While we’ve not had enough time to assess it for review yet, we’ve had a chance to play with one for a bit with a Raspberry Pi 5 and we’re very intrigued by it. CrowView has speakers, a webcam, a microphone, and an in-built battery so all you need to do is provide a Raspberry Pi to get it functioning like a true laptop. It’s very light too as it doesn’t have as much internal hardware as a real laptop.
There’s no extra software you need to add for that extra hardware either – the closest SD card we could find had been a fresh install and we were able to use the mouse and keyboard straight away to set it up.
At the time of writing this, the project just launched and already hit its funding goal, so that’s a good sign a lot of other folks are interested in it as well.
Look out for a full review in a future issue of The MagPi.
As a kid, I was always taking things apart to see how they worked. Most of those things even got put back together. Taking after my older brother, I started tinkering with electronics when I was a teen. Continuing to follow in my brother’s footsteps, I ended up with an undergraduate degree in computer engineering. I got back into electronics during the start of the maker movement when I first learned about the Arduino. I also got heavily involved with a local makerspace for several years that expanded my interest in the process of making, learning the tools, and collaborating with other people.
What is Riverside Raspberry Pi Meetup?
The meetup is an informal monthly meetup for bringing people together that are interested in electronics and embedded software development. But, despite the group name, it is not only for the Raspberry Pi platform. It is as much of a social group as it is a tech group. I try to have formal tech presentations every once in a while, but we more frequently just have open discussions about what everyone is working on, the problems they are facing with their projects, or just sharing the latest news about new maker tech. I do encourage members to contribute talks for the group whenever they can.
What kind of attendees do you get?
Many people who attend the meetup are mostly either new to the Raspberry Pi platform, or are just starting to learn about electronics. But we also have regulars who have been working with or tinkering with embedded platforms for years. Ages range from high schoolers to retirees. Most have at least a little bit of experience in some kind of programming, but not always. The membership is pretty diverse in terms of experience and goals, but we are very open to newcomers.
What are some of your favourite moments from the meetups?
Really, any time someone brings in a project to show off that they have been working on is my favourite part of belonging to the group. I don’t even care if it is someone’s first project where they just have a blinking LED on a breadboard. I want to hear about it and share in their excitement because I remember being there myself. My reason for having the group is to share what I know and to learn from others. I believe that inspiration is contagious.
Any future events planned?
We meet on the second Monday of every month in Riverside, CA, US. We have snacks. We also try to have a table at local events a few times a year and have members show off their projects, though that schedule varies. We collaborated with a few local school districts several years ago to put on a full Raspberry Pi Jam, and I’d like to do that again sometime as well.
About three weeks ago, a small team from the Raspberry Pi Foundation braved high temperatures and expensive coffees (and a scarcity of tea) to spend time with educators at the CSTA Annual Conference in Las Vegas.
With thousands of attendees from across the US and beyond participating in engaging workshops, thought-provoking talks, and visiting the fantastic expo hall, the CSTA conference was an excellent opportunity for us to connect with and learn from educators.
Meeting educators & sharing resources
Our hope for the conference week was to meet and learn from as many different educators as possible, and we weren’t disappointed. We spoke with a wide variety of teachers, school administrators, and thought leaders about the progress, successes, and challenges of delivering successful computer science (CS) programs in the US (more on this soon). We connected and reconnected with so many educators at our stand, gave away loads of stickers… and we even gave away a Raspberry Pi Pico to one lucky winner each day.
The team with one of the winners of a Raspberry Pi Pico
As well as learning from hundreds of educators throughout the week, we shared some of the ways in which the Foundation supports teachers to deliver effective CS education. Our team was on hand to answer questions about our wide range of free learning materials and programs to support educators and young people alike. We focused on sharing our projects site and all of the ways educators can use the site’s unique projects pathways in their classrooms. And of course we talked to educators about Code Club. It was awesome to hear from club leaders about the work their students accomplished, and many educators were eager to start a new club at their schools!
We gave a copy of the second Big Book to all conference attendees.
Back in 2022 at the last in-person CSTA conference, we had donated a copy of our first special edition of Hello World magazine, The Big Book of Computing Pedagogy, for every attendee. This time around, we donated copies of our follow-up special edition, The Big Book of Computing Content. Where the first Big Book focuses on how to teach computing, the second Big Book delves deep into what we teach as the subject of computing, laying it out in 11 content strands.
Our talks about teaching (with) AI
One of the things that makes CSTA conferences so special is the fantastic range of talks, workshops, and other sessions running at and around the conference. We took the opportunity to share some of our work in flash talks and two full-length sessions.
One of the sessions was led by one of our Senior Learning Managers, Ben Garside, who gave a talk to a packed room on what we’ve learned from developing AI education resources for Experience AI. Ben shared insights we’ve gathered over the last two years and talked about the design principles behind the Experience AI resources.
Ben discussed AI education with attendees.
Being in the room for Ben’s talk, I was struck by two key takeaways:
The issue of anthropomorphism, that is, projecting human-like characteristics onto artificial intelligence systems and other machines. This presents several risks and obstacles for young people trying to understand AI technology. In our teaching, we need to take care to avoid anthropomorphizing AI systems, and to help young people shift false conceptions they might bring into the classroom.
Teaching about AI requires fostering a shift in thinking. When we teach traditional programming, we show learners that this is a rules-based, deterministic approach; meanwhile, AI systems based on machine learning are driven by data and statistical patterns. These two approaches and their outcomes are distinct (but often combined), and we need to help learners develop their understanding of the significant differences.
Our second session was led by Diane Dowling, another Senior Learning Manager at the Foundation. She shared some of the development work behind Ada Computer Science, our free platform providing educators and learners with a vast set of questions and content to help understand CS.
Diane presented our trial with using LLM-based automated feedback.
Recently, we’ve been experimenting with the use of a large language model (LLM) on Ada to provide assessment feedback on long-form questions. This led to a great conversation between Diane and the audience about the practicalities, risks, and implications of such feature.
More on what we learned from CSTA coming soon
We had a fantastic time with the educators in Vegas and are grateful to CSTA and their sponsors for the opportunity to meet and learn from so many different people. We’ll be sharing some of what we learned from the educators we spoke to in a future blog post, so watch this space.
The touchscreen is surrounded by quite a large bezel which forms part of the protective case. With a lot of metal parts, it’s a pretty hefty unit that feels really solid – and heavy, at 1.67kg. Six mount points (two top and bottom, one either side) enable it to be mounted using the supplied brackets and bolts.
By default, the case has an IP (ingress protection) rating of 20, for protection against solid objects up to 12mm, but you can pay a little extra (€25) for IP65, which offers full protection against dust and low-pressure jets of water from all directions. Both versions have a wide operating temperature range of 0°C to 50°C, with humidity of 0% to 90%. The unit also has a shock resistance of 80m/s2 in the X, Y, and Z directions.
Powering it `up
After connecting a 12V (2.5A) or 24V (1.25A) DC power supply to the relevant screw terminals on the right-hand side (make sure the polarity is correct!), the TouchBerry comes to life, booting into Raspberry Pi OS – there’s no physical power button.
The pre-installed edition of Raspberry Pi OS is the older Buster version, although you could always re-flash or replace the microSD card by opening up the back of the case, a procedure which also enables you to move some jumper switches to reconfigure settings such as voltage levels for the I/O. The system runs fine, although we did experience occasional lag when trying to select items on the desktop.
You can connect to a Wi-Fi network from the desktop or command line (although we needed to manually add a network gateway to gain internet access), or plug in an Ethernet cable for a wired connection. The latter can also be used to network the TouchBerry Pi with a dedicated PLC to add extra I/O and exchange data. Naturally, you can install extra Raspberry Pi software in the usual way.
The only visual clue to the presence of Raspberry Pi 4 is provided by a cutouts for the latter’s USB and Ethernet ports, at the base of the unit. So you can plug in a mouse and keyboard if you want. Alternatively, you can install an on-screen keyboard.
Ins and outs
As mentioned, there are two sets of screw terminal connections on either side of the device for I/O. The right-hand side covers all of the main communications protocols: I2C, SPI, RS485 (half or full duplex), and RS232/TTL.
On the left side is the second set of I/Os, which requires an additional power supply to be connected to the 24V COM terminals there. Both the digital and analogue I/Os have self-insulation, so can be connected to power supplies with different voltages than 24V. Two analogue inputs can be used in the default 4–20mA configuration (requiring a 1kΩ resistor) or 0–10V. Three digital inputs and five digital outputs complete the set.
While the total number of I/Os on the TouchBerry Pi is no match for the huge number featured on a dedicated PLC, there should be enough here for many basic applications. We tested it out by connecting a DS18B20 temperature sensor and displaying the data in on-screen gauges using the Node-RED low-code environment, as detailed here. A little configuration is required, but it’s fairly easy to set up and deploy.
Verdict
8/10
A really solid unit with good hazard protection, a decent touchscreen, and enough I/O for simple applications.
If you are into tech, keeping up with the latest updates can be tough, particularly when it comes to artificial intelligence (AI) and generative AI (GenAI). Sometimes I admit to feeling this way myself, however, there was one update recently that really caught my attention. OpenAI launched their latest iteration of ChatGPT, this time adding a female-sounding voice. Their launch video demonstrated the model supporting the presenters with a maths problem and giving advice around presentation techniques, sounding friendly and jovial along the way.
Adding a voice to these AI models was perhaps inevitable as big tech companies try to compete for market share in this space, but it got me thinking, why would they add a voice? Why does the model have to flirt with the presenter?
Working in the field of AI, I’ve always seen AI as a really powerful problem-solving tool. But with GenAI, I often wonder what problems the creators are trying to solve and how we can help young people understand the tech.
What problem are we trying to solve with GenAI?
The fact is that I’m really not sure. That’s not to suggest that I think that GenAI hasn’t got its benefits — it does. I’ve seen so many great examples in education alone: teachers using large language models (LLMs) to generate ideas for lessons, to help differentiate work for students with additional needs, to create example answers to exam questions for their students to assess against the mark scheme. Educators are creative people and whilst it is cool to see so many good uses of these tools, I wonder if the developers had solving specific problems in mind while creating them, or did they simply hope that society would find a good use somewhere down the line?
Whilst there are good uses of GenAI, you don’t need to dig very deeply before you start unearthing some major problems.
Anthropomorphism
Anthropomorphism relates to assigning human characteristics to things that aren’t human. This is something that we all do, all of the time, without it having consequences. The problem with doing this with GenAI is that, unlike an inanimate object you’ve named (I call my vacuum cleaner Henry, for example), chatbots are designed to be human-like in their responses, so it’s easy for people to forget they’re not speaking to a human.
As feared, since my last blog post on the topic, evidence has started to emerge that some young people are showing a desire to befriend these chatbots, going to them for advice and emotional support. It’s easy to see why. Here is an extract from an exchange between the presenters at the ChatGPT-4o launch and the model:
ChatGPT (presented with a live image of the presenter): “It looks like you’re feeling pretty happy and cheerful with a big smile and even maybe a touch of excitement. Whatever is going on? It seems like you’re in a great mood. Care to share the source of those good vibes?” Presenter: “The reason I’m in a good mood is we are doing a presentation showcasing how useful and amazing you are.” ChatGPT: “Oh stop it, you’re making me blush.”
“Some people just want to talk to somebody. Just because it’s not a real person, doesn’t mean it can’t make a person feel — because words are powerful. At the end of the day, it can always help in an emotional and mental way.”
The prospect of teenagers seeking solace and emotional support from a generative AI tool is a concerning development. While these AI tools can mimic human-like conversations, their outputs are based on patterns and data, not genuine empathy or understanding. The ultimate concern is that this exposes vulnerable young people to be manipulated in ways we can’t predict. Relying on AI for emotional support could lead to a sense of isolation and detachment, hindering the development of healthy coping mechanisms and interpersonal relationships.
Arguably worse is the recent news of the world’s first AI beauty pageant. The very thought of this probably elicits some kind of emotional response depending on your view of beauty pageants. There are valid concerns around misogyny and reinforcing misguided views on body norms, but it’s also important to note that the winner of “Miss AI” is being described as a lifestyle influencer. The questions we should be asking are, who are the creators trying to have influence over? What influence are they trying to gain that they couldn’t get before they created a virtual woman?
DeepFake tools
Another use of GenAI is the ability to create DeepFakes. If you’ve watched the most recent Indiana Jones movie, you’ll have seen the technology in play, making Harrison Ford appear as a younger version of himself. This is not in itself a bad use of GenAI technology, but the application of DeepFake technology can easily become problematic. For example, recently a teacher was arrested for creating a DeepFake audio clip of the school principal making racist remarks. The recording went viral before anyone realised that AI had been used to generate the audio clip.
Easy-to-use DeepFake tools are freely available and, as with many tools, they can be used inappropriately to cause damage or even break the law. One such instance is the rise in using the technology for pornography. This is particularly dangerous for young women, who are the more likely victims, and can cause severe and long-lasting emotional distress and harm to the individuals depicted, as well as reinforce harmful stereotypes and the objectification of women.
Why we should focus on using AI as a problem-solving tool
Technological developments causing unforeseen negative consequences is nothing new. A lot of our job as educators is about helping young people navigate the changing world and preparing them for their futures and education has an essential role in helping people understand AI technologies to avoid the dangers.
Our approach at the Raspberry Pi Foundation is not to focus purely on the threats and dangers, but to teach young people to be critical users of technologies and not passive consumers. Having an understanding of how these technologies work goes a long way towards achieving sufficient AI literacy skills to make informed choices and this is where our Experience AI program comes in.
Experience AI is a set of lessons developed in collaboration with Google DeepMind and, before we wrote any lessons, our team thought long and hard about what we believe are the important principles that should underpin teaching and learning about artificial intelligence. One such principle is taking a problem-first approach and emphasising that computers are tools that help us solve problems. In the Experience AI fundamentals unit, we teach students to think about the problem they want to solve before thinking about whether or not AI is the appropriate tool to use to solve it.
Taking a problem-first approach doesn’t by default avoid an AI system causing harm — there’s still the chance it will increase bias and societal inequities — but it does focus the development on the end user and the data needed to train the models. I worry that focusing on market share and opportunity rather than the problem to be solved is more likely to lead to harm.
Another set of principles that underpins our resources is teaching about fairness, accountability, transparency, privacy, and security (Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education, Understanding Artificial Intelligence Ethics and Safety) in relation to the development of AI systems. These principles are aimed at making sure that creators of AI models develop models ethically and responsibly. The principles also apply to consumers, as we need to get to a place in society where we expect these principles to be adhered to and consumer power means that any models that don’t, simply won’t succeed.
Furthermore, once students have created their models in the Experience AI fundamentals unit, we teach them about model cards, an approach that promotes transparency about their models. Much like how nutritional information on food labels allows the consumer to make an informed choice about whether or not to buy the food, model cards give information about an AI model such as the purpose of the model, its accuracy, and known limitations such as what bias might be in the data. Students write their own model cards based on the AI solutions they have created.
What else can we do?
At the Raspberry Pi Foundation, we have set up an AI literacy team with the aim to embed principles around AI safety, security, and responsibility into our resources and align them with the Foundations’ mission to help young people to:
Be critical consumers of AI technology
Understand the limitations of AI
Expect fairness, accountability, transparency, privacy, and security and work toward reducing inequities caused by technology
See AI as a problem-solving tool that can augment human capabilities, but not replace or narrow their futures
Our call to action to educators, carers, and parents is to have conversations with your young people about GenAI. Get to know their opinions on GenAI and how they view its role in their lives, and help them to become critical thinkers when interacting with technology.
For his project, Rodrigo wanted to bring Winamp’s early days back to life in a more physical form so he decided to produce a music player that could easily be part of a hi-fi mini system. Specifically, he took inspiration from a Aiwa mini setup from around 1983, figuring he could create a case and have all of the functions available on a front touchscreen display.
One thing he noticed was the simplicity of design of the old devices. “Old electronics, especially audio devices, managed to look high-tech and yet elegant,” he explains. “They managed to fit in well with people’s home décor while using relatively simple construction techniques such as sheet metal or wood. I thought that, with all the new manufacturing services we have now, It wouldn’t be too hard to replicate some of the construction techniques of my old Aiwa system, so I set out to learn how to design sheet metal parts, which was in itself a fun exercise to do.”
Physical
Rodrigo based his build around a Raspberry Pi 4B with a 32GB microSD card. “I decided to use the Raspberry Pi because of the software support and extensive documentation and online resources,” he said. “In the past, I’ve completed projects with other single board computers and I’ve always found small details that made their usage much more complex, for example, limited documentation on how to do lower level things like interfacing with special displays, controlling the boot process, or even just supporting recent Linux kernels.
“Since this was a hobby project, I didn’t want to spend too much time figuring out those details on other SBCs. Also I chose Raspberry Pi 4 because it was the latest model at the time I started the project. Raspberry Pi 5 was announced after I’d begun and I figured I didn’t really need that device’s performance anyway.”
At first, Rodrigo toyed with the idea of making the front face physical. “I wanted to add real-life buttons using a 3D printer and I was going to design a custom PCB for the interface,” he says. But he couldn’t find screens with the correct size and dimensions for the spectrum analyser and song information displays that needed to be included to remain faithful to Winamp without making the build too complex.
“Making it physical would have made it less flexible as well,” he adds. For example, I would not be able to have the playlist view in the same display, which would make the device bigger and clunkier so I decided to go with a touchscreen.” This, he figured, would stretch across the entire front of the device and include all of the buttons as part of the UI. He reckoned his background as a software engineer meant this would prove to be relatively straightforward.
A little more love
Rodrigo opted for a 7.9-inch extra-wide screen and he created the case using sheet metal, sending his Onshape designs to PCBWay so they could be produced using 1mm anodised aluminium. He’d closely analysed his Aiwa system so he could figure the correct dimensions and overall look and, for convenience, he placed a power button, Ethernet port, USB-A and USB-ports and 3.5mm stereo jack at the back.
The majority of his time was spent on the software, however. He created a custom Qt 6 app which he wrote in C++. “Replicating the layout itself wasn’t that hard, it was just a matter of getting rough dimensions at first and drafting the UI in Qt Creator using the graphical Qt Widget designer interface, and then fine tuning dimensions by comparing screenshots in GIMP,” he explains. “The complex part was replicating the spectrum analyser and getting the raw audio data to feed it. I did a lot of research and looked into other open-source projects’ code to get ideas and code to base my solution on.”
It’s proven to be a very vivid, eye-catching solution and it doesn’t feel as if any compromises have been made. Above all, it has the intended nostalgic flavour thanks in the main to the spectrum analyser and the way the name of the song and artist scroll across the top. It also retains the intuitive UI in full, so songs can be selected and played in no time. “Linamp supports playing music CDs from a CD/DVD drive connected via USB as well, but you need to use a powered USB hub in order for the drive to work, otherwise Raspberry Pi may not have enough power for the external drive,” Rodrigo adds.
Yet despite being feature packed, Rodrigo wants more. He’s also been overwhelmed by the positive reaction from people who have seen the project and he is working on adding Spotify playback and Bluetooth. “It had an amazing response,” Rodrigo says. “I never imagined it would have been that popular, especially on YouTube, where I uploaded a quick video of how the UI works as an afterthought.” With more than 100,000 views, we don’t think we’ve heard the last of this project.
We tried out the Advanced kit with Raspberry Pi 5. The arm comes ready-assembled, sitting on a solid black metal base whose rear section holds Raspberry Pi, secured with screws and pillars. Four suction cups stick the base to a desk or the floor – not the most secure of methods – but the unit itself is very sturdy.
The breakout board is mounted on Raspberry Pi and wired from JST connectors to the arm, with some spare servo and SPI sockets. Power – for the arm and Raspberry Pi – is supplied from a 7.5V 6A DC PSU via a barrel adapter with two wires that fit into screw terminals on the breakout board.
The arm itself has six DOF (degrees of freedom) counting the gripper on the end of it, which can be rotated via a ‘wrist’. The base servo enables the whole arm to rotate, while three vertical elbow joints, with anodised metal brackets, enable countless possible positions. The servos are of three types/sizes, each with 0.3° accuracy, 0–240° rotation range, and feedback for angle read, temperature, voltage and position. Arm movement is reasonably smooth, and not particularly noisy.
A view to a thrill
Mounted just behind the gripper, the HD camera offers a wide-angle 120° view. It’s connected by a long, loose cable to one of Raspberry Pi’s USB ports, with no clips to secure it to the arm, although it does pass through a cutout in the metal base.
The camera is the key to the arm’s AI abilities. The simplest way to try these out is with the WonderPi smartphone app (iOS or Android). Once your phone is connected to the arm’s automatic Wi-Fi hotspot, you can use the app to control it remotely. The gripper, wrist, and base servos are adjusted by pressing on-screen arrows, while the others are controlled by dragging them on a graphic of the arm.
The app’s AI modes are where the fun really starts. Item tracking sees the arm move to track an object of the chosen colour (red, green, or blue) held in your hand. Face recognition is self-explanatory, resulting in a gripper ‘wave’ to say hello. Item sorting and stacking modes see the arm pick up coloured (or tagged) blocks in turn and either place them on the correct squares on the paper map, or stacked up on a blank square. It’s impressive to watch in action and the app gives you an FPV camera view. If you have the optional shelf units, you can get it to do warehousing, delivering, and transferring tasks.
The app is just a showcase for the arm’s capabilities, however: a starting point. By following an extensive array of online tutorials, you’ll learn how to program it with Python, use OpenCV for image recognition, employ ROS (Robot Operating System) and MoveIt motion planning, use inverse kinematics to reach a desired arm position, and much more.
Over the past few months, young people across Europe have run their computer programs on the International Space Station (ISS) as part of Astro Pi Mission Zero and Mission Space Lab.
Mission Zero code deployment | Credits: ESA/NASA
Mission Zero offers young people the chance to write a simple program that takes a reading from the colour and luminosity sensor on an Astro Pi computer on board the ISS, and uses it to set the background colour in a personalised image for the astronauts to see as they go about their daily tasks. In total, 16,039 teams and 24,663 young people participated in Mission Zero this year. This was a 3% increase in teams entering compared to last year.
Mission Space Lab offers teams of young people the chance to run scientific experiments on board the ISS. This year, 564 teams and 2,008 young people participated in Mission Space Lab. Compared with last year, there was a 4% increase in the number of teams who managed to achieve flight status and run their code in space.
To evaluate the projects, we encouraged mentors to complete surveys once their teams had submitted their computer programs. Overall, 135 Mission Zero mentors (11% of mentors) and 56 Mission Space Lab mentors (15% of mentors) completed surveys. We also ran focus groups with mentors from both projects to understand their experiences and the impact of these projects on young people.
Impact on young people
Understanding how technology is changing the world
The mentors we spoke to told us how valuable Mission Zero and Mission Space Lab are because these experiences connect young people to real technology. Mentors felt that Mission Zero and Mission Space Lab bridge the gap between theoretical coding and tangible outcomes, giving young people the confidence to engage with technology.
“Participating in Mission Space Lab offers students a great opportunity to work with the International Space Station, to see the Earth from above, to challenge them to overcome the terrestrial limits. It’s very important.” — Mission Space Lab mentor
“We want students to use their digital skills as superpowers to make the world a better place and this competition really aligns with that because regardless of your race, your ethnicity, your gender, you can write some code that actually runs in space. And if you can do that, then you can make medical tech, or you can solve the big problem that the adults of the world are still grappling with, so it’s the opening up [of] opportunities.” — Mission Zero mentor
Mentors observed that the project inspired children to consider careers they previously thought were out of reach. Space exploration was no longer a far away and theoretical idea for the children, but something connected to their everyday lives and their own learning.
“Some of the people that I was teaching this to felt like becoming an astronaut was really difficult to learn… now it’s not necessarily a distant thing to study.” — Mission Zero mentor
Mentors also described how the young people gained confidence in their ability to engage with technologies. One mentor described the “self-esteem” and “pride” younger pupils gained from participation. Others talked about the confidence that came with achieving something like having their code run in space and receiving certificates proving they were “space scientists”.
Our mentors
None of this would be possible without the hard work and dedication of our mentors. So, as part of our evaluation, we wanted to understand how we can best support them. For Mission Space Lab, that took the form of assessing the new guidance that we published this year and that sits alongside the project. When we spoke to mentors, they told us this guide provided clear, step-by-step guidance that enabled the young people to work through the project, and the majority of survey respondents agreed: 89% rated the Mission Space Lab project guide as somewhat or very understandable.
We also heard from mentors about the ways they are using Mission Zero in a wider context. Some told us that their schools ran the project as part of space-themed weeks where they used Mission Zero in conversations about space exploration, the Hubble telescope, and learning the names of the stars. Others used Mission Zero across multiple subjects by designing images and holding art competitions based on the design, as well as learning about pixels and animations.
Additionally, it was a pleasure to hear about young people who had participated in Mission Zero in previous years gaining leadership skills by supporting other young people to complete Mission Zero this year.
Next steps
Thank you to all the mentors who provided constructive feedback through surveys and focus groups. We have read and considered every comment and will continue to consider how to improve the experience for mentors and young people.
We will publish an in-depth report with the findings of our evaluation later in the year; however, we’ve already made some changes to the programme that will be launching for the 2024/25 Astro Pi challenge and wanted to share these updates with you now.
Improvements for next year:
Mission Zero
We’re adding a save button to Mission Zero to allow young people to work on this across multiple sessions.
We’re adding new code examples to the Mission Zero project guide. These have been selected from team submissions from the 2023/24 challenge.
Mission Space Lab
We’re creating an online testing tool for Mission Space Lab so that it will be easier for teams to test whether or not their code works. It will feature new data and images captured from the ISS in spring 2024.
We hope that all the young people and mentors who participated in last year’s Astro Pi challenge enjoyed the experience and learnt a lot. With the exciting updates we’re working on for the 2024/25 Astro Pi challenge, we hope to see even more young people participate and share their creative projects next year.
Project launch dates
16 September 2024: Mission Zero and Mission Space Lab launch
24 February 2025: Mission Space Lab submissions close
24 March 2025: Mission Zero submissions close
April – May 2025: Programs run on the International Space Station
That first turntable used a DC motor, which lacks precision, resulting in inconsistent movements which vary with the weight of the object placed on it. “We wanted something more precise,” recalls Martin, “and we wanted a remote that we didn’t keep losing, so we put it as a page on a web server.”
In addition, they simplified the original version’s 3D-printed gearing system, which included a worm gear, to use two gears instead of three.
Web-based spinning
Raspberry Pico W has a web server running on it, based on Simon Prickett’s Phewap project. On that server sits a page that shows a virtual remote. “The buttons trigger some MicroPython code that tells the turntable’s stepper motor to turn, then 3D printed gears translate the movement from the motor to the bearing, which in turn moves the turntable.” The ‘lazy Susan’-style bearing, as used in turntables for the dining table, features multiple metal balls between two circular metal plates for low-friction movement.
Since the stepper motor they used requires a fairly hefty voltage, a 20V Power Delivery trigger board is used to step up the voltage supplied via USB-C. “Using PD triggers was one of the good bits: it makes for a much smaller build than having to try to cram a power brick in there too. They’re really cheap too, so it’s a rare win-win.”
VEEB chose Raspberry Pi Pico W for the project due to “a flat refusal to use C, due to lack of skill, and the knowledge that the documentation for Raspberry Pi stuff is less sparse than for other microcontrollers,” says Martin. “When we get stuck, there’s usually someone that has run into a similar problem and documented it somewhere.”
Learning process
Martin reckons the project took around a year to complete, working on and off. “There were a few mini projects in there: learning enough OpenSCAD for the gears, learning enough CSS for the remote page, getting the stepper motor to work. It was one of those things that you keep going back to when you learn something new during another project.”
An impressive stop-motion video of a roller-skate and disco ball for the Twirly Shirley YouTube video was shot using a camera taking photos at set intervals, but a possible upgrade would be to use a Raspberry Pi single-board computer to automate the process. “An SBC could take photos and send requests for the turntable to move,” notes Martin. “The last thing we made used web sockets to make a Pico listen for signals being broadcast from a [Raspberry] Pi with a camera attached.”
Become familiar with electronic components, circuit building, code and some great starter projects. This month’s lead feature will have you up and running with electronics and code in no time at all.
Build a private cloud server
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Learn to use the new AI Kit
Get to grips with ultra-fast image recognition by connecting the new AI Kit to Raspberry Pi. With 13-tera operations per second, this neural interface performs machine learning tasks at ultra-fast speeds. Add some smarts to your projects, and learn how artificial intelligence works with AI Kit and Raspberry Pi.
Incredible photo & vision projects
Put that Raspberry Pi Camera to use with these incredible vision-based projects. Adding a camera to Raspberry Pi is more than just a camera replacement, with eyes your Raspberry Pi can see and interact with the world around it.
ArmPi FPV AI Vision
Give your Raspberry Pi a hand! In fact, give it the whole arm with this incredible robotics arm project. We test out ArmPi, and AI-powered robotic arm for experimentation.
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The world of education is loud with discussions about the uses and risks of generative AI — tools for outputting human-seeming media content such as text, images, audio, and video. In answer, there’s a new practical guide on using generative AI aimed at Computing teachers (and others), written by a group of classroom teachers and researchers at the Raspberry Pi Computing Education Research Centre and Faculty of Education at the University of Cambridge.
Their new guide is a really useful overview for everyone who wants to:
Understand the issues generative AI tools present in the context of education
Find out how to help their schools and students navigate them
Discover ideas on how to make use of generative AI tools in their teaching
Since generative AI tools have become publicly available, issues around data privacy and plagiarism are at the front of educators’ minds. At the same time, many educators are coming up with creative ways to use generative AI tools to enhance teaching and learning. The Research Centre’s guide describes the areas where generative AI touches on education, and lays out what schools and teachers can do to use the technology beneficially and help their learners do the same.
Teaching students about generative AI tools
It’s widely accepted that AI tools can bring benefits but can also be used in unhelpful or harmful ways. Basic knowledge of how AI and machine learning works is key to being able to get the best from them. The Research Centre’s guide shares recommended educational resources for teaching learners about AI.
One of the recommendations is Experience AI, a set of free classroom resources we’re creating. It includes a set of 6 lessons for providing 11- to 14-year-olds with a foundational understanding of AI systems, as well as a standalone lesson specifically for teaching about large language model-based AI tools, such as ChatGPT and Google Gemini. These materials are for teachers of any specialism, not just for Computing teachers.
You’ll find that even a brief introduction to how large language models work is likely to make students’ ideas about using these tools to do all their homework much less appealing. The guide outlines creative ways you can help students see some of generative AI’s pitfalls, such as asking students to generate outputs and compare them, paying particular attention to inaccuracies in the outputs.
Generative AI tools and teaching computing
We’re still learning about what the best ways to teach programming to novice learners are. Generative AI has the potential to change how young people learn text-based programming, as AI functionality is now integrated into many of the major programming environments, generating example solutions or helping to spot errors.
The Research Centre’s guide acknowledges that there’s more work to be done to understand how and when to support learners with programming tasks through generative AI tools. (You can follow our ongoing seminar series on the topic.) In the meantime, you may choose to support established programming pedagogies with generative AI tools, such as prompting an AI chatbot to generate a PRIMM activity on a particular programming concept.
As ethics and the impact of technology play an important part in any good Computing curriculum, the guide also shares ways to use generative AI tools as a focus for your classroom discussions about topics such as bias and inequality.
Using generative AI tools to support teaching and learning
Teachers have been using generative AI applications as productivity tools to support their teaching, and the Research Centre’s guide gives several examples you can try out yourself. Examples include creating summaries of textual materials for students, and creating sets of questions on particular topics. As the guide points out, when you use generative AI tools like this, it’s important to always check the accuracy of the generated materials before you give any of them to your students.
Putting a school-wide policy in place
Importantly, the Research Centre’s guide highlights the need for a school-wide acceptable use policy (AUP) that informs teachers, other school staff, and students on how they may use generative AI tools. This section of the guide suggests websites that offer sample AUPs that can be used as a starting point for your school. Your AUP should aim to keep users safe, covering e-safety, privacy, and security issues as well as offering guidance on being transparent about the use of generative tools.
It’s not uncommon that schools look to specialist Computing teachers to act as the experts on questions around use of digital tools. However, for developing trust in how generative AI tools are used in the school, it’s important to encourage as wide a range of stakeholders as possible to be consulted in the process of creating an AUP.
A source of support for teachers and schools
As the Research Centre’s guide recognises, the landscape of AI and our thinking about it might change. In this uncertain context, the document offers a sensible and detailed overview of where we are now in understanding the current impact of generative AI on Computing as a subject, and on education more broadly. The example use cases and thought-provoking next steps on how this technology can be used and what its known risks and concerns are should be helpful for all interested educators and schools.
I recommend that all Computing teachers read this new guide, and I hope you feel inspired about the key role that you can play in shaping the future of education affected by AI.
Developed by us and Google DeepMind, Experience AI provides teachers with free resources to help them confidently deliver lessons that inspire and educate young people about artificial intelligence (AI) and the role it could play in their lives.
Tracy Mayhead is a computer science teacher at Arthur Mellows Village College in Cambridgeshire. She recently taught Experience AI to her KS3 pupils. In this blog post, she shares 4 key learnings from this experience.
1. Preparation saves time
The Experience AI lesson plans provided a clear guide on how to structure our lessons.
Each lesson includes teacher-facing intro videos, a lesson plan, a slide deck, activity worksheets, and student-facing videos that help to introduce each new AI concept.
It was handy to know in advance which websites needed unblocking so students could access them.
“My favourite bit was making my own model, and choosing the training data. I enjoyed seeing how the amount of data affected the accuracy of the AI and testing the model.” – Student, Arthur Mellows Village College, UK
2. The lessons can be adapted to meet student’s needs
It was clear from the start that I could adapt the lessons to make them work for myself and my students.
Having estimated times and corresponding slides for activities was beneficial for adjusting the lesson duration. The balance between learning and hands-on tasks was just right.
I felt fairly comfortable with my understanding of AI basics. However, teaching it was a learning experience, especially in tailoring the lessons to cater to students with varying knowledge. Their misconceptions sometimes caught me off guard, like their belief that AI is never wrong. Adapting to their needs and expectations was a learning curve.
“It has definitely changed my outlook on AI. I went from knowing nothing about it to understanding how it works, why it acts in certain ways, and how to actually create my own AI models and what data I would need for that.” – Student, Arthur Mellows Village College, UK
3. Young people are curious about AI and how it works
My students enjoyed the practical aspects of the lessons, like categorising apples and tomatoes. They found it intriguing how AI could sometimes misidentify objects, sparking discussions on its limitations. They also expressed concerns about AI bias, which these lessons helped raise awareness about. I didn’t always have all the answers, but it was clear they were curious about AI’s implications for their future.
It’s important to acknowledge that as a teacher you won’t always have all the answers especially when teaching AI literacy, which is such a new area. This is something that can be explored in a class alongside students.
There is an online course you can use that can help get you started teaching about AI if you are at all nervous.
“I learned a lot about AI and the possibilities it holds to better our futures as well as how to train it and problems that may arise when training it.” – Student, Arthur Mellows Village College, UK
4. Engaging young people with AI is important
Students are fascinated by AI and they recognise its significance in their future. It is important to equip them with the knowledge and skills to fully engage with AI.
Experience AI provides a valuable opportunity to explore these concepts and empower students to shape and question the technology that will undoubtedly impact their lives.
“It has changed my outlook on AI because I now understand it better and feel better equipped to work with AI in my working life.” – Student, Arthur Mellows Village College, UK
What is your experience of teaching Experience AI lessons?
We completely agree with Tracy. AI literacy empowers people to critically evaluate AI applications and how they are being used. Our Experience AI resources help to foster critical thinking skills, allowing learners to use AI tools to address challenges they are passionate about.
We’re also really interested to learn what misconceptions students have about AI and how teachers are addressing them. If you come across misconceptions that surprise you while you’re teaching with the Experience AI lesson materials, please let us know via the feedback form linked in the final lesson of the six-lesson unit.
If you would like to teach Experience AI lessons to your students, download the free resources from experience-ai.org
As use of generative artificial intelligence (or generative AI) tools such as ChatGPT, GitHub Copilot, or Gemini becomes more widespread, educators are thinking carefully about the place of these tools in their classrooms. For undergraduate education, there are concerns about the role of generative AI tools in supporting teaching and assessment practices. For undergraduate computer science (CS) students, generative AI also has implications for their future career trajectories, as it is likely to be relevant across many fields.
Dr Stephen MacNeil, Andrew Tran, and Irene Hou (Temple University)
In a recent seminar in our current series on teaching programming (with or without AI), we were delighted to be joined by Dr Stephen MacNeil, Andrew Tran, and Irene Hou from Temple University. Their talk showcased several research projects involving generative AI in undergraduate education, and explored how undergraduate research projects can create agency for students in navigating the implications of generative AI in their professional lives.
Differing perceptions of generative AI
Stephen began by discussing the media coverage around generative AI. He highlighted the binary distinction between media representations of generative AI as signalling the end of higher education — including programming in CS courses — and other representations that highlight the issues that using generative AI will solve for educators, such as improving access to high-quality help (specifically, virtual assistance) or personalised learning experiences.
As part of a recent ITiCSE working group, Stephen and colleagues conducted a survey of undergraduate CS students and educators and found conflicting views about the perceived benefits and drawbacks of generative AI in computing education. Despite this divide, most CS educators reported that they were planning to incorporate generative AI tools into their courses. Conflicting views were also noted between students and educators on what is allowed in terms of generative AI tools and whether their universities had clear policies around their use.
The role of generative AI tools in students’ help-seeking
There is growing interest in how undergraduate CS students are using generative AI tools. Irene presented a study in which her team explored the effect of generative AI on undergraduate CS students’ help-seeking preferences. Help-seeking can be understood as any actions or strategies undertaken by students to receive assistance when encountering problems. Help-seeking is an important part of the learning process, as it requires metacognitive awareness to understand that a problem exists that requires external help. Previous research has indicated that instructors, teaching assistants, student peers, and online resources (such as YouTube and Stack Overflow) can assist CS students. However, as generative AI tools are now widely available to assist in some tasks (such as debugging code), Irene and her team wanted to understand which resources students valued most, and which factors influenced their preferences. Their study consisted of a survey of 47 students, and follow-up interviews with 8 additional students.
Undergraduate CS student use of help-seeking resources
Responding to the survey, students stated that they used online searches or support from friends/peers more frequently than two generative AI tools, ChatGPT and GitHub Copilot; however, Irene indicated that as data collection took place at the beginning of summer 2023, it is possible that students were not familiar with these tools or had not used them yet. In terms of students’ experiences in seeking help, students found online searches and ChatGPT were faster and more convenient, though they felt these resources led to less trustworthy or lower-quality support than seeking help from instructors or teaching assistants.
Some students felt more comfortable seeking help from ChatGPT than peers as there were fewer social pressures. Comparing generative AI tools and online searches, one student highlighted that unlike Stack Overflow, solutions generated using ChatGPT and GitHub Copilot could not be verified by experts or other users. Students who received the most value from using ChatGPT in seeking help either (i) prompted the model effectively when requesting help or (ii) viewed ChatGPT as a search engine or comprehensive resource that could point them in the right direction. Irene cautioned that some students struggled to use generative AI tools effectively as they had limited understanding of how to write effective prompts.
Using generative AI tools to produce code explanations
Andrew presented a study where the usefulness of different types of code explanations generated by a large language model was evaluated by students in a web software development course. Based on Likert scale data, they found that line-by-line explanations were less useful for students than high-level summary or concept explanations, but that line-by-line explanations were most popular. They also found that explanations were less useful when students already knew what the code did. Andrew and his team then qualitatively analysed code explanations that had been given a low rating and found they were overly detailed (i.e. focusing on superfluous elements of the code), the explanation given was the wrong type, or the explanation mixed code with explanatory text. Despite the flaws of some explanations, they concluded that students found explanations relevant and useful to their learning.
Perceived usefulness of code explanation types
Using generative AI tools to create multiple choice questions
In a separate study, Andrew and his team investigated the use of ChatGPT to generate novel multiple choice questions for computing courses. The researchers prompted two models, GPT-3 and GPT-4, with example question stems to generate correct answers and distractors (incorrect but plausible choices). Across two data sets of example questions, GPT-4 significantly outperformed GPT-3 in generating the correct answer (75.3% and 90% vs 30.8% and 36.7% of all cases). GPT-3 performed less well at providing the correct answer when faced with negatively worded questions. Both models generated correct answers as distractors across both sets of example questions (GPT-3: 11.1% and 10% of cases; GPT-4: 9.9% and 17.8%). They concluded that educators would still need to verify whether answers were correct and distractors were appropriate.
Undergraduate students shaping the direction of generative AI research
With student concerns about generative AI and its implications for the world of work, the seminar ended with a hopeful message highlighting undergraduate students being proactive in conducting their own research and shaping the direction of generative AI research in computer science education. Stephen concluded the seminar by celebrating the undergraduate students who are undertaking these research projects.
Our current seminar series is on teaching programming with or without AI.
In our next seminar, on 16 July at 17:00 to 18:30 BST, we welcome Laurie Gale (Raspberry Pi Computing Education Research Centre, University of Cambridge), who will discuss how to teach debugging to secondary school students. To take part in the seminar, click the button below to sign up, and we will send you information about how to join. We hope to see you there.
With that in mind, he went big. And we mean really big. He took a 65-inch touchscreen and connected it to a Raspberry Pi 5 computer, figuring games would look amazing across such a large display. It also enabled David to create a full-length mirror, despite it posing issues of its own.
“Working with such a large display was a challenge due to the physical weight of moving and manipulating it,” he explains. “I think it weighed 48kg so I really shouldn’t have been lifting it on my own. I was afraid I would break it by letting it flex, cracking the screen.”
Looking good
Initially, David tested the concept using a Raspberry Pi 3 computer and an official Raspberry Pi seven-inch touchscreen. He played around with PINN, a version of the NOOBS operating system installer, and sought to get everything working with RetroPie before ordering the larger equipment.
“Unfortunately, the curse of the early adopter struck, with RetroPie and PINN not having official support for Raspberry Pi 5 at the time,” David says. “It took some time to get PINN working at all and, even then, I think Raspberry Pi 5 support was questionable.” David switched to Recalbox which was installed on one partition. Another partition was used for the magic mirror functions.
“I wanted the mirror to play as many gaming platforms as possible,” David says. “To achieve this I figured I needed the most processing power, and the Raspberry Pi 5 seemed the best way to go. So far it has proved more than capable of emulating games on many platforms without much trouble.”
On reflection
David also added motion-sensing using a PIR sensor. When someone walks in front of the sensor, the screen turns on. When the person moves away, it turns off. The display also turns off at night and comes back on in the morning, using the Raspberry Pi OS’ Magic Mirror app to show the weather forecast, a calendar and more. The build also includes an RS232 converter so that the Raspberry Pi’s Universal Asynchronous Receiver/Transmitter (UART) can be converted for serial communications.
When you want to play, controllers can be connected via Bluetooth Low-Energy or USB, and the games look a treat on such a large screen. There is still room for improvement, however. “I still need to get around to reapplying the mirror film again,” he says. “I’d also like to spend more time with the plugins to the magic mirror platform, maybe even develop a couple of my own to make the best use of the screen real estate available. Maybe in the future there is scope for a camera, facial recognition and a multi-user experience.”
We love hearing from members of the community and sharing the stories of amazing young people, volunteers, and educators who are using their passion for technology to create positive change in the world around them.
In our latest story, we’re heading to London to meet Yang, a Manager in Technology Consulting at EY specialising in Microsoft Business Applications, whose commitment to CoderDojo is truly inspiring. Yang’s passion for volunteering has grown since she first volunteered at a CoderDojo club at a local museum. In recent years, she has actively searched for ways to bring the CoderDojo movement to more children, and encouraged her colleagues to come along on the journey too.
When Yang was growing up, both of her parents worked in STEM, but her own journey into a career in technology took a varied route. After initially studying journalism in China, her path shifted when she pursued a Master’s in Digital Humanities at UCL, London, broadening her digital skills and paving the way for her current role.
On a weekend visit to a museum, Yang found the opportunity to volunteer at their CoderDojo. This experience sparked an enthusiasm to create more opportunities for young people to explore the world of computing, and this soon evolved into a plan to implement clubs at the EY offices.
Building a community of mentors
With support from the EY Corporate Responsibility team, and fellow colleagues, Yang started to deliver Dojo sessions at the EY office in London. From the very first session, Yang was blown away by the level of enthusiasm among her colleagues, and their willingness to volunteer their time to support the club. She soon realised it was possible to roll this initiative out to other offices around the country, expanding the volunteer network and increasing their impact.
Clubs have now been run in four EY offices across the UK, and the team has even seen the first international club take place, at the EY office in Baku, Azerbaijan. In total, EY clubs have seen around 350 young people attend and give coding a go.
Championing diversity in tech
As a woman in tech, Yang is all too aware of the gender imbalance in the industry, and this is something she wanted the clubs at the EY offices to address.
“If there are some female role models, I think for a little girl grow up that means so much. Because if they can see somebody thrive in this industry, they will see themselves there one day. And that’s the inspiration.” – Yang
Yang actively encourages female participation in Dojo sessions, for example through holding sessions with a focus on engaging girls to mark International Women’s Day and Ada Lovelace Day. Through her leadership, she creates an inclusive environment where girls can envision themselves as future leaders.
Yang’s motivation doesn’t only inspire the young people attending her clubs, but also resonates with those who work with her on a daily basis, including colleagues like Iman and Elizabeth, who shared how much they admire Yang’s dedication and energy.
“I would love to have had a role model like [Yang] when I was younger. She’s just so inspiring. She’s so full of energy. I mean, from my personal experience, when I was younger, we didn’t have anything to do with coding.
There were situations where I was vaguely interested [in computing] but was told that it wasn’t for girls. And now with Yang running these events, seeing the girls come here and being so interested and wanting to learn, it really opens up so many more doors for them that they don’t even realise.” – Elizabeth, colleague and CoderDojo volunteer
Seeing the impact of her mentorship and the enthusiasm of young participants has fueled Yang’s passion even further.
“This has been a great opportunity to set up CoderDojo sessions for young people. I’ve had a lot of support from colleagues and other volunteers who have helped to run the sessions […] I feel super proud of what we’ve achieved so far.” – Yang
For Yang, mentorship isn’t just about teaching technical skills; it’s about helping young people develop confidence and resilience, and letting everyone know there is a place for them in computing should they want one.
Continuing to make a difference in her community and beyond, Yang recently participated in the 68th annual UN Women’s Commission on the Status of Women, which is the UN’s largest annual gathering on gender equality and women’s empowerment.
We’re delighted to be part of Yang’s journey, and can’t wait to see what she contributes to the world of tech next.
Help us celebrate Yang and her inspiring journey by sharing her story on X, LinkedIn, and Facebook.
Like many great ideas, Puttr came about because of some enforced downtime during lockdown. Entrepreneur and founder of several successful start-ups Matthew Allard had been on the golf team at university, and lockdown had him contemplating an at-home putting game that he and his son could both enjoy. Matthew had a personal interest in how software and computers can interact with the real world, and having taken post-graduate courses in embedded systems was keen to make use of what he’d learned.
One thing Matthew knew already was that “putting practice is boring and lonely” (don’t they have crazy golf courses in the US?) yet it accounts for 42% of time golfers put in. Creating a means to connect fellow golfers and ‘gamify’ putting could transform this rote activity and allow members of the golfing community to challenge each other with online tournaments.
Hits and misses
Matthew originally aimed to track made and missed putts via an app using sensors in the hole of an at-home putting mat hooked up to GPIO pins. However, he soon discovered this approach was limited: “I could detect when a ball went in the hole, [but] I couldn’t detect missed putts.” Next, Matthew tried break-beam IR sensors to get more precision and measure missed putts, as well as ‘makes’, but “quickly realised that any sun exposure would cause false positives in the break-beam”.
A friend tipped him off about Raspberry Pi, and Matthew soon saw he could use computer vision and a wide-angle lens to detect the location of the physical hole, then track any golf ball that passed its field of view. Once a ball has entered or exited, it sends the full ball path data over Bluetooth to a connected app on an iOS or Android device, he explains. Details of each putt are logged, with the user able to access stats on their performance and optionally share it with other Puttr users.
Raspberry Pi quickly proved a great choice, since it offered an operating system with all the tools he needed for the software along with good value hardware that worked well together. “Many suppliers tried to talk me into creating my own board [but] there were many reasons to use Raspberry Pi.” The camera connection, Bluetooth, Wi-Fi, and processor were all included. Matthew was also encouraged by the strong community keen to help with any troubleshooting he might need, given this was his first ever Raspberry Pi project.
Embrace the light
At first, Matthew stuck with his infrared break-beam idea, testing it in his garage in the evenings after long days at his day job. There were “a ton of tweaks” to get the computer vision to work well under different lighting conditions. Eventually, it seemed as though the beams were working just as he expected. “I would get a break when the ball enters the ramp, and another one when and if it entered the hole. Perfect!”
Replicating results when demonstrating the embryonic Puttr game to his son was less successful. In fact, it didn’t work at all in daylight. Matthew eventually realised that sunlight hitting the beam’s receiver was preventing the circuit being broken even when a ball passed through it because it emits infrared rays of its own: “Apparently I missed that in school!” Connecting Raspberry Pi 4 to a GATT server (for Apple devices) as a headless Bluetooth peripheral meant code pairing was not an option. Instead, Matthew created a Bluetooth Write Characteristic that can receive a Wi-Fi SSID and password specifically for the task. He then wrote all the computer vision code and app software to make Puttr work.
Prototyping involved laser-cutting Baltic birchwood, and Matthew’s first foray into 3D design and printing using CraftCloud to create the box used as both ball tracker and holdall, the ramp, and ball return chute. The clever design is portable, with the mat rolling up inside.
Matthew praises the “stable, tested OS, camera interface, Bluetooth and Wi-Fi, and says choosing Raspberry Pi meant R&D took at least a year less than choosing a different setup with costs that would have been much higher. New versions and applications are already planned. Since launching 18 months ago (after a successful Indigogo crowdfunder), the Puttr app has logged more than a million putts. The clever take on pitch and putt now has worldwide league tables, games and challenges, with a subscription model for golfers keen to pit their skills against others.
Elsewhere on the board you’ll find a USB-C power input, speakers, an LED display, GPIO pins, an RFID chip, plenty of sensors and switches and LEDs, and more besides. In the box there’s also a startling array of extra components, including a pair of SNES-like gamepads, a US-style power plug (with a three-pin adapter for UK sockets), servo and stepper motors, an IR remote, LEDs, a small stylus, headphones (3.5mm, so there’s nowhere to plug them in on a Raspberry Pi 5 board) and more. A GPIO ribbon cable is meant to bridge the gap between the Raspberry Pi’s pins and those on the carrier, but one wasn’t included in the package sent to us for review. Something that will fit is pretty cheap and easy to get online, but it would have been nice to have had it included.
It takes a bit of force to successfully mate your Raspberry Pi 5 board with the CrowPi carrier, as the cables put up some resistance to getting it in exactly the right place, and once it’s screwed down the microSD slot is inaccessible. You might also need to rely on Wi-Fi for networking, as the USB cable goes across the Ethernet port, though you may be able to negotiate a fit with a slim cable. Having a power connection enter vertically at the top right of the motherboard feels clunky too – it would have been so much tidier to have it pierce the casing at the rear.
A screw loose
A version of the Raspberry Pi OS with appropriate drivers is available from the CrowPi website – a 3.9GB download – and while the board booted first time, it threw an error when we tried to use the Recommended Software tool and the Terminal (the Terminal text is tricky to read on such a small screen, but that’s not Elecrow’s fault) to install new programs. There was also a loose screw in the case, which fell out when we tried giving it an experimental shake.
These problems are ones that can be fixed via software patches or by updating the package contents for future orders, and don’t affect the fact this is a convenient and well-made electronics board with prolific features. What they do mean is that, in its current state, it’s slightly difficult to recommend the CrowPi Compact Raspberry Pi Educational Kit, which is a shame, as it could be brilliant.
As the Experience AI Challenge has closed for submissions, we would like to thank all the talented young people who participated and submitted their projects this year.
The Challenge, created by us in collaboration with Google DeepMind, guides young people under the age of 18, and their mentors, through the process of creating their own unique AI project. It encourages young people to seek out real-world problems and create possible AI-based solutions. From January to May, participants in the UK were also able to submit their projects for feedback from AI experts.
In response to the submissions, Richard Hayler, our Director of Youth Programmes commented:
“In running the Challenge, we have seen an incredible display of creativity, ingenuity, and curiosity about AI among young people. The dedication and innovation they demonstrated in their submitted projects has been truly inspiring. The Challenge has not only showcased the immense potential of addressing problems using AI tools, but most of all the remarkable talent and dedication of the next generation of innovators.
We would also like to thank all the mentors who guided and encouraged participants throughout the Challenge for their invaluable support. Their expertise and mentorship were instrumental in the young people’s success.”
Some Challenge highlights
These are some examples of the innovative projects young people created:
AI creation: River Water Quality Prediction App
Creator: Shreyas, age 13
What does it do:
“The model predicts how good the water quality of a river is based on several factors such as the levels of ammonium, nitrates, and dissolved oxygen.”
Who is it for:
”It can be used to tell if river water is safe to drink, or safe for life. This can also be used by authorities to decide where to deploy limited resources to purify water depending on its toxicity.”
AI creation: Coeliac Disease
Creator: Zainev, age 14–18
What does it do:
“The model aims to identify foods that contain the allergen gluten.”
Who is it for:
“It is for people with gluten allergy and/or people trying to arrange food for those with a gluten allergy, as it will easily help them identify foods that contain gluten and are not safe to eat.”
AI creation: Spacepuppy’s colour adventure
Creator: Charlotte, age 12
What does it do:
“Teaches children about colours.”
Who is it for:
“Teachers at primary schools/ nurseries.”
AI creation: Nutrify
Creator: Ishaan, age 14–18
What does it do:
“The model identifies the students’ food items through a webcam image, giving its specific nutritional information including calories, carbs, sugars and proteins.”
Who is it for:
“This model can be easily used by students to be aware of the nutritional information of their meals.”
AI creation: Flossie
Creator: Florence, age 11
What does it do:
“Identifies dressing gowns, slippers and pyjamas.”
Who is it for:
“For young children to learn different clothing.”
AI creation: Dermalyst
Creator: Vedant, age 14–18
What does it do:
“Dermalyst is an AI-based dermatologist that analyses images of your skin to check if you have any skin infection or disease and also suggests solutions.”
Who is it for:
“This app is targeted at young people but anyone could use it. It saves them from having to wait for a GP appointment.”
AI creation: Bird identifier
Creator: William, age 13
What does it do:
“It is designed to identify common garden birds native to the United Kingdom. It can identify robins, blue tits, great tits and blackbirds by their photograph.”
Who is it for:
“Bird watchers may use the app to identify the birds that they see but don’t know what they are.”
Save the date for the celebratory webinar
We would like to invite you to an online webinar on Wednesday 10 July at 4pm BST to celebrate all Experience AI Challenge participants. Click ‘notify me’ on YouTube to be notified when the webinar starts.
During the webinar, Mark Calleja from the Raspberry Pi Foundation and Matko Bošnjak, Research Scientist at Google DeepMind, will highlight some young people’s AI creations, and discuss all things AI. You can share your questions about AI for Mark and Matko by filling in this form today.
Download the Experience AI Challenge resources
Once again thank you to everyone who participated in the Experience AI Challenge and submitted their projects.
If you’re interested in the Challenge, you can still download the resources and use them to create your own AI projects.
“This is a watershed moment for Raspberry Pi,” Eben posted on Raspberry Pi dot com that morning. “And the start of a new phase in our evolution: access to the public market will enable us to build more of the products you love, faster. And the money raised by the Raspberry Pi Foundation in the IPO will support its ambitions for global impact in its second decade.”
Philip Colligan, CEO of the Raspberry Pi Foundation wrote in a post a couple of weeks ago just how that would work: “To date, Raspberry Pi Ltd has donated nearly $50m from its profits to the Foundation, which we have used to advance our educational mission combined with over $60m in funding from philanthropy, sponsorship, and contracts for educational services,” he wrote. “From the Foundation’s perspective, an IPO provides us with the ability to sell some of our shares to raise money to finance a sustainable expansion of our educational activities. Put simply, instead of receiving a share of the company’s profits each year, we will convert some of our shareholding into an endowment that we will use to fund our educational programmes.”
What’s next
There’s been a whole lot of work behind the scenes for this for some time now – I’ve only caught glimpses on my monthly visits to Raspberry Pi Towers – so hopefully some of that pressure has now been alleviated. I’ll find out on my next visit.
Anyway, I thought I’d talk about it here as for various reasons we’ve not had a chance to mention it elsewhere in the magazine [lots of exciting new opportunities to end up in front of a judge – Ed]. Also, my car got returned the following day and now I sort of regret not having got up early for it. Ah well – onwards.
Tuesday 11 June 2024 will be remembered as one of the most important days in the history of Raspberry Pi.
At the London Stock Exchange on 11 June 2024.
The successful introduction of the Raspberry Pi Foundation’s commercial subsidiary on the London Stock Exchange is a genuinely remarkable achievement. I want to put on record my huge congratulations and thanks to Eben Upton, Martin Hellawell, and the whole team at Raspberry Pi Holdings plc for everything they have done to make this possible.
The purpose of the IPO was to secure the next stage of growth and impact for both the Foundation and the company. We have huge ambitions and the IPO has provided both organisations with the capital we need to pursue those ambitions at pace and scale. Our Chief Executive Philip Colligan has already explained what it means for the Raspberry Pi Foundation and our mission to empower young people all over the world.
In this post, I wanted to take a moment to acknowledge the significant contribution that others have made over the years, particularly all of the Trustees who have been so generous with their time, energy, and expertise.
Founding Trustees
The Raspberry Pi Foundation was established in 2008 by six founding Trustees: Alan Mycroft, David Braben, Eben Upton, Jack Lang, Pete Lomas, and Rob Mullins. All of them deserve credit and thanks for setting us off on this incredible journey.
Alan, Eben, Jack, and Rob were all involved with the Computer Lab at the University of Cambridge. They were dealing with a decline in applications to study the computer science undergraduate course, which was a symptom of the much wider challenge that far too many young people weren’t getting access to opportunities to learn computer science, or getting hands-on with programming and electronics.
David Braben brought an industry perspective, drawing on the challenges he was experiencing with recruiting engineers and programmers at the world-leading games company that he had founded, Frontier Developments.
At the Sony factory in Pencoed, Wales, that produces Raspberry Pi computers.
For Pete Lomas, he was paying forward the support and inspiration that he received from a college technician who gave him the opportunity and encouragement to experiment with programming a DEC PDP-8. That experience ultimately led Pete to establish Norcott Technologies, an electronics design and manufacturing business that he still runs today.
The founding Trustees’ original idea was to create a low-cost programmable computer — available for the price of a textbook — that would remove price as a barrier to owning a computer and inspire young people to take their first steps with computing. It took four years for the first Raspberry Pi computer to be launched, an achievement for which Eben and Pete were rightly honoured, along with other members of the team, as recipients of the prestigious MacRobert Prize for engineering.
Combining social impact and commercial success
What none of our founding Trustees could have predicted was the enormous commercial success of Raspberry Pi computers. In realising their vision of a low-cost programmable computer for education, the team created a new category of single-board computers that found a home with enthusiasts and industry, enabling the team to evolve — through hard work and creativity — into a business that is now entering a new phase as a listed company.
They also delivered on the original mission, with computer science at the University of Cambridge now being one of the most oversubscribed undergraduate courses in the country and many applicants citing Raspberry Pi computers as part of their introduction to programming.
The commercial success of Raspberry Pi has enabled the Foundation to expand its educational programmes to the point where it is now established as one of the world’s leading nonprofits focused on democratising access to computing education, and is benefiting the lives of tens of millions of young people already.
It takes a village
While no-one really knows the origin of the proverb ‘It takes a village to raise a child’, we can all recognise the truth in that simple statement. It applies just as much for endeavours like Raspberry Pi.
Over the years, Raspberry Pi has been a genuine team game. Employees in the Foundation and our commercial subsidiary, advisers, partner organisations and supporters, volunteers and community members have all played a crucial role in the success of both the company and the Foundation.
At a Raspberry Pi birthday celebration.
Over the years there have been 21 Trustees of the Foundation, bringing an incredible range of skills and experience that has elevated our ambitions and supported the teams in both the Foundation and the company.
All of our Trustees have provided their time and expertise for free, never receiving any financial benefit for their contribution as Trustees.
Serving as a Trustee of a charity is a serious business, with significant responsibility and accountability. While many charities have commercial operations, there is no doubt that the scale and complexity of Raspberry Pi’s commercial business has placed significant additional responsibilities on all of our Trustees.
I especially want to pay tribute to my predecessors as chair of the Board of Trustees: Jack Lang, one of our founding Trustees, who sadly passed away this year; and David Cleevely, who continues to support our work as a Member of the Foundation. Both Jack and David played a particularly important part in the success of Raspberry Pi.
Welcoming our new Trustees
As we enter this new phase for the Foundation’s relationship with Raspberry Pi Holdings Ltd, we are delighted to welcome three new Trustees to the Board:
Andrew Sliwinski is a VP at Lego Education, formerly co-director of Scratch @ MIT, ex-Mozilla, and founder of DIY.org. Andrew is a technologist and maker with a deep understanding of education systems globally.
Laura Turkington leads global partnerships and programmes at EY, and was previously at Vodafone Foundation. Laura has extensive global experience (including Ireland and India), including supporting large-scale initiatives on digital skills, computing education, and AI literacy.
Stephen Greene is the founder and CEO of Rockcorps and the former chair of the National Citizen Service. Stephen brings huge experience of building global volunteer movements, social enterprise, marketing (especially to young people), government relations, and education of disadvantaged youth.
From the start, Chris had an idea of what he wanted from the device. “I knew what I wanted to achieve – for my friend to press buttons, for virtual dice to be rolled and the result to be announced,” he says. “From there it was just about figuring out which components to use and how to lay them out efficiently. This needed to be hand-held, so a compact design was a must.”
To that end, Chris modelled the device on a gamepad. “I tried to imagine a controller, like an Xbox controller,” he says. “I sought to work out how it would look, feel and where the thumbs would rest. I wanted to make something that was comfortable and felt familiar and I also needed it to operate without being plugged in so I chose a Raspberry Pi Pico and used an 18650 battery shield to power it along with the other components.”
Roll with it
Selecting the Raspberry Pi Pico meant the dice roller could be compact and use minimal power. “It’s instantly on and it was able to handle the different components I needed, such as a microSD card reader, an audio amp with speaker and a litany of buttons,” Chris says.
Those buttons would be 3D printed with Braille so that his friend would be able to read their various functions and control the device effectively and independently. “Adding Braille wasn’t very difficult,” Chris says. “I designed flat button caps then added the Braille numbers to them in Blender, the free open-source 3D computer graphics software.”
Board games often make use of non-traditional dice – in other words, they’re not always familiar cubes with six marked faces. The standard polyhedral dice set also includes those with four, eight, ten, 12 and 20 sides (the entire set being referred to as d4, d6, d8, d10, d12 and d20). “I needed to have a button for each one,” Chris says. He also added two extras: Reset and Roll.
Writing the project in CircuitPython, he created a device that would allow his friend to press one of the numbered buttons to add it to the die pool. When the dice are ready to be rolled, it’d be a case of pressing Roll and listening to the result when it’s announced. “In addition, there are some long press modes,” Chris adds. “If you hold the d4 button for a few seconds, you will enter quiet mode which plays chimes rather than speaking out every die entered and describing what it’s doing. It still announces the results of the roll, but it can cut down on time and I added it in case the normal mode was too distracting to other players at the table.”
Holding down d6 saves the current die pool. “This is useful if you’re playing something like Dungeons & Dragons and constantly need to roll that big 8d6 fireball [that’s eight d6 dice being rolled at once],” Chris continues. “Long-pressing d8 will load the saved die pool from the microSD card, and that means the saved pool will persist across uses.” Long-pressing the d10 enters percentage mode which, rather than rolling dice, will simply give a percentage between one and 100.
Play the game
Coding all of this was a challenge. “Interfacing with the audio amp was tricky and I wish the device was a bit easier to understand in terms of audio quality, but this may have been due to everything being built on a prototype board,” Chris laments. He also found it difficult to perfect the controller shape. “The housing was 3D printed in a single colour and the top was painted red. I had a good friend of mine help with the ergonomics of it. There was also a lot of soldering,” he adds.
Even so, it’s been worth the time and effort. Chris’ friend loves using the device and he finds it allows him to fully participate in games. It’s also comfortable to hold, easy to use and very effective. What’s more, you can replicate it because Chris has made the code open source and he has also shared the 3D files on Printables. In that sense, it’s capable of helping many other people with a visual impairment.
Chris is certainly happy overall even though he would have done some things differently. “If there is one part of this project I wish I had done, it would have been to design a custom PCB using something like KiCad – if only I’d had issue 138 of The MagPi when I started! It probably would have helped a lot with audio issues and made for a cleaner build overall. It’s definitely on my list of things to learn for future projects.”
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