IBM and mindSpark are running a series of free webinars for teachers about artificial intelligence.
This evening’s 90 minute webinar was about bringing AI into the classroom, and I helped contribute some of the content.
The session was very interactive, but there were some pre-prepared presentations in there.
I’ve got a recording of one of the segments below, in which I shared some of my experiences of introducing AI and machine learning in schools, and what I’ve found works well.
We had an interesting Kafka question from an Event Streams user. The answer isn’t immediately obvious unless you know a bit about Kafka internals, and after a little searching I couldn’t find an explanation online, so I thought I’d share the answer here (obviously anonymised and heavily simplified).
Retention is a Kafka feature to help you manage the amount of disk space your topics use.
It lets you specify how long you want Kafka to keep messages on a topic for. You can specify this by time (e.g. “I want messages on this topic to be preserved for at least X days”) or by disk usage (e.g. “I want at least the last X gb of messages on this topic to be preserved”).
After the retention time or disk threshold is exceeded, messages become eligible for being automatically deleted by Kafka.
They had created a topic with a retention time of 7 days.
They had assumed that this meant messages older than 7 days would be deleted.
When they looked at the messages on their topic, they could see some messages older than 7 days were there, and were surprised.
They thought this might mean retention wasn’t working.
I needed to quickly record a demo of what it looks like to get started with Event Streams yesterday.
It’s a little rough around the edges (it was only for an internal event, so the production values were essentially me-talking-at-my-laptop without a lot of planning or editing) but I thought I’d share it here in case I need to point anyone else at it.
In this post, I want to share a random thing I made in Scratch this week, and ask for suggestions of what I could do with it.
I get a lot of emails from teachers and coding groups asking for help with Scratch projects. They’re normally small or specific questions – asking for help figuring out a bug in a Scratch project or how to get something working.
But this week I got a more challenging email. It asked for a way to show a map in Scratch, and use a Scratch script to plot points on the map, given coordinates in latitude and longitude.
I agreed to give it a try. (Details for how to access it below.)
The first of the Grand Challenges identified in the Government’s Industrial Strategy is about Artificial Intelligence. One of the things that these challenges highlight is the UK’s need for skills in these key areas.
To that end, STEM Learning and the Department for Business, Energy and Industrial Strategy have created the “Grand Challenges – Our Futures” programme to improve young people’s knowledge of the STEM skills identified in the Government’s Industrial Strategy Grand Challenges.
Last week, STEM Learning announced a set of new free resources to support teaching in these key areas.
The Artificial Intelligence resources include three different packages aimed at students of different ages.
In this post, I want to explain how to get started creating machine learning applications using the data you have on Kafka topics.
I’ve written a sample app, with examples of how you can use Kafka topics as:
I’ll use this post to explain how it works, and how you can use it as the basis of writing your first ML pipeline using the data on your own Kafka topics.
This weekend was NASA Space Apps Challenge again – a weekend space-themed hackathon organised by NASA. It runs around the world, and this year IBM Hursley hosted one again.
I was in a small team with Faith. There were a variety of challenges to choose from and we chose Orbital Scrap Metal which was about educating the public about orbital debris, or space junk – explaining what it is, where it comes from, and the potential impact it has.
so far, so good… we’ve chosen our #SpaceApps challenge https://t.co/32trxaEQX4 and we’ve got a (very rough) first version (sort of) working! pic.twitter.com/H3cUOjzQAH
— Dale Lane (@dalelane) October 19, 2019
We created a game to help kids learn about space debris while playing. It’s fun, educational, and is all driven by real live data about space debris – each time you play, you interact with different real debris items.
I’ve written before about how to write a schema for your developers using Kafka. The examples I used before were all in Java, but someone asked me yesterday if I could share some Python equivalents.
The principles are described in the Event Streams documentation, but in short, your Kafka producers use Apache Avro to serialize the message data that you send, and identify the schema that you’ve used in the Kafka message header. In your Kafka consumers, you look at the headers of the messages that you receive to know which schema to retrieve, and use that to deserialize message data.
