Posts Tagged ‘mlforkids-tech’

Machine Learning for Kids with EduBlocks

Saturday, July 8th, 2023

Students can now create Machine Learning for Kids projects using EduBlocks – letting them create machine learning Python projects in the browser by dragging and dropping blocks on a canvas.

This is all thanks to a fantastic new contribution from Joshua Lowe.

Here’s a quick run-through to show what this makes possible.

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What children can learn about artificial intelligence

Sunday, May 21st, 2023

One of the conference presentations I gave last year was a talk at Heapcon, sharing some stories of AI/ML lessons I’ve run in schools. The focus of the talk was how I’ve seen children understand and react to machine learning technologies.

I’ve since expanded the ideas in this talk into a mini-book at MachineLearningForKids.co.uk/stories but here is a recording of where some of these stories started.

Spotify extension for Scratch

Saturday, December 17th, 2022

In this post, I want to share a new Scratch extension that I made this week, explain what it does, and suggest a few ideas for the sorts of ways that it could be used.

Overview

The extension makes some of the data from the Spotify Audio Features API available as blocks in Scratch.

It means you can get numeric values representing different characteristics of songs, directly into a Scratch project.

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Geo-steering with IBM Code Engine and Cloud Internet Services

Saturday, September 3rd, 2022

In this post, I want to share a small tip from how I run Machine Learning for Kids: how I run instances of the site in different regions, and use geo-steering so that users are directed to the instance of the site nearest to them.

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Using pitch estimation to play with music in Scratch

Tuesday, October 5th, 2021

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.

Background

I started adding pretrained machine learning models to Machine Learning for Kids last year. Although my main focus is still allowing students to create their own machine learning models and make things with them, there are some fun projects that can be made using models that are too complex for students to train by themselves.

imagenet (that I added last Christmas), and the question-answering model (that I added in April) are both good examples of that!

I hope this one will be similarly welcomed!

SPICE

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).

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Visualizing TensorFlow image classifier behaviour

Saturday, July 10th, 2021

How to use Scratch to create a visualization that explains what parts of an image a TensorFlow image classifier finds the most significant.

An image classifier recognizes this image as an image of The Doctor.


prediction: The Doctor
confidence: 99.97%

Why? What parts of the image did the classifier recognize as indicating that this is the Doctor?

How could we tell?

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I wrote a book

Sunday, January 24th, 2021

It’s called “Machine Learning for Kids: A Project-Based Introduction to Artificial Intelligence”.

It’s a hands-on, application-based introduction to machine learning and artificial intelligence that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language.

Since starting the Machine Learning for Kids site, I’ve written project worksheets to inspire students and teachers what can be built using the tool. By making them freely available as Creative Commons-licensed MS Word docs, they’ve been a jumping off point to help teachers and code-club leaders to create their own lessons and activities.

As I’ve written the worksheets with schools and code clubs in mind, that introduced constraints.

Each worksheet is self-contained – many schools will only have time in their timetable/curriculum for one, or maybe two, AI projects, so a lot of the projects retread some of the same basics. None of them build on, or even refer to, any of the other worksheets. They also need to be short activities, so that they can be completed within a school lesson.

Writing a book version of Machine Learning for Kids was a chance to do something for a different audience: this time aimed at a child at home with their parents.

This means I didn’t have the same constraints as the worksheets on the site. It’s still based on explaining machine learning in a hands-on way through making projects in Scratch. But there’s a flow between the projects in the book. They’re in an intentional order, and there is a continuation between them. Each project builds upon the projects that came before it.

Some of the projects take a bit longer as they don’t need to be done in one sitting. I have more time and space to explain the ideas and to give the real-world context for each project. As each project doesn’t need to work as an introduction, it means the later chapters can get into more advanced topics that none of the project worksheets on the site go near, like accuracy, recall, and confidence matrices.

It’s been a lot of work. A lot more than I expected. Over two years of work. And not just by me: I had no idea how many people would be involved in making the book into a real thing. I’ve not really worked with editors before, and it has been a fascinating experience. They made my rambling gibbering so so much better that I’m almost embarrassed that only my name is on the cover. There’s no way the finished thing would be nearly as good without their work.

There were a few points where I wondered if it’d ever actually see the light of day – but it’s finally available. (Well, the e-book is available now, but the printed version is still a couple of weeks away).

I hope people find it useful! I am proud of it. I’m particularly proud of the Foreword, which I didn’t even write. It was very generously written by Grady Booch, and it’s the perfect inspirational start to what I wanted the book to be.

It’s very strange to see something I’ve written in online bookshops. It’s in Amazon, Waterstones, and WHSmith. That feels a bit weird. I hope that at some point I’ll get to see a printed copy in a real bookshop, but I suspect that won’t be any time soon!

Looking back at Machine Learning for Kids in 2020

Saturday, January 2nd, 2021

A review of what I did on Machine Learning for Kids in 2020.

Happy New Year!

At this time of year, it’s traditional to get a bit reflective, so I thought I’d look over the work I did on ML for Kids in 2020.
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