Capacity development

Purdue and Google collaborate to advance AI research and education

Purdue University and Google are expanding their partnership to integrate AI into education and research, preparing the next generation of leaders while advancing technological innovation.

The collaboration was highlighted at the AI Frontiers summit in Indianapolis on 13 November. The event brought together university, industry, and government leaders to explore AI’s impact across sectors such as health care, manufacturing, agriculture, and national security.

Leaders from both organisations emphasised the importance of placing AI tools in the hands of students, faculty, and staff. Purdue plans a working AI competency requirement for incoming students in fall 2026, ensuring all graduates gain practical experience with AI tools, pending Board approval.

The partnership also builds on projects such as analysing data to improve road safety.

Purdue’s Institute for Physical Artificial Intelligence (IPAI), the nation’s first institute dedicated to AI in the physical world, plays a central role in the collaboration. The initiative focuses on physical AI, quantum science, semiconductors, and computing to equip students for AI-driven industries.

Google and Purdue emphasised responsible innovation and workforce development as critical goals of the partnership.

Industry leaders, including Waymo, Google Public Sector, and US Senator Todd Young, discussed how AI technologies like autonomous drones and smart medical devices are transforming key sectors.

The partnership demonstrates the potential of public-private collaboration to accelerate AI research and prepare students for the future of work.

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Stanford’s new AI model boosts liver transplant efficiency

A new machine learning model has been developed by Stanford Medicine researchers to make liver transplants more efficient. It predicts whether a donor will die within the time frame necessary for organ viability.

Donation after circulatory death requires that the donor pass within 30 to 45 minutes after life support removal; otherwise, surgeons often reject the liver due to increased risks for recipients. The model reduced futile procurements by 60%, outperforming surgeons’ predictions.

The algorithm analyses a wide range of donor data, including vital signs, blood work, neurological reflexes, and ventilator settings. The model was trained on over 2,000 cases from six US transplant centres and can be customised for hospital procedures and surgeon preferences.

The model also features a natural language interface that extracts relevant medical record information, streamlining the transplant workflow.

Donation after circulatory death is becoming increasingly important as it helps narrow the gap between organ demand and availability. Normothermic machine perfusion devices preserve organs during transport, making such donations more feasible.

Researchers hope the model will also be adapted for heart and lung transplants, further expanding its potential to save lives.

Stanford researchers stress that better predictions could help more patients receive life-saving transplants. Ongoing refinements aim to decrease missed opportunities from just over 15% to around 10%, enhancing efficiency and patient outcomes in organ transplantation.

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CERN unveils AI strategy to advance research and operations

CERN has approved a comprehensive AI strategy to guide its use across research, operations, and administration. The strategy unites initiatives under a coherent framework to promote responsible and impactful AI for science and operational excellence.

It focuses on four main goals: accelerating scientific discovery, improving productivity and reliability, attracting and developing talent, and enabling AI at scale through strategic partnerships with industry and member states.

Common tools and shared experiences across sectors will strengthen CERN’s community and ensure effective deployment.

Implementation will involve prioritised plans and collaboration with EU programmes, industry, and member states to build capacity, secure funding, and expand infrastructure. Applications of AI will support high-energy physics experiments, future accelerators, detectors, and data-driven decision-making.

AI is now central to CERN’s mission, transforming research methodologies and operations. From intelligent automation to scalable computational insight, the technology is no longer optional but a strategic imperative for the organisation.

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Microsoft expands AI model Aurora to improve global weather forecasts

Extreme weather displaced over 800,000 people worldwide in 2024, highlighting the importance of accurate forecasts for saving lives, protecting infrastructure, and supporting economies. Farmers, coastal communities, and energy operators rely on timely forecasts to prepare and respond effectively.

Microsoft is reaffirming its commitment to Aurora, an AI model designed to help scientists better understand Earth systems. Trained on vast datasets, Aurora can predict weather, track hurricanes, monitor air quality, and model ocean waves and energy flows.

The platform will remain open-source, enabling researchers worldwide to innovate, collaborate, and apply it to new climate and weather challenges.

Through partnerships with Professor Rich Turner at the University of Cambridge and initiatives like SPARROW, Microsoft is expanding access to high-quality environmental data.

Community-deployable weather stations are improving data coverage and forecast reliability in underrepresented regions. Aurora’s open-source releases, including model weights and training pipelines, will let scientists and developers adapt and build upon the platform.

The AI model has applications beyond research, with energy companies, commodity traders, and national meteorological services exploring its use.

By supporting forecasting systems tailored to local environments, Aurora aims to improve resilience against extreme weather, optimise renewable energy, and drive innovation across multiple industries, from humanitarian aid to financial services.

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Baidu launches new AI chips amid China’s self-sufficiency push

In a strategic move aligned with national technology ambitions, Baidu announced two newly developed AI chips, the M100 and the M300, at its annual developer and client event.

The M100, designed by Baidu’s chip subsidiary Kunlunxin Technology, targets inference efficiency for large models using mixture-of-experts techniques, while the M300 is engineered for training very large multimodal models comprising trillions of parameters.

The M100 is slated for release in early 2026 and the M300 in 2027, according to Baidu, which claims they will deliver ‘powerful, low-cost and controllable AI computing power’ to support China’s drive for technological self-sufficiency.

Baidu also revealed plans for clustered architectures such as the Tianchi256 stack in the first half of 2026 and the Tianchi512 in the second half of 2026, intended to boost inference capacity through large-scale interconnects of chips.

This announcement illustrates how China’s tech ecosystem is accelerating efforts to reduce dependence on foreign silicon, particularly amid export controls and geopolitical tensions. Domestically-designed AI processors from Baidu and other firms such as Huawei Technologies, Cambricon Technologies and Biren Technology are increasingly positioned to substitute for western hardware platforms.

From a policy and digital diplomacy perspective, the development raises questions about the global semiconductor supply chain, standards of compute sovereignty and how AI-hardware competition may reshape power dynamics.

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Romania pilots EU Digital Identity Wallet for payments

In a milestone for the European digital identity ecosystem, Banca Transilvania and payments-tech firm BPC have completed the first pilot in Romania using the EU Digital Identity Wallet (EUDIW) for a real-money transaction.

The initiative lets a cardholder authenticate a purchase using the wallet rather than a conventional one-time password or card reader.

The pilot forms part of a large-scale testbed led by the European Commission under the eIDAS 2 Regulation, which requires all EU banks to accept the wallet for strong customer authentication and KYC (know-your-customer) purposes by 2027.

Banca Transilvania’s Deputy CEO Retail Banking, Oana Ilaş, described the project as a historic step toward a unified European digital identities framework that enhances interoperability, inclusivity and banking access.

From a digital governance and payments policy perspective, this pilot is significant. It shows how national banking systems are beginning to integrate digital-ID wallets into card and account-based flows, potentially reducing reliance on legacy authentication mechanisms (such as SMS OTP or hardware tokens) that are vulnerable to fraud.

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Czech central bank invests in digital assets for the first time

The Czech National Bank (CNB) has acquired digital assets for the first time, creating a USD 1 million test portfolio outside its international reserves. The initiative includes Bitcoin, a USD stablecoin, and a tokenised deposit on the blockchain.

The portfolio aims to provide practical experience with digital assets and test related operational processes, without plans for further active investment.

Governor Aleš Michl explained that the idea originated in January 2025 to evaluate decentralised Bitcoin and explore its potential role in diversifying reserves. Discussions later included stablecoins, tokenised deposits, and future payment methods, with insights to be shared over the next two to three years.

The CNB emphasises that the koruna remains the country’s legal tender, while new digital payment and investment methods are increasingly emerging.

The test portfolio will examine the full chain of digital asset management, from technical administration and multi-level approvals to crisis scenarios, security measures, and AML compliance.

The CNB aims to maintain and develop internal expertise, ensuring staff gain practical knowledge transferable across teams. The investment is separated from the central bank’s international reserves and will not influence monetary policy or foreign exchange operations.

Alongside the portfolio, the CNB has launched CNB Lab, an innovation hub to test digital assets, blockchain solutions, AI tools, and payment innovations. The Lab will help the bank prepare for the future of finance while building practical experience and team expertise.

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Explainable AI predicts cardiovascular events in hospitalised COVID-19 patients

In the article published by BMC Infectious Diseases, researchers developed predictive models using machine learning (LightGBM) to identify cardiovascular complications (such as arrhythmia, acute heart failure, myocardial infarction) in 10,700 hospitalised COVID-19 patients across Brazil.

The study reports moderate discriminatory performance, with AUROC values of 0.752 and 0.760 for the two models, and high overall accuracy (~94.5%) due to the large majority of non-event cases.

However, due to the rarity of cardiovascular events (~5.3% of cases), the F1-scores for detecting the event class remained very low (5.2% and 4.2%, respectively), signalling that the models struggle to reliably identify the minority class despite efforts to rebalance the data.

Using SHAP (Shapley Additive exPlanations) values, the researchers identified the most influential predictors: age, urea level, platelet count and SatO₂/FiO₂ (oxygen saturation to inspired oxygen fraction) ratio.

The authors emphasise that while the approach shows promise for resource-constrained settings and contributes to risk stratification, the limitations around class imbalance and generalisability remain significant obstacles for clinical use.

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New AI platforms approved for Surrey Schools classrooms

Surrey Schools has approved MagicSchool, SchoolAI, and TeachAid for classroom use, giving teachers access through the ONE portal with parental consent. The district says the tools are intended to support instruction while maintaining strong privacy and safety safeguards.

Officials say each platform passes rigorous reviews covering educational value, data protection, and technical security before approval. Teachers receive structured guidance on appropriate use, supported by professional development aligned with wider standards for responsible AI in education.

A two-year digital literacy programme helps staff explore online identity, digital habits, and safe technology use as AI becomes more common in lessons. Students use AI to generate ideas, check code, and analyse scientific or mathematical problems, reinforcing critical reasoning.

Educators stress that pupils are taught to question AI outputs rather than accept them at face value. Leaders argue this approach builds judgment and confidence, preparing young people to navigate automated systems with greater agency beyond school settings.

Families and teachers can access AI safety resources through the ONE platform, including videos, podcasts and the ‘Navigating an AI Future’ series. Materials include recordings from earlier workshops and parent sessions, supporting shared understanding of AI’s benefits and risks across the community.

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AI credentials grow as AWS launches practical training pathway

AWS is launching four solutions to help close the AI skills gap as demand rises and job requirements shift. The company positions the new tools as a comprehensive learning journey, offering structured pathways that progress from foundational knowledge to hands-on practice and formal validation.

AWS Skill Builder now hosts over 220 free AI courses, ranging from beginner introductions to advanced topics in generative and agentic AI. The platform enables learners to build skills at their own pace, with flexible study options that accommodate work schedules.

Practical experience anchors the new suite. The Meeting Simulator helps learners explain AI concepts to realistic personas and refine communication with instant feedback. Cohorts Studio offers team-based training through study groups, boot camps, and game-based challenges.

AWS is expanding its credential portfolio with the AWS Certified Generative AI Developer – Professional certification. The exam helps cloud practitioners demonstrate proficiency in foundation models, RAG architectures, and responsible deployment, supported by practice tasks and simulated environments.

Learners can validate hands-on capability through new microcredentials that require troubleshooting and implementation in real AWS settings. Combined credentials signal both conceptual understanding and task-ready skills, with Skill Builder’s more expansive library offering a clear starting point for career progression.

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