Archive for the ‘tech’ Category

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Machine learning hasn’t replaced the need to learn to code

Sunday, November 20th, 2022

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.

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Tech events are back

Saturday, November 12th, 2022

I’ve loved the return of (in-person) tech events and conferences this year. I’m not expecting to go to any more events in 2022, so I thought this would be a good time to look back over some of my highlights for this year.

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From bytes to objects: describing Kafka events

Saturday, October 23rd, 2021

The recording of the talk that Kate Stanley and I gave at Kafka Summit Americas is now available.

Events stored in Kafka are just bytes, this is one of the reasons Kafka is so flexible. But when developing a producer or consumer you want objects, not bytes. Documenting and defining events provides a common way to discuss and agree on an approach to using Kafka. It also informs developers how to consume events without needing access to the developers responsible for producing events.

In our talk, we introduced the most popular formats for documenting events that flow through Kafka, such as AsyncAPI, Avro, CloudEvents, JSON schemas, and Protobuf.

We discussed the differences between the approaches and how to decide on a documentation strategy. Alongside the formats, we also touched on the tooling available for the different approaches. Tools for testing and code generation can make a big difference to your day-to-day developer experience.

The talk was aimed at developers who maybe aren’t already documenting their Kafka events or who wanted to see other approaches.


watch the recording on the Kafka Summit website

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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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Building a Question Answering game in Scratch

Saturday, April 17th, 2021

I added a new project worksheet to Machine Learning for Kids today.

It has step-by-step instructions for how to make a quiz show game in Scratch that uses a machine learning model to understand questions on any topic the student chooses, and find the answer in Wikipedia pages.

It’s a fun little project, super simple to make, and works surprisingly well. It doesn’t get every question right, but it does a lot better than I expected.

I don’t normally write blog posts when I write new ML for Kids worksheets, but this one was a bit interesting.

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AsyncAPI plugin for Node-RED

Sunday, March 21st, 2021

screenshot

I’ve been tinkering with a new AsyncAPI plugin for Node-RED as a side project over the last couple of weeks. Time to share what I’ve got working so far.

Node-RED is an open-source visual programming tool. You assemble flows on a canvas from a palette of nodes, that you customize and then wire together. That makes it ideal for quick prototyping.

There are nodes for different types of servers and devices, which makes it great for quick integration projects. This includes nodes for sending and receiving messages using protocols like Kafka and MQTT.

Normally, this means having to choosing a node from the palette, dragging it onto the canvas, clicking into it, and customizing it: filling in the connection details for the broker and topic you want to use.

The idea of this plugin is to do all of that for you, if you have an AsyncAPI specification for your topics.

The plugin can generate and customize the nodes for you, based on your spec – making rapid prototyping based on the spec even quicker.

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Running TensorFlow models in Scratch

Thursday, November 19th, 2020

I gave a short presentation today to explain how you can use TensorFlow machine learning models in the student block-based coding platform, Scratch.

This post has the recording of my presentation, and I’ve put some notes (all the stuff I meant to say but forgot!) and screenshots below.


recording at https://youtu.be/qHKwtefn21w

I demonstrated three things:

  1. Using your own TensorFlow models in Scratch
  2. Using pretrained models in Scratch
  3. Creating TensorFlow models in Teachable Machine and using them in Scratch

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Explaining ML with neural networks

Tuesday, October 27th, 2020

I’m working on interactive visualisations for Machine Learning for Kids that explain more of the machine learning models that children create.

Machine Learning for Kids is a platform to teach children about artificial intelligence and machine learning, by giving them a simple tool for training machine learning models, and using that to make projects using tools like Scratch. I’ve described before how I’ve seen children learn a lot about machine learning principles by being able to play and experiment with it.

But I still want the site to do more to explain how the tech actually works. I’ve done this before for the decision tree classifiers that students train for numbers projects but with this new feature I’m trying to explain neural networks.

I’ve recorded a video run-through of what I’ve done so far. The screenshots below link to different sections of the video.

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