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Nvidia's 64GB DGX Spark launches at $4,999 with the same GB10 chip as the 128GB version. Compare memory limits, model support, clustering costs, and what IT buyers should verify.
The post Nvidia DGX Spark 64GB Launches at $4,999: Is It Worth It? appeared first on TechRepublic.
Google Cloud’s London outage disrupted banking apps and business services. Learn what happened and what IT teams should check after service recovery.
The post Google Cloud London Outage Disrupts Banking and Business Services appeared first on TechRepublic.
Running complicated algorithms on quantum computing hardware is going to require the use of logical qubits, where collections of hardware qubits are linked together in ways that allow errors to be detected and corrected. In general, the more hardware qubits you dedicate to each logical qubit, the better the chances are of identifying errors. But, given that the hardware qubits are prone to errors, adding more of them also increases the probability that errors will occur. If the hardware error rate is too high, then growing the logical qubit will only make matters worse.
Fortunately, several companies have demonstrated hardware where the error rate is not too high. Several demonstrations show logical qubits are beyond the breakeven point, where adding more qubits improves performance. The problem is that the improvement isn't very dramatic. To lower error rates to levels we'll need to run complex algorithms, we'd have to add a completely impractical number of qubits to each logical qubit.
On Monday, Quantinuum announced that its current generation of hardware is capable of scaling well beyond the breakeven point. Each step up in the size of logical qubits improved some measures of their performance by a factor of over 10.
Google’s Willow processor cuts magic-state errors 40-fold. Learn what the result means for quantum computing costs, scaling, and enterprise planning.
The post Google Willow Cuts Magic-State Errors 40-Fold appeared first on TechRepublic.
Liquid AI releases an open-weight model for local AI agents. Explore its reported performance, hardware requirements, privacy risks and licensing limits.
The post Liquid AI Releases LFM2.5-24B-A2B for Local AI Workflows appeared first on TechRepublic.
Nvidia commits $1 billion over five years to support US science, quantum computing and AI research, expanding its role in the federal Genesis Mission.
The post Nvidia’s $1 Billion Research Plan Targets AI, Quantum Computing and Medicine appeared first on TechRepublic.
The Dutch Tax and Customs Administration is reversing a planned Microsoft 365 cloud migration as officials seek greater control over sensitive government data.
The post Dutch Tax Agency Reverses Microsoft 365 Cloud Migration Over Data Risks appeared first on TechRepublic.
Computing education in England is in a stronger position than it was a decade ago. Increased access to technology, free learning resources for young people, and improved training for computing teachers has put us on a path to success.
But the subject is also at an important inflection point. Young people are growing up in a world shaped by digital technologies like AI, yet not all young people get the same opportunities to discover how computing can be creative, collaborative, and relevant to their lives. This is reflected in the number of learners who choose to study it. In England, GCSE Computer Science entries fell by 4.7% in summer 2025 (93,980 to 89,610), and A level entries fell by 2.4% (19,475 to 19,010)*.
Creating positive, accessible experiences early in a students’ education, and helping them to build confidence and see computing as something they can engage with, has never been more important.
For educators, computing is about much more than preparing learners for a single subject or qualification. Skills England’s 2025 assessment of priority skills up to 2030 estimates that demand for priority occupations will grow by 0.9 million, including 87,000 additional programmers and software development professionals**. These roles are identified as a priority across seven industry sectors, underlining the value of the broader skills students develop through computing: problem-solving, logical reasoning, creativity, and confidence.
That’s why initiatives such as the UK Bebras Challenge and the Raspberry Pi Foundation Coding Challenge matter. They give teachers ready-made, engaging ways to introduce computational thinking and coding without needing students to already see themselves as ‘computer scientists’. Low-barrier, fun, and practical, they help students experience the satisfaction of solving problems for themselves. As Mitchel Resnick, Professor of Learning Research at the MIT Media Lab, puts it: “They are not just learning to code, they are coding to learn.”***
Over the past year, the UK Bebras Challenge (November 2025), and the Raspberry Pi Foundation Coding Challenge (March 2026), have reached hundreds of thousands of students across the UK.
Participation continues to grow. In 2025, 562,924 students took part in Bebras, up from 467,000 in 2024, contributing over 1 million hours of learning. The Coding Challenge also grew, with 85,389 students taking part in 2026, up from 83,096 in 2025.
To understand what this looks like at the grassroots level, we spoke to two teachers about why they run these challenges and what their students gain from taking part.
I was introduced to the Bebras Challenge during my teacher training, where my placement school was already taking part in the competition. Not only was it an affordable way to introduce students to the world of computational thinking, but I quickly realised how useful it was in identifying which students would be a good fit for GCSE Computer Science. Since my initial positive experience, I’ve ensured that all computer science students take part in the challenge, no matter which school I’ve worked in.
From an admin perspective, it is quick and easy to create accounts for a large number of students in one go. Once student data is downloaded from your MIS, importing the spreadsheets is incredibly simple. As a busy teacher, this saves me a lot of time.
For students, the accessibility of Bebras is a big selling point. Due to the on-screen reader, ability to add extra time for SEND students and differentiated challenges, everyone can access the competition. The interactive elements within the questions also make it engaging and enjoyable to work with.
Students can be easily influenced by their teachers. When introducing the Bebras Challenge every year, it’s important to clarify its purpose and the reason behind taking part. With a genuine motive explained, I find students are receptive to attempting something new. Every individual challenge is unique and colourful, operating a simple UI to jump between questions. Students are spurred on to get stuck into the puzzles on their first encounter, responding to the tasks with confidence.
The number one skill that students develop is their ability to problem-solve. When faced with tasks of varying difficulties, they are able to apply the key principles of abstraction and decomposition to tackle problems. If I’m with a GCSE or A Level class, it presents as the perfect opportunity to reinforce their theory knowledge, linking computational thinking concepts to the question in front of them.
Additionally, it can promote the hard skill of literacy. By having separate competitions for different year groups, students are reading texts that are of an appropriate level for their age bracket. And if you’re in a primary school, there’s the option to work in groups, allowing students to develop another soft skill of teamwork.
I’ve previously taught a Year 9 student with a reading age of 10, who was finding all aspects of school a challenge. Utilising the adaptive technology available to them in the Bebras Challenge, they were one of few in their class to achieve a Merit. This student was able to showcase their strong logical reasoning skills, without being hindered by their reading comprehension.
Consequently, they chose Computer Science as one of their GCSE options and wanted to pursue a career as a software developer. It also raised an interesting discussion point with the school, investigating various access arrangements to better support them. The Bebras Challenge has quite literally changed this student’s life.
Just go for it! Given how renowned the competition is, my sixth form students have mentioned their results in university and apprenticeship applications. If students are needing evidence to back up their problem-solving abilities, or wanting to differentiate themselves from others with similar grades, the UK Bebras Challenge gives them an extra string to their bow.
As a teacher, just make sure you’re organised and prepare the accounts well in advance. Once the student accounts have all been created, using the helpful Coordinator Handbook as guidance, you can export their details and create login slips. Coupled with Bebras’ instructions presentation for in-class, it made for a smooth set-up and delivery. As the competition is straight after October half-term, it also allows teachers to ease their way back into school life. We always look forward to Bebras week at my school!
We are always looking for more competitions for students to participate in. We run a few competitions, but most are for the elite only. For example, we run the British Informatics Olympiad and British Algorithmic Olympiad but only about 20 of our 340 students can attempt either.
So when we heard about the Coding Challenge, it was exactly what we were looking for, a competition where everyone could take part and achieve good results. It’s always good to practise more coding skills, so this competition is ideal for us. This year was the first time we had the whole cohort take part, both Year 12 and Year 13.
The vast majority of students are excited about the Coding Challenge. They love competition!
The very first time they see them, it can be a bit overwhelming. There is a lot to do in a short time, which is why we get students to practise before the actual competition.
It has always been about problem solving. This is a skill that is still very much in demand, perhaps more so now. We are still teaching computational thinking when teaching them to code. There are many transferable skills from learning to code that are useful in most careers and industries. The need for resilience and attention to detail are vital skills for all.
It develops resilience and many other skills such as following instructions precisely, which are important in life. Also time management and planning, students need to decide what to try and in what order if they want to maximise their score.
Quite a few students will continue to work on the problems they encountered after the challenge has finished. They are determined to solve the problems! I hear many groups discussing techniques they used to solve the trickier problems which gives them the confidence to use those techniques in their classwork.
Do it! It is so easy to do. We run it in a normal lesson; it is in our schedule of lessons so all teachers do it the same week. From two weeks before the day we choose to set the challenge, we set homework to be practice questions.
The tricky part is making sure the students understand the interface, what code to copy in and how to test and run it, so the homework gives them a chance to do this. One teacher sets up the spreadsheet with all the student names to import to the very easy-to-use teacher admin website. It is then easy to download each class’s usernames and passwords.
Across both challenges, one thing is clear: when barriers are lowered, more students take part and discover that computing is for them. Whether through short problem-solving tasks or coding challenges, these experiences build confidence, develop transferable skills, and help learners see themselves in computing.
For teachers, they also offer something equally important: practical, structured activities that can fit into the school year and work with whole classes or cohorts. In a landscape where the UK needs more young people to develop digital and computational skills, accessible challenges like these can help spark interest early, celebrate success, and give every learner a meaningful first step into computing.
To register your school for the next UK Bebras Challenge coming in November 2026, go to bebras.uk.
References
***https://web.media.mit.edu/~mres/papers/L2CC2L-handout.pdf
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Amazon is reportedly considering an $8 billion Nvidia chip sale-leaseback as it looks for new ways to finance its massive AI infrastructure buildout.
The post Amazon’s $8 Billion Nvidia Chip Plan Shows the Cost of the AI Boom appeared first on TechRepublic.
AI technologies, such as smart speakers or streaming recommendations, are becoming part of everyday life for children and teenagers. It’s therefore increasingly important to help young people understand not just how these systems work, but how to question them.
When AI systems generate outputs such as a recommendation, these outputs are often accompanied by an explanation. In our latest research seminar, Professor Dr. Dan Verständig (Goethe University Frankfurt, Center for Critical Computational Studies) spoke about how explanations are shaped by the people who write them, and only become meaningful when someone else makes sense of them. Dan introduced us to Social Explainable AI (Social XAI) and Critical Computational Literacy (CCL), and asked us to consider not just what an AI system explains, but for whom, why, and who benefits.
Dan opened with a deceptively simple question: why do we need explanations in the first place? His answer was that explanations provide orientation. They reduce uncertainty, and in doing so, they make our shared social world liveable. Explanations have evolved from Socratic dialogue, to the printed book, to the classroom, to today’s digital interfaces, and now to AI-generated explanations. But explanations have never been neutral conveyors of information. Dan illustrated this with a simple demonstration: he showed the seminar participants an aerial photo of a beach and asked what they noticed.
The aerial photo Dan shared during his seminar. What do you notice about this picture?
Seminar participants pointed to the coastline, the sandy beach, and a tiny figure in the frame. Dan then revealed that the photo was taken by him, using a drone at Montara State Beach in California on a specific afternoon in April 2024. He asked how a climate researcher, a surfer, or an artist might each describe the very same image differently.
This demonstration highlighted that an explanation is always for someone, for some purpose, in some context. That framing carries straight through into how we should think about AI explanations too. An explanation that satisfies a data scientist debugging a model is not the same as one that satisfies a patient asking why an algorithm flagged their scan, or a citizen asking why they were denied a loan. In other words, explanations are never technical.
Drawing on Rohlfing and Lim’s 2026 work, Dan described Social XAI as an approach that puts interaction, rather than output, at the centre of explainability. Dan illustrated this with a simple but effective diagram: an AI explanation starts as a system output, is filtered through a person’s interpretation, and only then becomes meaning through construction. A one-size-fits-all output, however technically accurate, isn’t yet an explanation until someone has made sense of it in their own terms.
To explore this further, Dan’s research group has developed co-construction workshops, bringing people together to interrogate AI explanations collaboratively rather than receive them passively. In these workshops, participants are asked questions such as: “What counts as evidence? What is missing? What are the alternatives? Who benefits from this? Do we agree?”
To answer these questions, participants do not examine an AI model to find out how it generates a decision. Instead, participants scrutinise its outputs in the same way that they would critically evaluate a politician’s promise or a newspaper’s headline.
Critical Computational Literacy (CCL) is a framework that describes what people need to engage with AI explanations critically. Dan presented CCL as consisting of four interlocking dimensions:
Taken together, these dimensions push back against a narrow, purely technical notion of ‘AI literacy’ as knowing how a model works. Instead, CCL treats literacy as something biographical and value-laden. Dan explained that individuals bring their own history and stance to any encounter with computational systems, and genuine literacy means being able to interrogate these systems’ explanations rather than simply accept or operate them.
Although Dan’s research took place with adult participants, Social XAI and Critical Computational Literacy are ideas that could also be used in the K-12 classroom. For example, if students interact with AI explanations through smart speakers or streaming recommendations in their everyday lives, this presents an opportunity to teach them how to engage critically with AI outputs. Students might explore why a smart speaker suggested a particular recipe, or investigate the explanation for why a streaming service recommended a particular show.
Explainability is also a key part of our own Experience AI resources. When training a model, students write their own model cards to document who built a model, what training data was used, how accurate the model’s predictions were, and any known limitations. In this activity, explainability is traceable, and students use their analytical skills to create transparent, fair, and accountable model cards.
Social XAI suggests an extension to this activity. Students could ask what matters about the explanation they have written and why this is important. Through this critique, students can consider who will read these model cards and how the cards might be interpreted. In this way, AI explanations become more than a technical output, and become artefacts whose meaning is co-constructed by the author and the reader.
This seminar was part of our ongoing series on teaching about AI in the arts, humanities, and sciences. You can watch the full recording of Dan’s talk, including the Q&A discussion, here:
Our research seminar series continues to explore how AI is taught across the curriculum. In our next seminar on Tuesday, 6 October at 19:00–20:30 BST, we welcome Eleni Petraki and Damith Herath (University of Canberra) who will present an engineering and robotics curriculum aimed at equipping future engineers with the diverse skills demanded by a growing workforce. To take part in the seminar, click the button below to register. We hope to see you there.
The schedule of our upcoming seminars is available online. You can catch up on past seminars on the blog and on the previous seminars and recordings page.
The post Not all explanations are equal: Social Explainable AI and Critical Computational Literacy appeared first on Raspberry Pi Foundation.
West Java has ordered BDx to halt work on its 640MW AI data center until required building permits and environmental approvals are completed.
The post Indonesian Governor Orders Halt to BDx AI Data Center Over Missing Permits appeared first on TechRepublic.
Cloudflare said Tuesday it plans to issue quantum-proof TLS certificates, making it one of the first authorities to issue such certificates that use a form of cryptography that is widely believed to withstand attacks from quantum computers.
The Internet infrastructure provider said it will use an open source platform that issues both classic TLS certificates and a post-quantum equivalent known as Merkle Tree Certificates. The hybrid certificates will be free to both paying and non-paying users. To help build the massive system and establish ubiquity across the sprawling TLS ecosystem, Cloudflare will be acquiring an already trusted certificate root from CA GlobalSign. Cloudflare said the move will let millions of websites use post-quantum certificates at the flip of a switch and without incurring any increased performance overhead.
Cloudflare’s plans are part of a major overhaul in the web public key infrastructure (WebPKI) required to make website encryption and authentication safe for the coming post-quantum age. A major challenge is using quantum-proof signatures that can be easily transmitted during web requests and recorded in transparency logs to ensure counterfeit certificates aren't assigned to websites. The makeover will take years to complete, because it requires the work of an untold number of engineers who design operating systems, browsers, certificate authorities, and Internet infrastructure.
Imagine this scenario:
Alex, a teenaged learner, reads a homework assignment, ponders a little, and then opens an AI chatbot. A quick prompt produces a structured, well-written answer within seconds. Alex pastes the text into a document, tweaks a few words, and submits the work.
For many young people, this is becoming the norm.
Recent reports show that, in countries including the US and UK, over half of all teens use AI tools for homework, and 1 in 10 says they do most or all of their homework with chatbots. “I use it every day,” stated one 17-year-old student in a recent study (Pew Research, 2026), and went on to say how she relies on chatbots for everything from homework to life decisions. In another study, a teenager openly admitted to using AI to cheat on assignments, essays, and book reports (Harvard GSE, 2024).
This raises an important question: when AI technology does the work, what happens to the learning?
Researchers are increasingly concerned that such uncritical use of AI tools may lead to cognitive offloading, where learners outsource their thinking, and the weakening of higher-order thinking skills in young people, such as problem-solving, critical thinking and creativity.
To address these concerns, the Raspberry Pi Foundation and Google DeepMind have teamed up to create a new unit of research-informed lessons on large language models (LLMs) as part of Experience AI. Rather than focusing simply on how to use AI tools, the new unit helps learners understand how LLMs work, evaluate their outputs, and use them strategically to support their own learning.
The unit, designed for 13- to 16-year-old learners, supports a learning-focused approach, drawing on metacognition, critical thinking, feedback literacy, and learning-centered prompt engineering. The five lessons in it support young people’s cognitive development, strengthen their metacognitive skills, and bolster their ability to strategically use LLMs for their learning. They draw on extensive research within Google DeepMind’s Learning team on how to scaffold around AI use to preserve ‘productive struggle’ and learner agency.
The new ‘Using LLMs strategically for learning’ unit is grounded in learning science research, including feedback literacy. Feedback literacy refers to the skill of actively interpreting, judging and using information when learning, instead of passively receiving information.
In the unit, learners explore three types of information, also called ‘feedback types’:
Learners discover that deep learning requires a blend of all three feedback types. They also analyse LLM outputs and discover that LLMs default to providing the answer, usually in a ‘tell’ format.
To help learners strategically use LLMs for their learning, they are provided with prompting strategies that elicit LLM outputs in ‘guide’ and ‘challenge’ formats. This helps learners engage more deeply with their own learning and develop higher-order thinking skills.
To help young people strategically use LLMs for their learning, the lessons include learning-centred, platform-agnostic prompting techniques. These include strategies borrowed from research papers on the most effective prompting frameworks. To make sure that the prompting strategies in the lessons remain effective over time and can be translated into different languages when we expand the resources, we did not use acronyms, unlike most popular prompting frameworks.
There are several industry reports highlighting the future-proof skills in the age of AI, such as problem solving, communication, critical thinking, collaboration and creativity skills. In the ‘Large language models’ unit, learners reflect on which skills they want to build and consider how their use of LLMs can support or hinder their development, helping them make responsible and intentional choices about using LLMs in ways that build their long-term capabilities rather than replace them.
There are also activities in the lesson resources that help learners understand the long-term impacts of cognitive offloading and uncritical LLM use on the development of their future skills. The resources encourage learners to stay actively engaged in their learning and be reflective of their LLM use when learning.
Many young people (Pew Research, 2026) use LLMs as search engines, believing that LLMs produce outputs that are always accurate. To address that misconception, the lessons include activities that question where the training data comes from, and how accurate those sources of data are.
For example, when young people discover that a portion of data that LLMs are trained on come from social media platforms like Reddit and Facebook, they are encouraged to question the quality and factuality of these sources through classroom-wide discussions. Through discussions and thinking exercises, learners are also encouraged to question whose voices, languages, cultures and perspectives are (and are not) represented in these AI models.
As AI tools become more integrated in young people’s lives, many learners, like Alex, are tempted to turn to AI tools for quick answers, convenience, and support.
The ‘Using LLMs strategically for learning’ unit equips young people with the skills to use LLMs effectively, critically and strategically, in ways that are most helpful to their learning. Through research-informed lessons, they explore how LLMs are created, why their outputs are not always accurate and how to evaluate AI-generated responses.
Just as importantly, learners are encouraged to reflect on their own use of AI: when using an LLM supports their learning, when it does not, and how they can remain in control of their thinking, learning and skills development.
With these resources, young people like Alex are not discouraged from using LLMs, but encouraged to pause, rewrite their prompts in ways that are more helpful to their learning, evaluate the outputs they receive, and stay actively involved in their own thinking.
As we prepare young people for a future we do not yet know, the most important skill we can teach them is how to keep thinking. Who does the thinking, gets the learning.
The unit is available on the Experience AI website.
We would like to thank all the educators across South Africa, Nigeria, Kenya, the United Kingdom, India and beyond who piloted these resources and shared their thoughtful feedback. Their contributions helped shape and strengthen the unit. We are also grateful to Google DeepMind for their continued support in the development of this unit.
The post Encouraging learners to think first, prompt second: Using large language models to learn appeared first on Raspberry Pi Foundation.
Nine courses and 168 hours of video cover Tech+, A+, Network+, Security+, Cloud+, Pentest+, and more exams.
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AQSolotl has opened a Johor Bahru office for quantum-control hardware, strengthening Malaysia’s ties to Singapore’s quantum research ecosystem.
The post Singapore Quantum Startup AQSolotl Opens Johor Base in Malaysia’s Hardware Push appeared first on TechRepublic.
Apple Vision Pro costs $3,499, while Meta VR Glasses will cost $1,299.99. Compare price, comfort, displays, work, gaming, and availability.
The post Apple Vision Pro vs Meta VR Glasses: Is Meta Worth the Wait? appeared first on TechRepublic.
Meta's $1,299 VR Glasses combine 5K displays, AI, gesture controls and virtual desktops in a lighter headset powered by a separate compute puck.
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The EU and Philippines are nearing a trade pact that could reshape digital commerce, cloud access, and public-sector tech procurement.
The post EU-Philippines Trade Deal Nears Final Agreement, but Key Digital Rules Remain Unpublished appeared first on TechRepublic.
Indonesia’s One Data Bill puts cross-border data transfers, data sovereignty, and overseas processing rules under formal negotiation.
The post Indonesia Weighs New Cross-Border Data Rules in One Data Bill Talks appeared first on TechRepublic.
Walk into Allison Knoph’s fifth grade classroom in Edina, Minnesota in October and the creative writing station will be the loudest place in the room.
That is by design. “I use it at my creative writing station,” she says of Experience CS, which she has run each fall for the past couple of years. Kids use the station computers to be creative as they work on their programming projects, building characters and testing jokes they have written. Some laugh hard enough that a visitor might wonder if anyone is working.
“Giggling,” Allison says, “is a good problem to have.”
Experience CS is a standards-aligned computer science curriculum for grades 3 to 8 (ages 8 to 14) that integrates computing into core subjects like math, science, and art. Allison has completed two units with her students, The me project, designed for ages 9 to 10, and Ecosystems, designed for ages 12 to 13.
Of the two units, the storywriting-themed The me project was the stronger fit. It mapped cleanly onto language arts work her students were already doing. Students hit the signposts the unit lays out, but what they did inside that structure was theirs. One wrote the minimum and moved on. Another built a multi-scene story with custom sound effects, spending three weeks developing their project and making improvements.
The Ecosystems unit was more challenging for her students, and the students who finished “were the ones who were kind of the more gung-ho ones that were really into it.” She is not dismissing it, but she is clear that placement matters.
Allison highlighted that some of the students who were thriving in the Experience CS lessons were students who were experiencing difficulties in other areas of the curriculum. “The kids who don’t normally shine often do here,” she says. Describing one experience from her classroom, she shared that a student who had difficulties with reading quickly understood how a program should be sequenced. Within a week, classmates were walking to that desk for help. The peer support model is not something she engineers. It builds itself.
Running the lessons in October sets a tone she draws on all year. Something breaks, a student gets stuck, and the answer in her room becomes “We can figure it out.” That carries into math, into writing, into everything after.
Allison is not arguing that every student will grow up to write code. Her argument is that computational thinking transfers, and that it looks a lot like the writing instruction she is already doing: syntax, structure, sequence. Debugging a story and debugging a program are closer cousins than most people think.
In the age of AI, computational thinking and coding skills are especially important. Access to AI tools makes it easier for more people to generate code. Fewer people can read it, judge whether it is any good, and change it. Allison saw this on her son’s robotics team: the students who understood their code could adapt it when the robot did something unexpected. The ones who had generated code and dropped it in could not. Same tools, different outcomes, and the difference was comprehension.
Minnesota ranks near the bottom nationally for computer science access. In the most recent state-by-state accounting, 34 percent of its public high schools offered a foundational CS course, against a national average of 60 percent.
As a fifth grade teacher, Allison believes that if students may not have opportunities to learn CS in high school, the introduction has to happen earlier, in a classroom not labeled computer science.
She is candid that the barrier for elementary teachers is not interest. It is time, cost, and the fear of being asked a question they cannot answer. She sums up the Experience CS offering to her colleagues in a few words: free, little direct instruction, fits standards she is already teaching, and the kids like it enough to be loud about it.
“We can figure it out” is a good thing for a teacher to say out loud. It is a better thing for a room of ten-year-olds to start saying back.
Find out more about introducing Experience CS in your classroom: head to experience-cs.org today.
The post “We can figure it out”: How one Minnesota teacher uses Experience CS to set the tone for her whole year appeared first on Raspberry Pi Foundation.
Are you a computing teacher in England, Scotland, or Wales who works with 11- to 14-year-olds and is interested in how young people learn about AI ethics and sustainability?
The Raspberry Pi Computing Education Research Centre is launching an exciting new research study, and we would love you to get involved.
In this study, we will explore teaching about the impacts of AI on people, the environment, and society. Our aims for the study are to:
The study involves attending two in-person workshops in Cambridge, co-designing and teaching a unit of work, and taking part in evaluation activities such as surveys and interviews. Where necessary, we can offer support with the costs of attending the workshops, such as travel, accommodation, and supply cover.
According to a recent report, in the UK more than half of 8- to 17-year-olds use AI tools, and as these systems become an increasingly significant part of everyday life, helping young people to critically evaluate their impact is vital.
We recently conducted a scoping literature review to explore what ethical concepts are covered by AI literacy interventions for lower secondary students (11- to 14-year-olds). This work has been accepted for publication at the Frontiers in Education conference, which is taking place in October.
In this work, we used the 10 principles set out in UNESCO’s Recommendation on the Ethics of Artificial Intelligence as a framework for analysis of the ethical concepts covered by the interventions.
Our research showed that when AI ethics is taught to lower secondary students, interventions tend to focus most on concepts such as fairness and privacy. For example, multiple activities explored the important issue of algorithmic bias, teaching students about the causes of bias (such as training AI systems with small or unrepresentative datasets) and discussing real-world examples of bias in AI.
However, we found that other ethical concepts, such as proportionality and governance, are not often taught. And while a few interventions featured the theme of sustainability, we found that only one directly taught students about the environmental impact of AI itself.
Our new study aims to help address this gap by supporting young people to explore the social and ethical impact of AI tools from a sustainability perspective. Teachers will use real-world examples to engage their students in discussion and ethical reasoning. We hope the findings will support more computing teachers to teach about AI ethics in their lessons, and help students to think critically about the use of AI tools.
We are using a design-based research approach to work collaboratively with teachers to co-design a unit of lessons focused on AI ethics and sustainability.
Participating in the study will involve:
If you teach computing at lower secondary level (Years 7–9 or S1–S3) in England, Scotland, or Wales and would like to help shape the way we teach young people about ethical issues around AI, we would love to hear from you. Please register your interest via the form below.
http://rpf.io/sage-application
If you have any questions about the project, please email katharine.childs@raspberrypi.org.
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Artificial intelligence (AI) is already part of young people’s everyday lives, from the content recommended to them on social media to the generative AI tools they increasingly encounter. But AI technologies are also being used to tackle challenges in the wider world, from forecasting floods to monitoring our environment.
That’s why we believe AI literacy shouldn’t sit within a single subject. Young people need opportunities to explore how AI works, where it is used, and the questions it raises across the curriculum.
To address this, we created a new collection of free themed Experience AI resources, designed to make it easier for educators to bring AI literacy into the subjects and topics they already teach. The resources include those co-developed by the Raspberry Pi Foundation and Google DeepMind, alongside those developed independently by the Raspberry Pi Foundation, including two units created with support from our partner, Digital Moment.
When we began developing these resources, we initially explored creating materials specifically for individual curriculum subjects. But through conversations with educators and our partners around the world, we learnt that a more flexible approach could be much more useful.
Curricula differ between countries, schools, and age groups, and AI technologies rarely fit neatly within traditional subject boundaries. The same technology can raise scientific, geographical, social, creative, and ethical questions.
So rather than assigning each resource to a particular subject, our new resources are organised around broad themes, including the environment, critical thinking, and ethics.
The resources include:
Designed for learners aged 8 to 16, the resources can be used in different subjects and adapted by educators to suit their own classroom context.
That flexibility is important. We want educators to be able to introduce meaningful AI literacy without feeling that they need to become AI specialists or find space for an entirely new subject in an already busy curriculum.
The need for this approach is increasingly recognised by subject experts.
Dr Becky Kitchen, Head of Professional Development at the Geographical Association, highlighted the importance of giving young people opportunities to engage critically with AI technologies as part of their wider learning:
“Pupils are increasingly coming into contact with AI and so it’s vital that they are taught how to harness its power in an effective and critical way. Developing resources across the curriculum to embed this knowledge, understanding, and use is critical.”
After reviewing the new resources, she added:
“These resources are outstanding. They develop pupils’ geographical knowledge while simultaneously exploring the role that AI can have in a meaningful and concrete way.”
The connections extend well beyond geography.
Professor Geoff Cox, Professor of Art and Computational Culture at London South Bank University, emphasised the role that arts and humanities subjects can play in developing a richer understanding of AI technologies:
“If AI literacy is left solely to STEM subjects or computer science, we lose the opportunity to ask deeper human, cultural, and ethical questions — questions that art is uniquely equipped to explore.”
He also highlighted the value of combining different ways of learning:
“The resources move between visual analysis, practical activity, and critical reflection, recognising that only in combination can AI literacy be developed effectively.”
These perspectives reflect an important principle behind the collection. AI literacy isn’t simply about knowing how a technology works. It is also about being able to question it, understand its applications and limitations, and consider its impact on people, communities, and the world.
Before launching the resources more widely, earlier this year we collaborated with our partner, Digital Moment, to give educators in Canada the opportunity to test some of them with their students. Their experiences helped us understand not only how the resources worked in practice, but also where they could fit naturally into existing teaching.
As Indra Kubicek, CEO of Digital Moment, explains:
“Educators play a critical role in helping students understand and think critically about the use of AI and its implications across a wide range of subjects. Building the next generation of creators, builders, and innovators who will shape the future of AI starts with AI education in the classroom.”
One of the educators who took part was Grade 8 teacher Sandra Theobald, who tested our AI and social media activity. Before the lesson, she expected her students, who were already regular social media users, to be familiar with many of the ideas.
While they did have a good background knowledge, the activity prompted them to make new connections between their own behaviour, the data they generate, and the content they are shown as a result of the algorithms presented to them.
Students were able to explore the ideas themselves, with Sandra taking more of a supporting role. She found that they came away feeling more confident about understanding how their interactions with social media affect what they see, and keen to share what they had discovered with their families.
For educators who may feel unsure about introducing AI tools in their classroom, Sandra’s advice is to simply give it a go, rather than waiting until feeling like an expert:
“My advice is to start small and seek out trusted resources and experts in AI education. Partner with another educator so you can explore and learn together. There is so much information available that it can feel overwhelming, so take it one step at a time. Learn alongside your students and build your confidence as you go.”
Canadian educator Colin McKenzie also used the AI and social media unit with his Grade 7 and 8 classes.
Using Somekone, a closed social media simulation for the classroom, his students created accounts and explored how their behaviour affected the content the system recommended.
The experience felt familiar enough to capture their attention, but gave them an opportunity they wouldn’t normally have when using a real social media platform: to examine what was happening behind the scenes.
Students reflected on their interests and choices and then used data on their own behaviour to understand how that behaviour affected what they were shown. Colin found that students were eager to return to the activity each day to discover what would happen next.
Crucially, he also found it straightforward to incorporate the activity into his existing teaching rather than having to significantly change his plans.
“I think this is a powerful and easy resource for teachers to use in the classroom. It is easy to set up and navigate, and it ties in well with curriculum areas around online usage, especially with how prominent AI is becoming in our educational lives.”
For Colin, the activity provided a practical way to connect AI literacy with what his students had already learnt about online safety and evaluating information. It also helped students recognise that the content they encounter online isn’t simply appearing by chance: their own actions can influence the content recommended to them by AI-driven systems.
The experience was valuable enough that Colin plans to make the unit part of his teaching each year.
Feedback like this has reinforced why flexibility is central to our approach.
A lesson about flood forecasting might support learning in geography or science. Exploring AI-generated content could lead to discussions in art, media literacy, humanities, or computing. An activity investigating social media algorithms can connect AI literacy with online safety, critical thinking, and digital citizenship.
Most importantly, these things provide opportunities for students to encounter AI literacy in different contexts and recognise that understanding AI is relevant far beyond the computing classroom.
AI technologies will continue to change. The contexts in which young people encounter them will change too.
Our aim isn’t to prepare students for one particular AI tool or moment in time. It’s to help them develop the knowledge and critical thinking skills they need to understand all kinds of AI technologies, question them, and make informed decisions about them.
Educators shouldn’t need to be computer science teachers, or AI experts, to do that.
By creating flexible resources that connect AI with subjects and issues young people are already exploring, we hope to make it easier for more educators to bring AI literacy into their classrooms.
Explore the new themed Experience AI resources and discover a starting point for your learners.
We’d like to say a big thank you to our global partner, Digital Moment, for their support in trialling these new resources with educators and learners in Canada.
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NVIDIA unveiled new AI for Media tools at IBC 2026 for real-time video upscaling, frame generation, synthetic-video detection and live broadcasting.
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South Korea will launch the 614-petaflop Hangang supercomputer in December, opening high-end compute to researchers, national projects, and companies.
The post South Korea to Launch 614-Petaflop Supercomputer: What It Can Do appeared first on TechRepublic.
The Philippines’ $34.4 billion AI infrastructure plan targets 1.5 GW of data center capacity by 2033, as two US hyperscalers evaluate sites.
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The UK Bebras Challenge, the nation’s largest computing challenge, is back!
Last year, more than 526,000 students from across the UK took part, tackling fun and thought-provoking puzzles that introduce key ideas in computational thinking with no extra preparation needed.
Read on to learn how your school can get involved.
The UK Bebras Challenge is a free-to-enter annual challenge that is designed to spark interest in both computational thinking and computer science among students aged 6 to 19. The 45-minute challenge is accessible to everyone, offering age-appropriate but challenging interactive tasks for students at different levels, including a tailored version for secondary students with severe sight impairments.
The tasks are designed to give every student the opportunity to showcase their potential and all participating students receive a certificate. There are also certificates based on performance within school and gold certificates based on national boundaries. With self-marking tasks and no text-based programming required, it’s easy to have your school participate in the UK Bebras Challenge.
“The UK Bebras challenge provides an engaging way for students to apply their problem-solving skills in a fun, competitive environment. It complements our coding curriculum, encouraging critical thinking and enhancing computational thinking,” – Jonathan Phillips from Hereford Cathedral School
Each Bebras task’s background section is now linked to a related topic on the Ada Computer Science platform. Following the live challenge, teachers and students can explore ithe detailed explanation of the computer science concept behind each Bebras task, along with the computational thinking skills students may use to solve that task.
“We have thoroughly enjoyed delivering the UK Bebras Challenge at our school. It has been an invaluable resource and by participating in this exciting event, our students have been introduced to the world of computational thinking in a fun and engaging way. The challenges are designed to push students to think critically and creatively, developing essential problem-solving skills that are applicable both inside and outside of the classroom.” – Sharon Pendreigh from Brownedge St Mary’s Catholic High School
If you are either a UK school or teach a UK-based curriculum, then visit the UK Bebras website for more information and to register your school.
Once you’ve registered, you’ll get access to the entire UK Bebras set of questions from previous years, allowing you to create custom quizzes for your students to tackle at any time throughout the year. These quizzes are self-marking, and you can download your students’ results to keep track of their progress. The questions are perfect for enrichment activities, end-of-term quizzes, lesson starters, and even full lessons to develop computational thinking skills and promote computing concepts.
Register for free at bebras.uk/admin.
Here’s a question we ran in 2018 for the Juniors group (ages 10 to 12). Can you solve it?
This is the map of a park:
The green circles with letters represent the trees and the brown lines are paths. Note that some letters are used to label more than one tree. Walking from tree F to tree B can be described as F D E C A B.
Last Sunday two families walked in the park.
The Wilde family’s walk was B A A A C E D E E D A.
The Gilde family’s walk was F D C D A E A D E D A.
Both families started their walks at the same time.
Walking from one tree to another tree, down one path takes the same amount of time.
How many times did the two families meet at a tree?
They never met at any of the trees.
Computer scientists often use graphs (and then they speak about vertices and edges instead of trees and paths connecting them).
Another interesting point about this task is the representation of the walks in the park. Despite the fact that some of the trees (vertices) are marked by the same letter, the walks that start from B or F can be unambiguously described by the sequence of letters along the walk. It means that one sequence of letters describes only one walk. It is because the neighbours of each tree (neighbours of tree X are the trees that are directly connected to X by a path) are always labeled with different letters. So if we know where we are at some moment of the walk and we see the next letter in the walk’s representation then there is no doubt which tree we should visit next.
This Bebras task was developed by the Bebras team in South Korea and refined by members of the international Bebras community.
In 1936, a Hungarian mathematician called Dénes Kőnig wrote the first ever textbook on graph theory. Today, graph theory is fundamental to computer science and in particular networking, operation research and data structures.
Did you get it right?
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Nvidia PAIR is a free open-source tool that distributes local AI workloads across PCs and Macs, letting idle hardware help run multi-agent tasks.
The post Nvidia Launches Free PAIR Tool to Share AI Workloads Across PCs and Macs appeared first on TechRepublic.
Experience AI equips young people with a meaningful understanding of artificial intelligence (AI) and machine learning by giving educators the knowledge and confidence to teach these topics in ways that suit their classrooms.
Whether you’re introducing AI to learners for the first time, helping them deepen their understanding, exploring generative AI with them, or integrating AI literacy across the curriculum, Experience AI offers you all the resources you need for free.
Since we started publishing Experience AI resources in 2023, they have been downloaded over a million times in 195 countries, and we have worked with partner organisations in more than 40 countries to train educators to teach AI literacy. Thanks to partners, we have learned a lot about how teachers around the world use the resources in their classrooms, and this has given us direction for what new resources to develop.
Many teachers looking for AI literacy resources are not computing specialists and have very busy timetables. Under pressure to deliver more content without more instructional time, what educators need are resources they can integrate into what they already teach. To support them, we’re now offering an updated suite of Experience AI resources that make AI literacy more accessible, flexible, and relevant.
The resources include those co-developed by the Raspberry Pi Foundation and Google DeepMind, alongside those developed independently by the Raspberry Pi Foundation.
The new Discovering AI resources, aimed at learners aged 8–12 and learners aged 13–16, are single lessons for introducing the fundamental ideas behind AI. They support educators with learners who have little or no prior knowledge of AI, and include engaging, age-appropriate activities.
From there, our updated Foundations of AI units let teachers support their learners to develop a deeper understanding of how AI systems work, how they’re trained, and how they can be applied to real-world problems.
And a new and growing collection of thematic resources supports teachers and learners to explore AI through cross-subject topics such as creativity, the environment, and critical thinking.
Experience AI now fully reflects our belief that AI literacy needs to be cross-curricular. Application of AI technologies isn’t limited to neat domains or subjects, so learners’ opportunities to understand them shouldn’t be either.
Education systems vary significantly between countries, and in most national curricula, AI literacy is not yet clearly defined. Nevertheless, teachers are both under pressure to deliver this new topic area in their limited classroom time, and eager to rise to the challenge to support their learners.
So we asked ourselves: how can we help educators to teach AI literacy in any subject without additional lesson time, when we cannot create specific resources for every subject in every education system?
Our answer came from educators themselves. Through our network of global partners, we learned that teachers were not waiting for us to tell them in what subjects the Experience AI resources belonged. They were already adapting them to teach AI literacy in all sorts of contexts. For example, we saw that some educators adapted the resource on AI and ecosystems, which we had developed for Biology classrooms, for their Geography classrooms, where it supported similar learning goals.
This insight into teachers’ classroom practice prompted us to change our approach.
Now, rather than designing resources for a single subject, we design and organise them in themes that fit across subjects, such as environment, creativity, ethics, and critical thinking. So a resource for exploring the environmental impact of AI data centres could be used in Geography, Physics, Citizenship, or Business Studies classrooms. An activity about AI-generated media could prompt discussions in Digital Literacy, Art, or Computing lessons. The same material supports different curricular goals, depending on how teachers choose to use it.
A key advantage of this new thematic approach is flexibility. Our thematic resources support teachers to:
Like all Experience AI materials, the new resources:
While Experience AI will continue to focus on core AI literacy concepts — how AI systems work, how they are used, and how to think critically about them — the resources we offer will increasingly be:
In this way, we aim to make teaching AI literacy practical and achievable for every educator, regardless of their subject specialism or previous experience of using or teaching about AI.
We’ve tested the new thematic resources with our Experience AI partner, Digital Moment in Canada, who also co-created our Social Media and Flood Forecasting units. Educators’s feedback shows that they value the added flexibility and find the new materials easier to bring into their teaching.
With our new approach, we’re able to offer a more flexible Experience AI programme that supports a wider range of educators, however they want to bring AI literacy into their classrooms.
If you use the resources in your classrooms, please tell us what you think. We’ll continue refining and expanding the Experience AI programme and resources in response to feedback from educators and partners around the world.
Get in touch and share your stories of using Experience AI in the classroom via our email: advocacy@experience-ai.org
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IBM Ventures backs BQP as its BQPhy software aims to accelerate enterprise engineering workloads using existing GPU and HPC infrastructure.
The post IBM Ventures Backs BQP to Bring Quantum-Inspired Computing to Enterprises appeared first on TechRepublic.
Anthropic reportedly signed a $35 billion Lambda cloud deal tied to a Texas data center leased by Nvidia and developed by Hut 8 for Claude computing capacity.
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Google DeepMind tested Gemini 2.5 Flash Lite behind a cryptographic wall designed to protect confidential AI benchmarks and proprietary model weights.
The post Google DeepMind Seals Gemini Test to Protect AI Benchmarks appeared first on TechRepublic.
Anthropic eyed up to 5GW of AI data center capacity in NSW. Here’s what the potential buildout could mean for Australia’s power grid and IT leaders.
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Google adds pay-as-you-go pricing, spending caps and savings plans to help businesses manage Gemini Enterprise agent costs and unpredictable usage.
The post Google Adds Pay-As-You-Go Gemini Enterprise appeared first on TechRepublic.
Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support learners who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?
For learners in the UK, there is currently no established qualification that develops technical AI skills. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for learners from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.
We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.
In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).
The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.
Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.
Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.
We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning model as their EPQ artefact.
Creating a machine learning model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.
Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.
To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.
The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:
The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.
Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.
After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.
We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.
The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027.
The post Supporting a technical AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.
NUS, DayOne, and Cortical Labs have deployed a 20-unit biological computing rack in Singapore using living human neurons alongside silicon hardware.
The post Singapore Switches On Biological Computing Rack With Living Human Neurons appeared first on TechRepublic.
When faced with a programming problem, computing educators often start solving it without a second thought. Before writing a single line of code, experienced programmers use a range of skills to choose their approach and make other key decisions. Thanks to repeated practice, many of these decisions feel automatic and require little conscious thought. However, for beginner programmers, starting a programming problem can feel overwhelming, requiring intense thought and concentration.
So how can we be more aware of the automatic steps we take and choices we make so that we can break them down for learners, and make it easier for them to solve problems?
In today’s blog, we look at what ‘subgoal labels’ are and how they can be used as a form of scaffolding to support learners in breaking down computer science and programming problems.
We also share the new subgoal labels Quick Read, which you can download for free to:
Subgoal labels are simple instructions that can be added to tasks — they are particularly useful when added to worked examples. They act as signposts to provide clear directions for a larger task.
Let’s look at the following example: You’re working through a programming problem with your students. The task requires them to draw a shape using a turtle sprite. Instead of just showing learners a complete script, you could instead add these subgoal labels to the worked example.
Programming problem: Draw a blue square in the top right corner of the screen using a screen turtle.
The labels act as scaffolding for students, helping them break down the task and work through it. While the exact number or specific context of the goals might change from one project to the next, the idea is always to break down tasks into smaller, more manageable actions.
You do not need to redesign your entire curriculum to start seeing the benefits of this approach. Here are three practical ways to introduce subgoal labels in your next computing lesson:
Integrating these labels into your teaching practice offers lots of benefits, for example:
Download the full Subgoal Labels Pedagogy Quick Read as a PDF:
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OpenAI’s rumored $300–$400 smart speaker could signal a bigger shift as the ChatGPT maker looks to bring AI beyond phones, laptops, and screens.
The post OpenAI’s ‘Doughnut-Shaped’ AI Speaker Could Launch in 2027 appeared first on TechRepublic.
D-Wave is a bit of an oddity in the quantum computing space, having been founded back in the last century. And its initial offering wasn't a quantum computer like those being developed by IBM or Google. Instead, the company built what's now called a quantum annealer, a machine that isn't general-purpose but can solve a large class of optimization problems. While the hardware shares some similarities with the qubits used in gate-based quantum computers, it operates in a fundamentally different way.
But a few years back, D-Wave started working on gate-based hardware, apparently choosing a somewhat unusual qubit technology called fluxonium. And this year, the company acquired a startup called Quantum Circuits that spun out of Yale University and has been developing what's called a dual-rail qubit (the same technology used by Amazon), which promises to make most errors very easy to detect, simplifying error correction.
On Wednesday, the company is publishing a paper in Nature that describes a key step in validating this dual-rail technology, showing that two of the qubits can be entangled without altering their best feature: Most are a single type that is easy to detect.
Microsoft and Meta are both pouring billions into AI infrastructure, but investors are rewarding Microsoft’s clearer links between spending, cloud revenue, paid Copilot adoption, and contracted demand.
The post Why Investors See More Payoff in Microsoft’s AI Spending Than Meta’s appeared first on TechRepublic.
There are many algorithms for which it has been mathematically proven that a quantum computer can generate results that would take a classical computer an unreasonable amount of time to generate. Unfortunately, today's quantum computers either can't run those algorithms or can only run simplified versions that classical computers can also handle. This has left the field facing a challenging question: Can we demonstrate the promise of quantum computers on today's noisy, limited hardware?
That's a more difficult question than it may first appear. If you generate a result that's out of reach of today's regular computers, it may not be possible to verify that you got the right result. And given that today's quantum computers are somewhat error-prone, getting the wrong result is a distinct possibility. Further, in the absence of a mathematical proof of the capabilities of quantum hardware, it's possible that a better classical algorithm could outperform the quantum hardware.
These issues inspired IBM to launch a quantum advantage tracker. On Thursday, the company announced three new entries that it says clearly show a quantum advantage, each using a different approach to overcoming errors and validating quantum results. "Trusted computing when you can do classical simulations is irrelevant," IBM's Jay Gambetta told Ars. "Trusted computing when you can't do classical simulations is a big deal."
If you follow quantum computing news for long enough, it can start to seem like any quantum system that can alternate between two well-separated energy states can be used as a qubit. Atoms, ions, photons, electrons, and manufactured devices all have their backers. One of the key things that attracts backers is the tech's ability to scale. We'll need a lot of high-quality qubits to start doing any complex computations, and the ability of any technology to get us there is the subject of debate.
So even as some technologies can now support thousands of qubits, some competitors are still working on a small handful of qubits—the companies behind them are convinced that they have the potential to scale more effectively.
One of those technologies involves quantum dots that hold a single electron. Their advantage is that we can manufacture them using the same tech we use to build traditional processors, an approach that has proven to be scalable. This week saw two new papers describing different ways of using quantum dots, one of which was appealing enough that IBM bought the company that developed it. Separately, another company has released a processor showing 100 individual electrons being held in diamond defects, technology that wasn't obvious could scale.
Amazon reportedly plans to consolidate several Nova AI models. Here’s what the strategy shift could mean for AWS and enterprise AI customers.
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Verizon is seeking to tap into Silicon Valley’s AI spending in a big way. The telecom giant revealed a deal worth more than $1 billion to link Google data centers using Verizon’s dark fiber routes—and it’s also tearing copper wiring out of central offices as it converts them into small data centers suitable for supporting AI inference workloads.
The telecom company publicly disclosed the Google partnership and data center conversion efforts as part of a new “AI Connect” business initiative during an earnings conference call covering the second quarter of 2026. Verizon also expects to announce additional AI-related deals by the end of the year that would be “worth multiple billions of dollars in revenue over the next several years,” said Dan Schulman, CEO of Verizon Communications, during the earnings call.
“Now it really is about, 'how do you connect data center to data center to assure that you can optimize to maximize the ever-exploding need for compute?'” Schulman said. “And while that data center connectivity demand is exploding, there's an equal amount of desire and demand to power inference models and applications that need ultra-low latency, like robotics or remote surgery, autonomous driving, and move that out into the edge, as opposed to these big, massive data centers.”
Google raised its 2026 capital expenditure forecast to as much as $205 billion as AI demand drives more investment in cloud infrastructure and data centers.
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An AWS software bug showed some customers billing estimates in the billions and trillions. Here is what failed, why invoices were unaffected, and what IT teams should know.
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There are some obvious big picture issues that stand between us and useful quantum computing. Issues like whether we can make enough high-quality hardware qubits to connect into the error-corrected logical qubits we need, and how we generate the states needed to perform universal computation on those logical qubits. But there are also many less prominent challenges that will need to be solved before we can perform calculations.
One of those challenges, which only affects some types of hardware, is calibration. For devices we manufacture, like superconducting qubits, there are always subtle variations among individual qubits. (This is not true when we use something like an atom to hold the qubit, but the lasers that control them can drift.) As a result, this hardware is put through a process called calibration, where we test different frequencies and amplitudes of the microwave pulses that control them to find the combination that produces the lowest error rates, and then save those settings for use in calculations.
However, you can't perform the typical calibration process while you're doing calculations, which means drift becomes an issue for long and complicated algorithms. Google, though, has figured out that it's possible to do calibration using the same data that's used for error correction.
We work with partners globally to promote AI literacy through Experience AI, our programme created in collaboration with Google DeepMind. With resources available in 19+ languages and a network of partners in over 38 countries, a key part of our work is translating and localising our materials so as many young people around the world as possible build the confidence to engage with AI critically and responsibly.
But localisation introduces a unique hurdle: sometimes, languages are divided into ‘small languages’ and ‘big languages’.
As a speaker of Croatian, a ‘small language’, I think about this divide often. What makes a language small? A small language has a small number of speakers and is usually not considered a key market. Practically, this means resources for translation are often directed towards ‘big languages’. This makes sense from an impact perspective: translating into languages with more speakers maximises reach.
That is why, as a localisation coordinator at the Foundation, I am delighted that we partnered with Croatian organisation Suradnici u učenju to ensure that for Experience AI, Croatian is not treated as a small language — it is simply a language.
But translating into Croatian can present hurdles. When I got my first computer in Croatia, the user interface was in English. As a result, I never learned how to say “copy/paste” in Croatian. Those types of menus and tools had simply not been translated yet when I lived there. This happens a lot, especially with software and fast-emerging technologies like AI. Native speakers have no other choice but to use English words: the English words enter the language, and sometimes they stay.
As a result, translating key Experience AI terms into Croatian wasn’t easy. Even terms like ‘AI’ went through several rounds of discussion. Should we use the English ‘AI’ (artificial intelligence) or the Croatian ‘UI’ (umjetna inteligencija)? How should we pronounce ‘AI’ or ‘UI’ in spoken language?
Sometimes, it can be hard to know what the right thing to do is. While the English terms have been normalised, the Croatian word can seem foreign and out of place. When making a decision about how we translate terms for Experience AI resources, we also have to think about fairness and accessibility. Is it fair to assume that all young people and educators understand English words? If we assume incorrectly, we are preventing some learners from fully accessing and understanding our materials.
That’s why our in-country partners are a big part of our translation process. When we aren’t sure about whether we are making the right choice for educators and learners, we can rely on their expertise. They are not only native speakers, but also subject matter experts. For Croatian specifically, Suradnici u učenju did a very thorough review of the translation.
Beyond AI terminology, localisation also meant aligning resources with the terminology and conventions already used in Croatian schools and curricula. This ensured that the resources felt familiar to teachers and reflected the language they already use in classrooms, textbooks, and educational guidance.
As Lidija Kralj, from Suradnici u učenju explained:
“It was very important that an Informatics subject expert worked together with a Croatian language expert, and that both of us are teachers. We discussed whether to use the English abbreviation ‘AI’ or the Croatian ‘UI’, and ultimately felt it was our responsibility as educators to use Croatian terms where they already exist. We also wanted to be consistent with terminology that teachers already know from textbooks and the Croatian education system.
This combination — a subject expert, a language expert, and a Raspberry Pi Foundation localisation expert who understands our language — helped us create resources that feel natural and ready for classroom use. The response from participants in our first Experience AI course in Croatian has already shown us how much teachers value having fully localised materials.”
We opted for the approach where, if a Croatian word exists, we will use the Croatian word. Personally, I enjoyed seeing words like ‘offline activity’ slowly disappear from our resources and get replaced with Croatian words like “aktivnost bez računala” (activity without a computer).
As a result, the Croatian translation of our resources now flows very naturally, is accessible, and doesn’t read like a translation. You can check out our Croatian resources online.
We work with partners worldwide to bring AI literacy education to millions of young people. Discover all Experience AI partners here.
And to learn more about our resources and to see what other languages we translate into, check out the Experience AI website.
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There’s something really special about a Code Club. It’s a unique space for young people to get creative with technology on their terms, explore what interests them, and learn through experimenting and having fun. But speak to any Code Club leader and they’ll tell you: Code Club delivers so much more than just coding skills.
In the thousands of free Code Clubs across the world, you’ll see young people not only make amazing tech creations, but also grow as people. You’ll see young people find their voice and grow in confidence as they present what they’ve made to a room full of friends and parents. You’ll see them show a problem in their code to their friends and work as a team to fix it. You’ll see young people come up with the most creative digital solutions to challenges they see around them.
We’re delighted that the UK government recently recognised Code Club as a key resource to help schools enhance their enrichment offer. This confirms what the community tells us, and what independent research shows: that Code Club works. Not only does it help young people develop their programming skills, but it also builds life skills including confidence, resilience, and problem solving. And Code Club is completely free. Schools, libraries, and community centres can set up Code Clubs using their existing equipment, and register their clubs to access all our free resources and support for getting started and running successful club sessions for the long term.
And what about AI? There’s no getting around it, at the moment AI dominates almost every conversation about computing education, and this includes enrichment and non-formal activities too. All young people should have the power and agency to understand, question, and shape the AI systems increasingly affecting their lives. We believe that kids still need to learn to code in the age of AI, and we’ve created resources and coding projects for Code Clubs that help young people understand how AI tools work, and how to use them carefully.
Last year we announced our ambition to support every school and library in the UK to set up a free Code Club so that young people can develop the skills and knowledge they need to thrive in the age of AI.
Now we’re pleased to announce a package of free support for UK trusts and local authorities that are interested in becoming growth partners and setting up Code Clubs across their networks. If you join us as a growth partner, you will get:
If you would like to find out more about how Code Club can enhance your enrichment offer, how easy it is to get started, and how we can help, please get in touch!
The post Enhancing your enrichment offer? Code Club is the answer appeared first on Raspberry Pi Foundation.
Last fall I met with Mark Nechanicky and Taryn Israel Nechanicky, two teachers from Albert Lea, Minnesota. Mark and Taryn are bringing computer science into their classroom with Experience CS, our free cross-curricular resource teaching computer science concepts and reinforcing students’ content knowledge in areas including math, science, language arts, and more.
We talked about some of the challenges they faced, and how they’ve worked around them, including constraints on their time and the requirements around teaching their core content areas. But most of our conversation was on the impact the Experience CS resources had on their students. Mark and Taryn shared how teaching cross-curricular computing helped their students to find joy in their learning, express creativity in their projects, and build a sense of leadership within the classroom.
In a little under a week Mark, Taryn, and I will be presenting a breakout session titled Foster Student Engagement, Confidence, and Collaboration through Integrated CS in New Orleans, Louisiana at the Computer Science Teachers Association (CSTA) annual conference. You can read more about our session at the end of this blog post. For now, here’s a preview.
When it was time to code, Taryn’s students cheered. She said, “I believe this is because the curriculum makes coding feel like play, which is the basis of how Scratch is designed. Students have choices, agency, chances to make mistakes, and problem-solve individually and together.”
Taryn shared an example of student agency she saw when teaching the first lesson in Weather watchers, a unit designed for students in third grade (ages 8–9) that integrates math and science concepts with programming. During that lesson a student was able to bring her beloved dog, Chewy, into her program as a sprite, and create something meaningful to her.
While we were designing the units of Experience CS, we focused on ensuring that our resources provided students and teachers with properly scaffolded learning experiences. We did this to support all students in their computing journey, and to provide them with the opportunity to be creative and express themselves in their programs.
In the Weather watchers unit, students collect weather data and create picture graphs using sprites that represent different weather conditions. Mark’s class let us know the starter project for the unit was missing something — keep an eye out for a tornado sprite in the future! With a little creativity and problem solving, they were able to design just the sprite they needed.
Mark added, “students aren’t just following step-by-step directions to create identical projects. As they work through the lessons, they have the flexibility to add their own sprites, design additional interactions, customize characters, and extend projects in ways that reflect their interests.”
The largest part of our discussion was how teaching cross-curricular computer science created environments for all students to become leaders. “One thing I’ve noticed is that coding creates leadership opportunities for students who don’t always get the chance to be seen as the expert. Because it is a new experience for almost everyone, it changes the dynamic in the classroom. Students with Individualized Education Plans (IEPs), English Language Learners, introverted students, and students who may struggle in other academic areas are often the ones discovering new ideas, solving problems, and showing classmates how to do something cool.”
Mark also shared an experience he had while teaching Digit dash, a game design unit that reinforces multiplication fluency, designed for students in fourth grade (ages 9–10). That unit was the first time his students had explored how variables can be used to store a score in a game. The first student to figure out how to use variables in their program was introverted, but became the class “expert” on variables and modeled it for his fellow students.
If you’re heading to New Orleans, we hope to see you at our CSTA breakout session, Foster Student Engagement, Confidence, and Collaboration through Integrated CS. During our session, you will be able to hear directly from Mark and Taryn on the impact that teaching Experience CS has had on their students in the last school year. We will include examples from the Experience CS units Weather watchers, Logic and lore, Picture this!, and Digit dash.
Our CSTA session will take place on July 17th, from 3:00 PM to 4:00 PM CT, in Borgne (Floor 3).
And we’d love to hear from you. Have you used our Experience CS resources in your classroom? How did it go?
The post Joy in learning computer science with Experience CS appeared first on Raspberry Pi Foundation.
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