turn.
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If the model is not accurately estimating which actions you are doing,
you may need to review your data and retrain the model.
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Click on ‘Edit data samples’ to collect more data samples,
or delete any samples that you think may not fit.
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You can also add more actions, for example to teach
the model what data for ‘being still’ looks like.
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Then you can retrain and test your ML model again.
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Once you’re happy that you’ve made an ML model
that is good at recognising your chosen actions,
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you can use your model in a MakeCode program
and put it on a micro:bit.
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You’ll already have seen some blocks in the Testing model page.
These are the machine learning, or ML, blocks
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you can use in your MakeCode programs.
These blocks make different icons appear
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on the micro:bit display when each
action is recognised by the ML model.
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Click on ‘Edit in MakeCode’ to open the
blocks in a special MakeCode editor.
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Click on ‘Download’ to transfer your
code and your ML model to a micro:bit.
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Follow the instructions on screen.
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Unplug the micro:bit from your computer, attach a battery pack and test it out.
You can take the micro:bit anywhere.
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Your ML model is now running on the micro:bit itself,
you no longer need a computer to make it work.
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There are more ML blocks you can use to create your own projects using
AI with MakeCode, and you can combine
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these with all of the other blocks too.
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You can also save your project - your data and code blocks - together in one file
so you can continue working on it later.
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You can do this in CreateAI by clicking
‘Save’ and giving your project a name.
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Or you can save your project from MakeCode.
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Just click on the 3 dots and
choose ‘download as file’.
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What will you create with machine learning
and MakeCode using micro:bit CreateAI?