Tech
New AI System Helps “Kidnapped” Robots Find Their Way in Changing Environments
Researchers in Spain have developed an AI system that allows robots to recover their position even after being moved, powered off, or displaced, offering a solution to the long-standing “kidnapped robot” problem. The system, designed at Miguel Hernández University of Elche, could enable autonomous machines to navigate safely in environments that change over time.
Autonomous robots, used in service operations, logistics, infrastructure inspection, environmental monitoring, and self-driving vehicles, often rely on satellite navigation systems such as GPS. These signals can be unreliable near tall buildings or completely unavailable indoors, making precise localisation a persistent challenge.
The new approach, called MCL-DLF (Monte Carlo Localisation – Deep Local Feature), uses 3D LiDAR technology to scan surroundings with laser pulses, creating a detailed map-like representation of the environment. By analysing both large structures and small distinguishing details, the system helps robots determine their exact location.
“This is similar to how people first recognise a general area and then rely on small distinguishing details to determine their precise location,” said Míriam Máximo, lead author of the study and a researcher at Miguel Hernández University of Elche.
MCL-DLF uses AI to identify which environmental features are most useful for localisation. The system maintains multiple possible location estimates simultaneously and continuously updates them as new sensor data becomes available. This allows robots to maintain reliable positioning even when environments look similar or have changed, such as when vegetation shifts or lighting conditions vary.
The research team tested the system over several months on the university campus under diverse conditions, including different seasons, lighting, and natural changes in vegetation. Results showed that MCL-DLF provided stronger positioning accuracy and more consistent performance compared with conventional localisation methods.
By enabling robots to navigate without constant reliance on external infrastructure, the system could increase operational independence in real-world environments, where conditions rarely remain static. Reliable localisation is particularly important for tasks where safety and precision are critical, such as autonomous deliveries, environmental monitoring, and industrial inspections.
The development of MCL-DLF represents a significant advance in robotics, providing a practical solution to the kidnapped robot problem. Researchers say the technology could help service and industrial robots operate more effectively in complex, dynamic settings, paving the way for wider adoption of autonomous systems in both indoor and outdoor environments.
With AI-driven localisation, robots may soon be able to recover from displacements quickly and continue tasks without human intervention, making them more resilient and adaptable in everyday operations.
Tech
European Governments Move to Cut Dependence on Palantir Amid Rising Security and Privacy Concerns
Tech
Microsoft Unveils In-House AI Models and Quantum Breakthrough as Tech Giant Moves to Reduce External Dependence
Microsoft has taken a major step toward reducing its reliance on external artificial intelligence partners, unveiling seven in-house AI models at its Build 2026 developer conference in San Francisco. The move signals a strategic shift as the company seeks greater control over its AI stack while its key investee firms prepare for high-profile public listings.
Satya Nadella, Microsoft’s chief executive, told attendees that the industry is entering a new phase in which companies must do more than simply consume frontier AI systems. “We believe the time has come for every company to move from consuming a frontier model to fully participating at the frontier,” he said.
At the centre of the announcement is MAI-Thinking-1, Microsoft’s first reasoning model built entirely from scratch using commercially licensed data and without distillation from external systems. The model includes 35 billion active parameters and a 256,000-token context window, designed for complex reasoning tasks, coding, and long-form instruction handling.
Microsoft also introduced MAI-Code-1-Flash, a coding-focused model integrated into GitHub Copilot and Visual Studio Code, aimed at converting natural language prompts into functional software code. The company said these tools will run on Azure infrastructure, allowing it to reduce costs currently paid to external model providers and potentially offer cheaper services to developers.
Mustafa Suleyman, chief executive of Microsoft AI, said internal testing suggested strong performance gains. After optimisation for consulting firm McKinsey, he said the new models outperformed OpenAI’s GPT-5.5 in quality while offering what Microsoft estimates as up to ten times better cost efficiency, based on scaled public pricing comparisons.
In independent evaluations conducted by Surge, Microsoft’s third-party rating partner, MAI-Thinking-1 was reportedly preferred over Anthropic’s Claude Sonnet 4.6, while matching Claude Opus 4.6 on coding benchmarks.
Alongside its AI announcements, Microsoft revealed progress in quantum computing. The company’s new Majorana 2 chip is said to be 1,000 times more stable than its predecessor, extending qubit lifespan from milliseconds to an average of 20 seconds. While still far from practical deployment, Microsoft believes this marks a meaningful step toward scalable quantum machines.
Zulfi Alam, corporate vice president of Microsoft Quantum, said the company aims to deliver a commercially useful quantum system by 2029, though current prototypes contain only 12 qubits, far short of the millions required for full-scale systems.
The announcements come as Microsoft’s AI partners move toward public markets. Anthropic has filed confidentially for an IPO following a major funding round valuing it at $965 billion, while OpenAI is also preparing a filing. Microsoft has invested heavily in both companies, committing billions of dollars while integrating their models into Azure.
The new direction suggests Microsoft is positioning itself to compete directly with its own partners, as the race for dominance in advanced AI and next-generation computing intensifies.
Tech
Estonia’s AI Education Model Draws Attention as Europe Debates Digital Learning
As European governments weigh how to integrate artificial intelligence into classrooms and allocate funding for digital literacy, Estonia’s approach to AI education is gaining attention as a practical and structured model.
The Baltic nation’s AI Leap programme is designed not only to teach students how to use artificial intelligence tools but also to strengthen critical thinking and teacher involvement at a time when AI is becoming deeply embedded in everyday learning.
Concerns have grown across Europe that while students are increasingly comfortable using AI tools, many struggle to evaluate or question the information these systems generate. Educators and employers have raised concerns that overreliance on chatbots and automated tools could weaken analytical thinking and increase vulnerability to misinformation.
Estonia has chosen to address this challenge directly rather than attempting to limit student exposure to AI.
According to the AI Leap programme, between 64% and 90% of Estonian students were already using AI tools before the initiative began. Programme organisers argued that ignoring this reality could undermine learning and reasoning skills.
The initiative aims to train 48,000 students and 6,700 teachers over two years in a country with a population of just 1.36 million.
The programme has two primary goals: helping teachers adapt to AI-assisted education and encouraging students to develop responsible, thoughtful AI habits.
To support this effort, Estonia has introduced several key measures. Teachers participate in study circles that meet monthly to develop teaching methods and exchange experiences. A central online platform provides educational resources, videos, self-assessment tools and discussion forums.
More than 4,000 teachers are also receiving premium access to advanced AI platforms such as ChatGPT and Gemini to support lesson planning and classroom preparation.
One of the programme’s most distinctive features is a Socratic-style chatbot designed to guide students rather than provide direct answers. The chatbot encourages questioning, self-management and contextual thinking, helping students assess AI-generated information instead of accepting it automatically.
The programme also includes debate leagues, creative arts projects and student-led initiatives aimed at encouraging discussion and experimentation with AI beyond formal classroom settings.
Estonia has placed strong emphasis on management and implementation. School principals oversee local delivery, while nine regional managers coordinate activities across seven educational regions. The initiative operates through a public-private partnership, with the government providing half of the funding and private partners contributing the remainder.
Technology companies, educators and researchers are involved in designing and testing tools tailored to Estonia’s education system.
Education analysts say Estonia’s strategy highlights a broader lesson for Europe: AI literacy may depend less on limiting technology and more on teaching students how to use it thoughtfully, critically and responsibly.
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