Within just a few years, artificial intelligence systems that sometimes seem to display almost human characteristics have gone from science fiction to apps on your phone. But there’s another AI-influenced frontier that is developing rapidly and remains untamed: robotics. Can the technologies that have helped computers get smarter now bring similar improvements to the robots that will work...
One of our favourite things is 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.
Recently, we had the pleasure of speaking with Isabel, a computer science teacher at Barton Peveril Sixth Form College in Eastleigh, England. She told us her fascinating journey from industry to education, along with how she is helping to make the tech space inviting to all.
Isabel’s path to working in the tech sector started with her early exposure to engineering thanks to her father’s career in telecoms.
“I find this is true for a lot of female engineers my age: you will find that their dad or their uncle was an engineer. I remember that when I made the decision to study engineering, my teachers asked me if I was sure that it was something I wanted to do.”
Isabel pursued a degree in engineering because she loved the technical aspects, and during her studies she found a passion for programming. She went to work as a software engineer in Hampshire, contributing to the development of 3G mobile phone technology.
Despite enjoying her career in tech, Isabel felt a strong pull towards teaching due to her long-standing involvement with youth groups and a desire to give back to the community.
“While I was at university in London, I took part in a scheme where we could go into local primary schools and help with their science teaching. At the time, I just thought this was my way of giving back, I hadn’t really thought of it as a career. But actually, after a while, I thought ‘I’m enjoying this programming, but I really liked helping the young kids as well’.”
The transition wasn’t easy, as Computer Science was not widely taught in schools at the time, but Isabel persevered, teaching IT and Media to her classes as well.
Once Isabel settled into her teaching role, she began thinking about how she could tackle a problem she noticed in the STEM field.
Having experienced first-hand what it was like to be the only woman in STEM spaces, Isabel’s commitment to diversity in technology is at the core of her teaching philosophy. She works hard to create an inclusive environment and a diversity of opportunities in her classroom, making sure girls feel encouraged to pursue careers in tech through exploring various enrichment activities.
Isabel focuses on enrichment activities that bridge the gap between academic learning and real-world application. She runs various projects and competitions, ensuring a balanced representation of girls in these initiatives, and gives her students the opportunity to participate in programs like the Industrial Cadets, Student Robotics, and Coolest Projects.
Isabel told us that she feels these opportunities provide essential soft skills that are crucial for success in any career.
“The A level environment is so academic; it is heavily focused on working on your own on very abstract topics. Having worked in industry and knowing the need to collaborate, I found that really hard. So I’ve always made sure to do lots of projects with my students where we actually work with real engineers, do real-world projects. I believe strongly in teaching soft skills like team working, project management, and time management.”
A key resource in Isabel’s teaching toolkit is the Ada Computer Science platform. She values its reliability and the timely updates to the topics, which are crucial in a rapidly evolving subject like Computer Science.
She said she encourages both her students and fellow teachers, especially those who have retrained in Computer Science, to use the platform as a resource.
“Ada Computer Science is amazing. We know we can rely on saying to the students ‘look on Ada, the information will be correct’ because I trust the people creating the resources. And we even found ourselves as teachers double-checking things on there. We struggle to get Computer science teachers, so actually only two of us are Computer Science teachers, and the other three are Maths teachers we have trained up. To be able to say ‘if you are not sure about something, look on Ada’ is a really nice thing to have.”
Despite her efforts, Isabel acknowledges that progress in getting more girls to pursue tech careers is slow. Many girls still view tech as an uninviting space and feel like they don’t belong when they find themselves as one of a few girls — if not the only one — in a class. But Isabel remains hopeful that continuous exposure and positive experiences can change these perceptions.
“I talk to students who are often the only girl in the class and they find that really hard. So, if at GCSE they are the only girl in the class, they won’t do [the subject] at A level. So, if we leave it until A level, it is almost too late. Because of this, I try as much as I can to get as many girls as possible onto my engineering enrichment projects to show them as many opportunities in engineering as possible early on.”
Her work with organisations like the UK Electronics Skills Foundation reflects her commitment to raising awareness about careers in electronics and engineering. Through her outreach and enrichment projects, Isabel educates younger students about the opportunities in these fields, hoping to inspire more girls to consider them as viable career paths.
As new technology continues to be built, Isabel recognises the challenges in keeping up with rapid changes, especially with fields like artificial intelligence (AI). She stays updated through continuous learning and collaborating with her peers, and encourages her students to be adaptable and open to new developments. “The world of AI is both exciting and daunting,” she admits. “We need to prepare our students for a future that we can hardly predict.”
Isabel’s dedication to teaching, her advocacy for diversity, and her efforts to provide real-world learning opportunities make her an inspiring educator. Her commitment was recognised by the Era Foundation in 2023: Isabel was named as one of their David Clark Prize recipients. The award recognises those who “have gone above and beyond the curriculum to inspire students and showcase real-world engineering in the classroom”.
Isabel not only imparts technical knowledge — she inspires her students to believe in their potential, encouraging a new generation of diverse tech professionals.
If Isabel’s story has inspired you to encourage the next generation of young tech creators, check out the free teaching and training resources we provide to support your journey.
If you are working in Computer Science teaching for learners age 14 and up, take a look at how Ada Computer Science will support you.
The post Celebrating the community: Isabel appeared first on Raspberry Pi Foundation.
Computing today is almost entirely digital. The vast informational catacombs of the internet, the algorithms that power AI, the screen you’re reading this on — all are powered by electronic circuits manipulating binary digits — 0 and 1, off and on. We live, it has been said, in the digital age. But it’s not obvious why a system that operates using discrete chunks of information would be good at...
Can you keep a secret? Modern techniques for maintaining the confidentiality of information are based on mathematical problems that are inherently too difficult for anyone to solve without the right hints. Yet what does that mean when quantum computers capable of solving many problems astronomically faster are on the horizon? In this episode, host Janna Levin talks with computer scientist Boaz...
Computing today is almost entirely digital. The vast informational catacombs of the internet, the algorithms that power AI, the screen you’re reading this on — all are powered by electronic circuits manipulating binary digits — 0 and 1, off and on. We live, it has been said, in the digital age. But it’s not obvious why a system that operates using discrete chunks of information would be good at...
Can you keep a secret? Modern techniques for maintaining the confidentiality of information are based on mathematical problems that are inherently too difficult for anyone to solve without the right hints. Yet what does that mean when quantum computers capable of solving many problems astronomically faster are on the horizon? In this episode, host Janna Levin talks with computer scientist Boaz...
Amanda Randles wants to copy your body. If the computer scientist had her way, she’d have enough data — and processing power — to effectively clone you on her computer, run the clock forward, and see what your coronary arteries or red blood cells might do in a week. Fully personalized medical simulations, or “digital twins,” are still beyond our abilities, but Randles has pioneered computer models...
Theoretical computer scientists deal with complicated ideas. But whenever possible, they’d prefer to work with simpler ones. A 2009 tool known as the regularity lemma gives them a great way to do this. It effectively lets them break a given computational problem or function into simpler pieces. For computational complexity theorists, who study the relative hardness of different problems...
Say you want to send a private message, cast a secret vote or sign a document securely. If you do any of these tasks on a computer, you’re relying on encryption to keep your data safe. That encryption needs to withstand attacks from codebreakers with their own computers, so modern encryption methods rely on assumptions about what mathematical problems are hard for computers to solve.
Imagine that you’re sent to a pristine rainforest to carry out a wildlife census. Every time you see an animal, you snap a photo. Your digital camera will track the total number of shots, but you’re only interested in the number of unique animals — all the ones that you haven’t counted already. What’s the best way to get that number? “The obvious solution requires remembering every animal you’ve...
Imagine you had a friend who gave different answers to the same question, depending on how you asked it. “What’s the capital of Peru?” would get one answer, and “Is Lima the capital of Peru?” would get another. You’d probably be a little worried about your friend’s mental faculties, and you’d almost certainly find it hard to trust any answer they gave. That’s exactly what’s happening with many...
Imagine you had a friend who gave different answers to the same question, depending on how you asked it. “What’s the capital of Peru?” would get one answer, and “Is Lima the capital of Peru?” would get another. You’d probably be a little worried about your friend’s mental faculties, and you’d almost certainly find it hard to trust any answer they gave. That’s exactly what’s happening with many...
Enlarge (credit: boonchai wedmakawand)
Reinforcement-learning algorithms in systems like ChatGPT or Google’s Gemini can work wonders, but they usually need hundreds of thousands of shots at a task before they get good at it. That’s why it’s always been hard to transfer this performance to robots. You can’t let a self-driving car crash 3,000 times just so it can learn crashing is bad.
But now a team of researchers at Northwestern University may have found a way around it. “That is what we think is going to be transformative in the development of the embodied AI in the real world,” says Thomas Berrueta who led the development of the Maximum Diffusion Reinforcement Learning (MaxDiff RL), an algorithm tailored specifically for robots.
The problem with deploying most reinforcement-learning algorithms in robots starts with the built-in assumption that the data they learn from is independent and identically distributed. The independence, in this context, means the value of one variable does not depend on the value of another variable in the dataset—when you flip a coin two times, getting tails on the second attempt does not depend on the result of your first flip. Identical distribution means that the probability of seeing any specific outcome is the same. In the coin-flipping example, the probability of getting heads is the same as getting tails: 50 percent for each.
Physicists have done a remarkable job explaining the chaos of the universe with well-behaved equations, but certain situations remain mysterious. Among these are collections of many tiny particles — they can be atoms, electrons, anything sufficiently small — that interact in surprising and complicated ways. These interactions give rise to exotic quantum phenomena including superconductivity (in...
Start talking to Ellie Pavlick about her work — looking for evidence of understanding within large language models (LLMs) — and she might sound as if she’s poking fun at it. The phrase “hand-wavy” is a favorite, and if she mentions “meaning” or “reasoning,” it’ll often come with conspicuous air quotes. This is just Pavlick’s way of keeping herself honest. As a computer scientist studying language...
What’s the best way to solve hard problems? That’s the question at the heart of a subfield of computer science called computational complexity theory. It’s a hard question to answer, but flip it around and it becomes easier. The worst approach is almost always trial and error, which involves plugging in possible solutions until one works. But for some problems, it seems there simply are no...
What’s the best way to solve hard problems? That’s the question at the heart of a subfield of computer science called computational complexity theory. It’s a hard question to answer, but flip it around and it becomes easier. The worst approach is almost always trial and error, which involves plugging in possible solutions until one works. But for some problems, it seems there simply are no...
As educators, it’s important that we showcase the wide range of career opportunities available in the field of computing, not only to inspire learners, but also to help them feel sure they’re choosing to study a subject that is useful for their future. For example, a survey from the BBC in September 2023 found that more than a quarter of UK teenagers often feel anxious, with “exams and school life” among the main causes. To help young people chart their career paths, we recently hosted two live webinars for National Careers Week in the UK.
Our goal for the webinars was to highlight the breadth of careers within computing and to provide insights from professionals who are pursuing their own diverse and rewarding paths. Each webinar featured engaging discussions and an interactive Q&A session with learners who use our Ada Computer Science platform. The learners could ask their own questions to get firsthand knowledge and perspectives from our guest speakers.
Jess Van Brummelen is a Human–Computer Interaction Research Scientist at Niantic, the video games company behind augmented reality game Pokémon Go. After developing an interest in programming during her undergraduate degree in mechanical engineering, she went on to complete a Master’s degree and PhD in computer science at MIT.
Ashley Edwards is a Senior Research Scientist at Google DeepMind, working on reinforcement learning. She received her PhD in 2019 from Georgia Tech, spent time as an intern at Google Brain, and worked as a research scientist at Uber AI Labs.
You can read extracts from our interviews with Jess and Ashley and watch the full videos below. Teachers have contacted us to say they’ll be using the webinars for careers-focused sessions with their students. We hope you will do the same!
Please note that we have edited the extracts below to add clarity.
Hi Jess. What advice would you give to a student who is thinking about a career in human–computer interaction in the gaming industry?
In terms of HCI and gaming, I’d actually recommend that you keep gaming! It’s a small part of my job but it’s really important to understand what’s fun and enjoyable in games. Not only that; gaming can be great for learning to problem-solve — there’s been all sorts of research on the positive impact of gaming.
A second thing, going back to how I felt in my mechanical engineering classes, I really felt like an ‘other’ and not someone who is the standard computer scientist or engineer. I would encourage students to pursue their dreams anyway because it’s so important to have diversity in these types of careers, especially technology, because it goes out to so many different people and it can really affect society. It’s really important that the people who make it come from many different backgrounds and cultures so we can create technology that is better for everyone.
[From Owen, a student on the livestream] What’s the most impossible idea you’ve come up with while working at Niantic?
I’m currently publishing a paper addressing the question, ‘Can we guide people without using anything visual on their phone?’ That means using audio and haptic (technology that transmits information via touch, e.g. vibrations) prompts instead. We tried out different commands where the phone said ‘turn left’ and ‘turn right’, but we really wanted to test how to guide someone more specifically in a game environment. For example, if there was a hidden object on a wall in a game that a person couldn’t see, could we guide them to that object while they’re walking? So I ran a study where I guided people to scan a statue by moving around it. Scanning is the process of using the camera on your phone to scan an object in real life, which is then reconstructed on your phone. Scanning objects can trigger other augmented reality experiences within a game. For example, you might scan a real-life box in a room and this might trigger an animation of that box opening to reveal a secret within the game. We tested a lot of different things. For example, test subjects listened to music as they were walking and when they were on the right path, the music sounded really good. But when they were off the path, it sounded terrible. So it helped them to look for the right path. Then if you were pointing the phone in the wrong direction for scanning objects, you would get warning vibrations on the phone. So we did the study and we were hoping it would improve safety. It turns out it was neutral on improving safety — I think this is because it was such a novel system. People weren’t used to using it and still bumped into things! But it did make people better at scanning the objects, which was interesting.
Watch Jess’s full interview:
Hi Ashley. Is there something you studied in school that you found to be more useful now than you ever thought it would be?
Maths! I always enjoyed doing maths, but I didn’t realise I would need it as a computer scientist. You see it popping up all the time, especially in machine learning. Having a strong knowledge of calculus and linear algebra is really helpful.
How do you train an AI model using machine learning?
You start by asking the question, ‘What is the problem I’m trying to solve?’ Then typically you need input data and the outputs you want to achieve, so you ask two more questions, ‘What data do I want to come in?’ and ‘What do I want to come out?’ Let’s say you decide to use a supervised learning model (a category of machine learning where labelled data sets are used to train algorithms to detect patterns and predict outcomes) to predict whether a photo contains a cat. You train the model using a giant set of images with labels that say either ‘This is a cat’ or ‘This isn’t a cat’. By training the model with the images, you get to a point where your model can analyse the features of any image and predict whether it contains a cat or not.
In my field of research, I work on something called reinforcement learning, which is where you train your model through trial and error and the use of ‘rewards’. Let’s imagine we are trying to train a robot. We might write a program that tells the robot, ‘I am going to give you a reward if you take the right step forward and it’s going to be a positive reward. If you fall over, I’m going to give you a negative reward.’ So you train the robot to prioritise the right behaviours to optimise the rewards it’s getting.
[From a student] Will I still need to learn to code in the future?
I think it is going to be very different in the future, but we’ll still need to learn how to build different types of algorithms and we’re going to need to understand the concepts behind coding as well. We’ll still need to ask questions like, ‘What is it that I want to build?’ and ‘Is this actually doing the correct thing?’
Watch Ashley’s full interview:
Jess and Ashley are forging successful careers not only through a combination of smart choices, hard work, talent, and a passion for technology; they also had access to opportunities to discover their passion and receive an education in this field. Too many young people around the world still don’t have these opportunities.
That is why we provide free resources and training to help schools broaden access to computing education. For example, our free learning platform, Ada Computer Science, provides students aged 14 to 19 with high-quality computing resources and interactive questions, written by experts from our team. To learn more, visit adacomputerscience.org.
The post Careers in computer science: Two perspectives appeared first on Raspberry Pi Foundation.
We offer Ada Computer Science as a platform to support educators and learners alike. But we don’t take its usefulness for granted: as part of our commitment to impact, we regularly gather user feedback and evaluate all of our products, and Ada is no exception. In this blog, we share some of the feedback we’ve gathered from surveys and interviews with the people using Ada.
Ada Computer Science is our online learning platform designed for teachers, students, and anyone interested in learning about computer science. If you’re teaching or studying a computer science qualification at school, you can use Ada Computer Science for classwork, homework, and revision.
Launched last year as a partnership between us and the University of Cambridge, Ada’s comprehensive resources cover topics like algorithms, data structures, computational thinking, and cybersecurity. It also includes 1,000 self-marking questions, which both teachers and students can use to assess their knowledge and understanding.
Throughout 2023, we continued to develop the support Ada offers. For example, we:
A few weeks ago we launched two all-new topics about artificial intelligence (AI) and machine learning.
So far, all the content on Ada Computer Science is mapped to GCSE and A level exam boards in England, and we’ve just released new resources for the Scottish Qualification Authority’s Computer Systems area of study to support students in Scotland with their National 5 and Higher qualifications.
Ada is being used by a wide variety of users, from at least 127 countries all across the globe. Countries where Ada is most popular include the UK, US, Canada, Australia, Brazil, India, China, Nigeria, Ghana, Kenya, China, Myanmar, and Indonesia.
Just over half of students using Ada are completing work set by their teacher. However, there are also substantial numbers of young people benefitting from using Ada for their own independent learning. So far, over half a million question attempts have been made on the platform.
Students use Ada for a wide variety of purposes. The most common response in our survey was for revision, but students also use it to complete work set by teachers, to learn new concepts, and to check their understanding of computer science concepts.
Teachers also use Ada for a combination of their own learning, in the classroom with their students, and for setting work outside of lessons. They told us that they value Ada as a source of pre-made questions.
“I like having a bank of questions as a teacher. It’s tiring to create more. I like that I can use the finder and create questions very quickly.” — Computer science teacher, A level
“I like the structure of how it [Ada] is put together. [Resources] are really easy to find and being able to sort by exam board makes it really useful because… at A level there is a huge difference between exam boards.” — GCSE and A level teacher
Students and teachers alike were very positive about the quality and usefulness of Ada Computer Science. Overall, 89% of students responding to our survey agreed that Ada is useful for helping them to learn about computer science, and 93% of teachers agreed that it is high quality.
“The impact for me was just having a resource that I felt I always could trust.” — Head of Computer Science
Most teachers also reported that using Ada reduces their workload, saving an average of 3 hours per week.
“[Quizzes] are the most useful because it’s the biggest time saving…especially having them nicely self-marked as well.” — GCSE and A level computer science teacher
Even more encouragingly, Ada users report a positive impact on their knowledge, skills, and attitudes to computer science. Teachers report that, as a result of using Ada, their computer science subject knowledge and their confidence in teaching has increased, and report similar benefits for their students.
“They can easily…recap and see how they’ve been getting on with the different topic areas.” — GCSE and A level computer science teacher
“I see they’re answering the questions and learning things without really realising it, which is quite nice.” — GCSE and A level computer science teacher
Our content team is made up of experienced computer science teachers, and we’re always updating the site in response to feedback from the teachers and students who use our resources. We receive feedback through support tickets, and we have a monthly meeting where we comb through every wrong answer that students entered to help us identify new misconceptions. We then use all of this to improve the content, and the feedback we give students on the platform.
We’ll be conducting another round of surveys later this year, so when you see the link, please fill in the form. In the meantime, if you have any feedback or suggestions for improvements, please get in touch.
And if you’ve not signed up to Ada yet as a teacher or student, you can take a look right now over at adacomputerscience.org
The post How we’re creating more impact with Ada Computer Science appeared first on Raspberry Pi Foundation.
Computer scientists are a demanding bunch. For them, it’s not enough to get the right answer to a problem — the goal, almost always, is to get the answer as efficiently as possible. Take the act of multiplying matrices, or arrays of numbers. In 1812, the French mathematician Jacques Philippe Marie Binet came up with the basic set of rules we still teach students. It works perfectly well...
A team of computer scientists has created a nimbler, more flexible type of machine learning model. The trick: It must periodically forget what it knows. And while this new approach won’t displace the huge models that undergird the biggest apps, it could reveal more about how these programs understand language. The new research marks “a significant advance in the field,” said Jea Kwon...
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