If you already have a teacher account and student logins fully set up on the website Machine Learning for Kids, please skip to Training the AI.
Follow the steps below to train an AI model to recognise cartoon faces wearing glasses or sunglasses.
With the windows side-by-side, drag the images into the appropriate bucket: glasses, sunglasses, or noGlasses. You should end up with 13 or 14 images in each bucket.
Note, dragging does not work in Microsoft Edge (as of September 2019).
Placing face images from the training gallery into the correct label buckets.
Select Train new machine learning model. This may take from 10 to 15 minutes. The page will update when it's done.
IMPORTANT NOTE – AUTOMATIC DELETION OF AI MODELS: By default, after 24 hours Machine Learning for Kids automatically deletes AI models trained by students. As a teacher, you can increase this time to as much as 1.5 weeks (but remember that there is a limited number of models that a class can have at any time). Student training data is not deleted, so models can always be retrained.
Follow the steps below to test your AI model.
Code a program using the extra Machine Learning for Kids blocks and Images blocks. The screenshot below shows a sample program.
IMPORTANT NOTE – SAVING PROGRAMS: To accommodate each AI model, a custom Scratch 3 environment is launched when you select Make. This is different from the regular Scratch 3 environment accessed at scratch.mit.edu. Programs cannot be shared or uploaded across the two different environments. This means several things:
Image: Sample screenshot showing a very simple program within a Scratch 3 custom environment using Machine Learning for Kids blocks and Images blocks. Click the image to expand it in a new window.
Build a gallery of faces (cartoon or real) and try making your own AI model.
NOTE: For privacy reasons, it is recommended that photos do not include student faces or other personal identifiers.
Algorithmic bias creates errors that may lead to unfair or dangerous outcomes, for instance, for one or more groups of people, organisations, living things and the environment.
Algorithmic bias is often unintentional. It can arise in several ways. Some examples:
Watch this CNN video that further discusses some of the limitations of AI and examples of algorithmic bias.