IBM Shoebox was the world’s first speech-recognition system, created in 1961. It was a voice controlled calculator: you input a sum by speaking the numbers zero through nine and six command words, including “plus”, “minus”, and “total”.
To calculate 12 + 34 you could say “one two plus three four total” and it would respond with the answer.
You can see it being used by inventor William Dersch in this two-minute demo video.
Screen recording at youtu.be/cpCeaR9KTF8
MobileNet is a light-weight machine learning model for performing image classification.
In this Machine Learning for Kids project, students can try MobileNet for themselves using the familiar educational low-code programming language Scratch.
In this post, I want to share a Scratch extension that I’ve been working on today: enabling access to books data from the OpenLibrary API through new Scratch blocks.
Most of the work I do on Machine Learning for Kids involves adding machine learning models into Scratch. To enable students to create interesting projects, it also helps to make it easier to get external data into Scratch that they can use for training and classifying. A few examples of where I’ve done this in the past include creating Scratch blocks to access weather data, data from Spotify, and data from Wikipedia.
The new blocks I’ve worked on today use the OpenLibrary API to enable access to information about books.
Machine Learning for Kids is intended to be an open creative sandbox to let students invent their own AI-powered projects. But in order to enable that, I create more prescriptive project worksheets to inspire and show what is possible.
I’ve just written another worksheet based around regression models – a model type that I added support for in February.
This project is based on shooting basketballs.
Students are free to devise their own projects in Machine Learning for Kids, but I also write project worksheets to help inspire students and teachers.
This evening, I’ve written a new worksheet based around regression models. (I wrote about adding support for regression models to the site a couple months ago).
The premise for this project is sort of Space Invaders. Except with only a single Space Invader. And it’s a cute little bug.
The aim of the game is to shoot at the bug.
I’m on vacation for the Easter holidays this week. Apparently I don’t know how vacations work, so I’ve spent a lot of the last six days working on a major rewrite of a big chunk of Machine Learning for Kids. In this post, I want to describe what I’ve been doing and why.
In this post, I want to share a preview of a new feature I’m adding to Machine Learning for Kids, to ask for feedback and ideas for projects that it could be used to make.
I’ll start by contrasting this new feature with what I’ve done with Machine Learning for Kids before, then I’ll share screenshots of the new feature, and finally I’ve got a ten-minute video showing the sort of school lesson that I think it could be used for.
I added support for “local projects” (storing projects on your own computer) to Machine Learning for Kids this week. In this post, I want to give a little background.