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Website: https://raspberrypi.com
194 Cambridge Science Park, Milton Road (or Maurice Wilkes Building, St. John's Innovation Park in some refs)
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Raspberry Pi
Raspberry Pi is a low-cost, credit card-sized single-board computer (SBC) that supports various operating systems, primarily Linux-based. It features GPIO pins for interfacing with sensors and actuators, making it suitable for embedded systems and IoT applications. Its versatility allows for integration in robotics, automation, and educational projects, leveraging programming languages like Python and C for development.
RobotToday Initiative
Robotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
At Goodwood, Robots Explore Their Role in Enhancing Human Experience
At the Goodwood Cricket Ground, a fox-eared robot on roller skates greeted visitors without imitating humans or threatening to replace them, showcasing its unique identity. This event, part of the FOS Future Lab's Intelligent Systems Zone, featured three exhibitors presenting diverse answers to the question of what intelligent machines should do for humanity. One Sheffield startup, led by Raspberry Pi co-founder Liz Upton, demonstrated a method for programming robots using simple English. A robotic arm responded to natural language commands, with COO Eleanor Tang-Smith emphasizing the goal of making robots perform tasks that humans find tedious. Meanwhile, a large screen displayed a real-time reconstruction of Goodwood's famous Taylor Garage, merging digital and physical worlds seamlessly. The fox-eared robotic dogs, designed in Paris, avoided the 'uncanny valley' by engaging with humans through expressive features. They are already in use in hospitals and airports for tasks like transporting and assisting, allowing humans to focus on more urgent matters. The event highlighted three approaches to human-robot interaction, emphasizing the importance of language, vision, and gestures in redefining the interface between humans and machines.
Robotics AI Spatial Computing Human-Robot Interaction
Small-AI Models Gain Traction Around the World
One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year.The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says.The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. “Our server was in the United States, and just to get the result of a single scan was taking me over 5 minutes.”So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo.More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of “small AI.”Small AI for Global Health Care AccessSmall AI is a far cry from wealthy nations’ colossal large language models (LLMs), hyperscale data centers, multibillion-dollar investments, and debates about AI consciousness. But for millions of people around the world, the only AI that matters, and often the only kind available, is small. (According to a World Bank Report issued in November, only 0.7 percent of internet users in the world’s poorest countries have used ChatGPT, compared to a quarter of all internet users in the most developed nations.)“Most people are discussing AI from the LLM/generative side. But that needs a lot of computing power, electricity, massive data, and skilled people to manage it,” Ajay Banga, president of the World Bank, said last January at the World Economic Forum, in Davos. “Outside the developed world, other than maybe India and China, very few countries have that combination.”By contrast, small AI can deliver useful, even life-saving services to people in areas that have none of those things, Banga said. In India, where the government’s AI plans call for more development of small AI, many such systems are working for farmers.For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.“This is the most important area in AI nowadays,” says Marcelo José Rovai, a professor at the Institute of Engineering and Information Systems at the Federal University of Itajubá, in Brazil, who was involved in all three projects. “It’s growing very fast.”Low-Power, Small-AI Models on Devices Small AI models can run on a variety of low-power devices, including [from left to right] an Arduino Nano 33 BLE Sense, a Seeed Wio Terminal, and an Arduino Portenta.Moez AltayebFor Alonge, Rovai, and other advocates, small AI is not just “a promising trend,” as that November World Bank report calls it. It may be, in the long term, the form of AI that will touch the most lives and remain sustainable after some of the giant models become too costly for most users.“I think the future of AI is not like one giant model, at a center. I think it’s millions of small, precise models deployed at the edge, each one solving like a specific problem, a specific context,” Alonge says. This is partly because much of humanity—including people in parts of rich countries as well as the developing world—lives without access to cutting-edge frontier models. But, he says, it’s also because those models are not sustainable.“If someone is not subsidizing it, most people will not be able to afford those models. So those of us who are said to be small-AI developers are the ones who will have to build for the majority of the world,” Alonge says.There is no strict definition of “small AI,” but people often use the term for language models with at most a few billion parameters. (Compare that to cutting-edge models, which can include more than a trillion.) That’s small enough to run directly on a phone or a Raspberry Pi. That’s what allows these applications to run on devices without a connection to a data center and use only a few watts of power, often supplied by a battery or a solar panel.Despite their small footprint, these models aren’t fundamentally different technology from that of gigantic AI models, Rovai says. Many instances of small language models were created the same way the phone-based version of Alonge’s pharmaceuticals scanner was—by “pruning” large models, or removing the parameters that weren’t involved in the task. The result is a system that’s less capable generally but still very good at the specific job it was pruned for, Rovai says. A lighter version of RxAll’s RxScanner spectrometer sends its results to an AI model run locally on a phone to check that a drug’s molecular signature is genuine.RxAllOther small models are created by “distillation.” They are trained to mimic a large model, until their performance approaches that of their “teacher,” Rovai says. In other cases, a larger model’s precision is reduced, for example, so that a model run on 32-bit architecture can run on 8-bit designs. In situations where the machine learning application is being used to classify data or predict patterns (like an ant infestation), it’s trained from the beginning on a small device, not derived from a larger model at all. Running all these small, specialized systems is becoming easier, Rovai says, for two reasons.The first reason is that hardware is getting better and more capable while using less power, he says. This means more and more phones can run small AI—especially those equipped with neural processing units, which are specialized chips that handle AI tasks like facial recognition and changing the brightness, shadows, or contrast in a photo.In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.The second reason Rovai cites is the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.Rovai illustrated these reasons on a Zoom call, using one of his most recent experiments. Holding up a device, he says, “This is the new Arduino UNO Q—a US $50 device with a Qualcomm chipset. I’m running a language model here, which collects data from sensors and analyzes that data to detect tiny pools of water where mosquitoes might be breeding. It takes 3 watts to run it.”Support for Small-AI DevelopmentConvinced that millions of people are already benefiting from these kinds of applications, the World Bank now actively promotes small AI with grants, mentorship programs, financing, technical advice, and models of government policies that are friendly for small-AI development. For example, in Rwanda, the World Bank is backing a government program to help low-income households get devices that can run AI.All that said, no one claims that large language models are going away entirely. To create a generative AI that can run on a phone or other small device requires the architectural insights, data processing, and results of a larger model, Rovai says. “We need the big models to create these smaller models.” And for all that small AI can benefit people without access to big AI, the technology can’t solve the larger problems of development and digital inequality, Alonge says. Implementing small AI won’t allow nations to escape the challenge of creating an ecosystem to support AI: reliable power, a supply chain that works, and an educational system that develops the talents needed to create AI tools.Though his drug-scanning system can run for days on a phone with no connection, “you still want to be able to enable periodic syncing for updates with new signatures for the medications and analytics,” Alonge says. “And even when you are using batteries, reliable power is important. That phone battery is not going to last forever.”In many parts of the world, the future of small AI isn’t assured, he says. “It works, and many places will eventually need to use it. The question is whether or not the political actors are wise enough to invest in infrastructure to support it long term.”
Small-language-models Artificial-intelligence Llms
DEEPX and Sixfab Launch 'DEEPX AI HAT' to Drive Edge Physical AI on Raspberry Pi
A new partnership has emerged to enhance the Raspberry Pi ecosystem by providing high-performance, low-power AI acceleration. This initiative aims to eliminate the reliance on cloud services for real-time inference, particularly benefiting applications in robotics and smart automation. The collaboration has introduced a production-ready Starter Kit and software development kits (SDKs) designed to lower entry barriers for developers worldwide. By making these tools accessible, the partnership seeks to empower a broader range of innovators to integrate advanced AI capabilities into their projects, fostering growth and development within the global tech community.
Video Friday: Do Robots Even Need Legs?
In the latest edition of Video Friday, IEEE Spectrum robotics showcases a variety of innovative robotics videos and announces upcoming events in the field. Notable events include RSS 2026 scheduled for July 13-17 in Sydney, the Summer School on Multi-Robot Systems from July 29 to August 4 in Prague, Actuate 2026 on August 18-19 in San Francisco, and IROS 2026 from September 27 to October 1 in Pittsburgh. Among the featured projects, Eno, an advanced AI and general-purpose robot developed by Genesis, exemplifies a new generation of robots designed to enhance human capabilities. Meanwhile, NASA's Jet Propulsion Laboratory is testing the ERNEST rover in California's Colorado Desert, which is being developed for future lunar missions and can operate autonomously over challenging terrain. Sony AI's Ace project demonstrates a robotic system capable of adapting to unpredictable scenarios in table tennis, while ANYbotics highlights the economic benefits of their quadruped robots in industrial inspections, preventing significant production losses. GITAI is preparing for a robotic satellite servicing demo, and Bi-AQUA is exploring underwater photography challenges for robots. Sanctuary AI has achieved impressive results in wire plugging tasks for a major automotive supplier, showcasing a success rate exceeding 99.5%. Additionally, various other robotics projects are highlighted, including a bipedal robot named GrowBot, which operates on a low-cost Raspberry Pi and aims to make physical AI accessible to a broader audience.
Video-friday Robot-videos Lunar-rover Inspection-robots Robot-hands Robot-ai
AI Is Insatiable
A recent analysis by Senior Editor Samuel K. Moore highlights the ongoing DRAM shortage, primarily driven by the increasing demand for high bandwidth memory (HBM) from AI hyperscalers like Google, Microsoft, OpenAI, and Anthropic. This shortage is significantly impacting the performance of large language models, as these companies invest heavily in building expansive data centers to support their AI operations. The report, published on February 10, has been updated to reflect the current state of the memory market, which is also affecting the prices of low-cost computers, such as the Raspberry Pi. The demand for memory is exacerbated by the energy consumption of AI technologies, which could account for up to 12 percent of all U.S. power by 2028. As companies like Nvidia and AMD require more memory for their processors, the pressure on supply chains continues to mount. Moore notes that any adjustments in production schedules from major HBM manufacturers—Micron, Samsung, and SK Hynix—could signal a potential easing of the shortage. Additionally, tech companies may need to adapt by opting for hardware that requires less memory or redesigning products to mitigate the impact of the constraints. The analysis emphasizes the importance of monitoring these developments as the tech industry navigates the challenges posed by the memory shortage. To stay informed on this evolving situation and broader technology trends, readers are encouraged to subscribe to the weekly newsletter, Tech Alert.
Semiconductors Dram Memory Chips Ai Data-centers
AGRICULTURAL ROBOTICS | Harvesting Robots: $6.9B Market
Harvesting robots hit $2.24B in 2024 and are targeting a $50B labor market at less than 5% penetration. Deep-dive: market data, 14 companies, economics, and 2030 outlook.
Axon: Ambitious Open-Source Humanoid AI Robot Project Hits GitHub
Two teenage developers have launched an innovative open-source humanoid robot project named Axon on GitHub. This ambitious initiative showcases a working prototype equipped with advanced features such as AI voice control, integration with large language models, and the ability to move its arms and head, as well as drive. Despite its impressive capabilities, the project demands a high level of technical expertise and refinement. The robot operates using a Raspberry Pi, multiple ESP32 microcontrollers, and a dedicated server for AI processing, highlighting the complexity involved in its development.
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