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.
This video starts with one student’s training data from their Pac-Man project. They played a simplified version of Pac-Man in Scratch.
They set up the game in Scratch so that every time they pressed an arrow key (‘left’, ‘right’, ‘up’, or ‘down’) as well as moving their Pac-Man character, it put the x,y coordinates for Pac-Man and the Ghost into the training bucket for that direction.
For example, when Pac-Man was at x=3,y=4 and the Ghost was at x=5,y=5 – they went right. That became a training example for when it’s good to go right. and so on.
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.
If students are given the time and freedom to create their own machine learning models, rather than being given an existing model to use, they can learn even more.
A major part of the Machine Learning for Kids site is a child-friendly training tool that can be used to create a wide range of machine learning models.
For example, students can make their own simple chatbots, by training a text classifier to recognise frequently asked questions. They can choose their own subject for what the chatbot can answer questions about. In the video shown here, the student chose to make a project about the Moon.
They have to guess what questions someone might ask about their subject. In the video shown, you can see the student thought someone might ask where the Moon is, how big it is, how cold it is on the Moon, or what it’s made of.
For each of those questions, they came up with a few examples of how someone might ask that question.
They used those examples to train their own custom machine learning model, unique to their project.
Then they scripted the responses that their chatbot should give when it gets a question that it has learned to recognise.
I’ve run this project with school classes dozens of times, and it is different every time, with each class bringing their own creativity and imagination to the chatbot.
I’ve helped history classes make chatbot Vikings, chatbot Romans, and chatbot Ancient Greeks – trained to answer about what it was like to live in their times, what they ate, what they wore, and so on.
I’ve helped English classes have created chatbot Shakespeares that they trained to answer questions about his life and some of his most famous plays.
I’ve helped school clubs create local chatbot guides about their own school or their own town, trained to answer questions about their local area.
By going through the process for themselves, they learn the workflow of a machine learning project – a workflow that is similar to real-world projects: predict what users might do; collect examples of how the user would do that; use those examples to train a machine learning model to recognise that; and script what the system should do in response when it recognises something.
Going through the process of creating a machine learning project for themselves gives students an insight into how these systems are created in the real world.
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.
I’ve added a pitch extraction machine learning model to Machine Learning for Kids today. In this post, I want to describe the model a little, and suggest a few ways that students could use it.
The new model is a pitch estimation model. Given some audio as input, you can use it to recognize the dominant pitch in sung audio (even if there is background music and noise).
It was quite a long session, as we wanted it to be a hands-on practical CPD (Continuing Professional Development) workshop rather than just me giving a short talk. In the 90-minute workshop, we made two separate AI projects, which was a chance to see and contrast a few different approaches.
I started with a couple of definitions, then demonstrated a variety of projects that I’ve seen primary school students make, and finally walked people through a hands-on demo so they could try it out for themselves.
This week I ran