Posts Tagged ‘ibmwatson’

How to transcribe and analyse a phone call in real-time

Saturday, July 16th, 2022

In this post, I want to share an example of how to stream phone call audio through IBM Watson Speech to Text and IBM Watson Natural Language Understanding services, and show some ideas of what you could use this for.

Let’s start with a demo

That’s what I want to show you how to build.

At a high-level, this is what you will have seen in that video:

1.
Faith made a phone call to a phone number managed by Twilio.

2.
Twilio routed the phone call to me, and I answered the call.

We then started talking to each other. And while we were doing this:

3.
Twilio streamed a copy of the audio from the phone call to a demo Node.js app

4.
The Node.js app sent audio to the Watson Speech to Text service for transcribing.

5.
Watson Speech to Text asynchronously sent transcriptions to the Node.js app as soon as they were available.

6.
The app then submitted the transcription text to Watson Natural Language Understanding for analysis.

7.
All of this – the transcriptions and analyses – were displayed on the demo web page.

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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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How I ended up making MachineLearningForKids

Sunday, October 29th, 2017

I write a lot about what I’m doing with machine learning for kids, but in this post, I want to share a little about how I ended up doing it and why.

I tend to write about *what* I’ve done. I rarely write how things happened though, or what made me do them. I just assume that people would be less interested in that.

But, if I think about what I find interesting, it tends to be the backstory to projects. To use Nick as an example, I’ve seen him give loads of talks about Node-RED. And I’ve enjoyed the ones where he talks about how Node-RED happened more than where he gives demos of what Node-RED is.

Inspired by that, I thought I should at least try to capture a few breadcrumbs for how I ended up where I am now with machinelearningforkids.co.uk.

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Machine Learning for Kids event at Hursley

Wednesday, August 30th, 2017

On Tuesday, a couple of dozen children (aged 8-14) spent the afternoon at Hursley so I could give them an intro to machine learning using some of the activities I’ve written for machinelearningforkids.co.uk.

I think it went pretty well, so I thought it’d be good to share what we did.


This was what the room looked like before the kids arrived… with just my two kids helping me set up. It all got a lot busier after this!

The general approach was letting them all work at computers, guided by a worksheet to build something that illustrated an aspect of machine learning. And then following this with a group discussion to draw out what they observed and what it meant.

We did this all together for the first couple of activities. Because of the large age range in the group, after this I let them split up and tackle different activities at different speeds, and followed this up by discussing their projects with them in smaller groups.

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MachineLearningForKids.co.uk

Wednesday, August 2nd, 2017

I’d like to introduce “Machine Learning for Kids“: a tool to help school children learn about machine learning by making things with it.

The video above is a walkthrough of the tool and examples of how I’ve been using it. The rest of this post is a transcript for the video.

machinelearningforkids.co.uk is a simple tool for training a variety of types of machine learning model, and an environment for creating games and other interactive projects that use them.

This is done by extending Scratch: a visual programming environment created to teach coding to kids, that is widely used in schools and other educational organisations like Code Club and Girls Who Code.

It gives students a blank canvas without prescribing what they make. They’re free to use their imagination and creativity to find fun uses for the machine learning models that they train.

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