This post was written for MachineLearningForKids.co.uk/stories: a series of stories I wrote to describe student experiences of artificial intelligence and machine learning, that I’ve seen from time I spend volunteering in schools and code clubs.
I like to introduce students to building with machine learning by allowing them to play with pretrained models – a range of new blocks that can be added to the Scratch palette to represent a variety of powerful machine learning models.
For example, imagenet: a model that can recognise the object in a photo that you give it. It can recognise over a thousand different things.
With just a few Scratch blocks, students can start building projects that do remarkably powerful and impressive things.
Another example is the face detection model: a model that can identify different parts of a face (e.g. eyes, nose, ears, etc.) in a picture or webcam view.
With just a few Scratch blocks, students can make a fun face filter with animated sprites that follow their face.
These are very simple projects to build, but they offer a lot of opportunity for experimentation and creativity.
Learning about machine learning in a sandbox that students are already familiar with (rather than learn about AI/ML in a new AI-specific tool or platform) sends a clear message to students.
Students shouldn’t think that what they already learn about coding isn’t useful any more. They shouldn’t get the impression that learning to code is somehow less valuable because machine learning models will just magically do stuff for them in the future.
Instead, students see that machine learning adds new tools to their existing toolbox. They see that what they’ve been learning about coding is still important and valuable, and that machine learning expands the types of things they’re able to build.
Building with machine learning in an existing sandbox like Scratch makes it clear that ML isn’t separate to, or a replacement for, coding.
Tags: machine learning, scratch