Using modular synth to control Atem camera switching over WiFi using ESP8266

via Dangerous Prototypes

Sebastian writes:

I’ve been wanting to control my camera switching from my modular synth. So I made a setup where a low to high transition on a digital input on an ESP8266 module generates an OSC message on the WiFi network for the ATEM Mini Pro switcher to change cameras. Here, it’s triggered from the kick but it could be any clock, gate or trigger signal. Makes use of atemOSC.

More details on Little-Scale blog.

Check out the video after the break.

GoodBoy is a robot dog that runs on Arduino

via Arduino Blog

Daniel Hingston wanted to build a four-legged walking robot for several years, and with current coronavirus restrictions he finally got his chance. His 3D-printed robodog, dubbed “GoodBoy,” is reminiscent of a miniature version of Boston Dynamics’ Spot, which helped inspired the project. 

It’s extremely clean, with wiring integrated into the legs mid-print. Two micro servos per leg move it in a forward direction, controlled by an Arduino Uno.

Obstacle avoidance is provided by a pair of ultrasonic sensor “eyes,” allowing it to stop when something is in its path. An LDR sensor is also implemented, which when covered by its human minder commands it to present its paw for shaking.

Be sure to check out a short demo of GoodBoy below! 

Wireless Remote Weather Station with micro:bit

via SparkFun: Commerce Blog

Did you install your micro:climate kit in a remote, hard-to-reach location and need to pull weather data easily from the comfort of your own home? Or, maybe you want to add a timestamp. With the radio blocks, a second micro:bit and the gator:RTC, you can obtain these readings wirelessly with ease.

Check out the tutorial to add additional functionality to your micro:climate kit!

New!

Wireless Remote Weather Station with micro:bit

May 11, 2020

Monitor the weather without being exposed to it through wireless communication between two micro:bits using the radio blocks! This is useful if your weather station is installed in a location that is difficult to retrieve data from the OpenLog. We will also explore a few different ways to send and receive data.

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Learning AI at school — a peek into the black box

via Raspberry Pi

“In the near future, perhaps sooner than we think, virtually everyone will need a basic understanding of the technologies that underpin machine learning and artificial intelligence.” — from the 2018 Informatics Europe & EUACM report about machine learning

As the quote above highlights, AI and machine learning (ML) are increasingly affecting society and will continue to change the landscape of work and leisure — with a huge impact on young people in the early stages of their education.

But how are we preparing our young people for this future? What skills do they need, and how do we teach them these skills? This was the topic of last week’s online research seminar at the Raspberry Pi Foundation, with our guest speaker Juan David Rodríguez Garcia. Juan’s doctoral studies around AI in school complement his work at the Ministry of Education and Vocational Training in Spain.

Juan David Rodríguez Garcia

Juan’s LearningML tool for young people

Juan started his presentation by sharing numerous current examples of AI and machine learning, which young people can easily relate to and be excited to engage with, and which will bring up ethical questions that we need to be discussing with them.

Of course, it’s not enough for learners to be aware of AI applications. While machine learning is a complex field of study, we need to consider what aspects of it we can make accessible to young people to enable them to learn about the concepts, practices, and skills underlying it. During his talk Juan demonstrated a tool called LearningML, which he has developed as a practical introduction to AI for young people.

Screenshot of a demo of Juan David Rodríguez Garcia's LearningML tool

Juan demonstrates image recognition with his LearningML tool

LearningML takes inspiration from some of the other in-development tools around machine learning for children, such as Machine Learning for Kids, and it is available in one integrated platform. Juan gave an enticing demo of the tool, showing how to use visual image data (lots of pictures of Juan with hats, glasses on, etc.) to train and test a model. He then demonstrated how to use Scratch programming to also test the model and apply it to new data. The seminar audience was very positive about the LearningML, and of course we’d like it translated into English!

Juan’s talk generated many questions from the audience, from technical questions to the key question of the way we use the tool to introduce children to bias in AI. Seminar participants also highlighted opportunities to bring machine learning to other school subjects such as science.

AI in schools — what and how to teach

Machine learning demonstrates that computers can learn from data. This is just one of the five big ideas in AI that the AI4K12 group has identified for teaching AI in school in order to frame this broad domain:

  1. Perception: Computers perceive the world using sensors
  2. Representation & reasoning: Agents maintain models/representations of the world and use them for reasoning
  3. Learning: Computers can learn from data
  4. Natural interaction: Making agents interact comfortably with humans is a substantial challenge for AI developers
  5. Societal impact: AI applications can impact society in both positive and negative ways

One general concern I have is that in our teaching of computing in school (if we touch on AI at all), we may only focus on the fifth of the ‘big AI ideas’: the implications of AI for society. Being able to understand the ethical, economic, and societal implications of AI as this technology advances is indeed crucial. However, the principles and skills underpinning AI are also important, and how we introduce these at an age-appropriate level remains a significant question.

Illustration of AI, Image by Seanbatty from Pixabay

There are some great resources for developing a general understanding of AI principles, including unplugged activities from Computer Science For Fun. Yet there’s a large gap between understanding what AI is and has the potential to do, and actually developing the highly mathematical skills to program models. It’s not an easy issue to solve, but Juan’s tool goes a little way towards this. At the Raspberry Pi Foundation, we’re also developing resources to bridge this educational gap, including new online projects building on our existing machine learning projects, and an online course. Watch this space!

AI in the school curriculum and workforce

All in all, we seem to be a long way off introducing AI into the school curriculum. Looking around the world, in the USA, Hong Kong, and Australia there have been moves to introduce AI into K-12 education through pilot initiatives, and hopefully more will follow. In England, with a computing curriculum that was written in 2013, there is no requirement to teach any AI or machine learning, or even to focus much on data.

Let’s hope England doesn’t get left too far behind, as there is a massive AI skills shortage, with millions of workers needing to be retrained in the next few years. Moreover, a recent House of Lords report outlines that introducing all young people to this area of computing also has the potential to improve diversity in the workforce — something we should all be striving towards.

We look forward to hearing more from Juan and his colleagues as this important work continues.

Next up in our seminar series

If you missed the seminar, you can find Juan’s presentation slides and a recording of his talk on our seminars page.

In our next seminar on Tuesday 2 June at 17:00–18:00 BST / 12:00–13:00 EDT / 9:00–10:00 PDT / 18:00–19:00 CEST, we’ll welcome Dame Celia Hoyles, Professor of Mathematics Education at University College London. Celia will be sharing insights from her research into programming and mathematics. To join the seminar, simply sign up with your name and email address and we’ll email the link and instructions. If you attended Juan’s seminar, the link remains the same.

The post Learning AI at school — a peek into the black box appeared first on Raspberry Pi.

#FreePCB via Twitter to 2 random RTs

via Dangerous Prototypes

Every Tuesday we give away two coupons for the free PCB drawer via Twitter. This post was announced on Twitter, and in 24 hours we’ll send coupon codes to two random retweeters. Don’t forget there’s free PCBs three times a every week:

  • Hate Twitter and Facebook? Free PCB Sunday is the classic PCB giveaway. Catch it every Sunday, right here on the blog
  • Tweet-a-PCB Tuesday. Follow us and get boards in 144 characters or less
  • Facebook PCB Friday. Free PCBs will be your friend for the weekend

Some stuff:

  • Yes, we’ll mail it anywhere in the world!
  • Check out how we mail PCBs worldwide video.
  • We’ll contact you via Twitter with a coupon code for the PCB drawer.
  • Limit one PCB per address per month please.
  • Like everything else on this site, PCBs are offered without warranty.

We try to stagger free PCB posts so every time zone has a chance to participate, but the best way to see it first is to subscribe to the RSS feed, follow us on Twitter, or like us on Facebook.

This cycling game is controlled by a real bike

via Arduino Blog

What is one to do when stuck indoors due to bad weather or other circumstances, without the ability to ride your beloved bicycle? If you’re game designer Jelle Vermandere, you build your own cycling simulator as seen in the clip below. 

Vermandere not only created a computer simulation in Unity, but a custom Arduino Uno rig that allows him to use his actual bike as the controller.

The game features procedurally-generated maps, along with competitors using Vermandere’s own likeness scanned in as the model. When the racing begins, wheel speeds are sensed via a magnetic window sensor and steering is handled by a LEGO potentiometer rig. 

The game (without its unique interface) is playable now on your browser, while code is available on GitHub.