Interdisciplinary approaches

New benchmark tests AI on unpublished mathematics problems

AI systems have demonstrated growing capabilities in advanced mathematics, according to benchmark results published by the non-profit organisation First Proof.

The organisation evaluated four frontier AI systems, including ChatGPT 5.5 Pro, against ten unpublished research-level mathematical problems contributed by leading mathematicians.

The benchmark found that seven of the ten problems received at least one solution judged to be correct by expert reviewers across the participating systems. One notable result involved a stochastic partial differential equations problem, where an AI system produced a correct solution using an approach different from the human-developed proof, drawing praise from expert referees for its originality.

Despite the progress, significant limitations remain.

Several problems remained unsolved, including a metric geometry challenge on which none of the systems made meaningful progress. Reviewers also reported that AI systems handled routine mathematical reasoning effectively but continued to struggle with the most challenging conceptual and creative aspects of proof construction.

Why does it matter?

The benchmark offers one of the most demanding independent tests of AI performance in advanced mathematics, a field often viewed as a proxy for higher-level reasoning and scientific problem-solving. The results suggest that frontier AI systems are increasingly capable of contributing to specialised research tasks and, in some cases, generating approaches that differ from those developed by human experts.

At the same time, the findings highlight the limits of current AI systems. While they can assist with complex reasoning and formal problem-solving, they continue to struggle with the deepest conceptual challenges that often drive mathematical breakthroughs. This suggests that AI may increasingly serve as a research assistant and discovery tool, while human expertise remains essential for guiding and validating scientific advances.

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European Commission opens applications for RAISE AI research advisory board

The European Commission has opened applications for the RAISE High-Level Academic Advisory Board, inviting leading researchers in AI and AI-enabled science to help shape Europe’s future AI research agenda.

The advisory board will support the implementation of the EU’s AI in Science Strategy and provide independent scientific guidance on the development of RAISE (Resource for AI Science in Europe).

RAISE was launched in 2025 under Horizon Europe to strengthen European leadership in both fundamental AI research and the application of AI across scientific disciplines.

The Commission is seeking academics with expertise in AI research or experience applying AI in fields such as medicine, climate science and advanced materials. Board members will provide strategic recommendations on research priorities, governance structures, benchmarks and framework conditions needed to accelerate AI-enabled scientific discovery.

Through RAISE, the EU aims to bring together leading researchers, computational resources, data and funding within a coordinated ecosystem that supports scientific excellence and strengthens Europe’s position in global AI research and innovation.

Why does it matter?

The initiative reflects growing recognition that AI is becoming a foundational tool for scientific discovery across disciplines ranging from healthcare and climate research to materials science and physics. Governments are increasingly investing in AI research infrastructure to ensure that researchers have access to the computing power, data and expertise needed to remain globally competitive.

The advisory board also highlights Europe’s ambition to play a larger role in shaping the future of AI-enabled science. By coordinating talent, infrastructure and funding through initiatives such as RAISE, the EU aims to strengthen both its scientific capacity and its position in the global race for AI innovation.

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Japanese researchers develop interpretable AI for materials discovery

Researchers in Japan have developed an interpretable AI method to explain how AI models make predictions in materials discovery. The method analyses features learned by a trained AI model and uses them to identify relationships between atomic structure and optical spectra.

The study was led by researchers from the Institute of Science Tokyo, in collaboration with Tohoku University. The work is expected to be published in the journal Advanced Intelligent Discovery.

AI is increasingly used in materials research to predict how materials behave based on atomic structure. Such models can accelerate materials discovery and reduce reliance on trial-and-error experimentation, but many operate as black boxes, making it difficult to understand how they arrive at specific predictions.

The researchers addressed this problem by analysing a trained AI model that predicts optical absorption spectra from atomic structural data. They extracted features from the model’s internal layers and clustered materials according to shared structural and spectral characteristics.

The team used an atomistic line graph neural network trained on data from 2,681 metal oxides, chalcogenides, and related compounds. The clustering process classified materials into groups sharing structural characteristics such as elemental composition, atomic coordination, bond lengths, bond angles and similar spectral signatures.

According to the researchers, the model learned meaningful relationships between atomic structure and material properties without being explicitly provided oxidation states or electronic configurations as input. The interpretable AI method could therefore help researchers identify the factors behind desired spectral shapes and support more rational materials design.

The approach could also be applied beyond optical absorption spectra. Researchers said the approach could also help explain how atomic arrangements influence other material properties under varying conditions, such as temperature and pressure, opening new possibilities for designing materials with targeted characteristics.

Why does it matter?

One of the main challenges facing the use of AI in scientific research is explainability. While AI systems can identify patterns and generate accurate predictions, researchers often need to understand the reasoning behind those predictions before they can confidently apply them in experimental settings.

By revealing how AI models connect atomic structures with material properties, interpretable AI could make machine learning a more effective tool for scientific discovery. The approach may help accelerate the development of advanced materials for applications ranging from renewable energy and electronics to sensors and next-generation manufacturing, while improving trust in AI-assisted research.

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South Korea selects site for AI defence robotics hub

South Chungcheong Province and the city of Nonsan have been selected to host a new AI defence robotics innovation cluster in South Korea.

The project was chosen under the Defense Acquisition Program Administration’s 2026 defence innovation cluster programme and will run for five years, from 2026 to 2030. It will receive a total of 49.9 billion won in national and local funding, including 24.5 billion won from the central government.

The cluster will be developed around Naedong and Yeonmu-eup in Nonsan and will focus on building an AI defence robotics ecosystem. The project is intended to support the full development cycle, from technology research and testing to demonstration and commercialisation.

Plans include a 45,190-square-metre testing and certification facility in Yeonmu-eup, designed to support research and development, test evaluation and demonstration activities in one location.

The initiative will involve Chungnam Techno Park, Konyang University, the Korea Testing Laboratory, the Korea Institute of Industrial Technology, the Korea Automotive Technology Institute and KAIST’s Mobility AX Research Institute.

Provincial officials said Nonsan’s existing defence infrastructure, including the Nonsan Defence National Industrial Complex, the headquarters of South Korea’s three armed services and Korea National Defense University, helped support the site’s selection.

Why does it matter?

The project shows how South Korea is linking AI, robotics and defence industrial policy through testing and certification infrastructure. For digital policy, the relevant signal is the institutionalisation of AI-enabled military robotics development, including facilities for experimentation, evaluation and commercialisation. It also reflects the growing importance of regional defence-tech clusters as governments seek to build domestic capacity in autonomous and unmanned systems.

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UK to test AI legal assistants to help reduce court delays

The UK government will develop and test AI legal assistants as part of a broader set of technology initiatives aimed at reducing court delays and improving the efficiency of the justice system. The Ministry of Justice said the tools will support routine casework, including research and case analysis, before any possible use in the Crown Court.

The AI legal assistants will be developed in collaboration with legal professionals and AI developers, with initial testing taking place in controlled environments. The government said the trials will help establish standards for the safe and ethical use of AI in legal settings and ensure any future systems meet the expectations of judges and legal practitioners before wider deployment.

Judges are also preparing to test an AI tool designed to identify trial-ready cases and group similar hearings together. The government said the tool is intended to better use judicial, prosecutorial, and court resources, helping cases move more quickly for victims.

The announcement also covers Justice Transcribe, an AI tool now available to every probation officer in England and Wales. The tool records and transcribes conversations with offenders, reducing the administrative burden associated with transferring handwritten notes into digital systems.

According to the government, Justice Transcribe could free up the equivalent of 18,750 days annually, enabling probation officers to spend more time supervising offenders and supporting efforts to reduce reoffending. A similar transcription tool is being trialled in Immigration and Asylum Tribunals to support judges with case notes and reduce administrative pressure.

The projects form part of the Prime Minister’s AI Exemplars programme, which aims to accelerate the adoption of AI across public services. The government also pointed to AI Growth Labs, secure testing environments intended to help the UK lawtech sector develop and refine AI products before bringing them to market.

Why does it matter?

Justice systems around the world are exploring how AI can help address growing caseloads, administrative burdens and resource constraints. Applications such as legal research assistance, transcription services and case management tools have the potential to improve efficiency while allowing legal professionals to focus on higher-value tasks.

At the same time, the use of AI in judicial and legal contexts raises important questions about accountability, transparency, fairness and human oversight. The UK’s emphasis on controlled testing and ethical safeguards reflects growing recognition that AI deployment in the justice sector requires robust governance alongside technological innovation.

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Researchers develop AI governance tools for public health across the Global Majority

A research team led by Professor Jude Kong from the University of Toronto is developing new tools to monitor, assess, and govern the use of AI in public health across the Global Majority, with a particular focus on Africa.

The team, which includes Jake Effoduh, Jim Hinton, Abbas Yazdinejad, and Maral Niaz, has begun mapping how AI is being integrated into healthcare systems and infrastructure. The work focuses on identifying key actors, technologies and use cases, providing a clearer picture of how AI is becoming embedded in public health systems.

The next phase involves developing a dynamic dashboard designed to track AI systems and support evidence-based decision-making. Rather than relying solely on top-down governance frameworks, the team aims to co-develop tools that policymakers, civil society organisations, educators and practitioners can use in their own contexts.

In practice, this means creating tools that are not only technically robust but also socially legitimate and locally relevant. While strengthening AI literacy and governance capacity across the Global Majority, the initiative aims to empower policymakers with evidence-based insights, support civil society in understanding AI systems, and enable more informed and inclusive decision-making processes.

By bringing together expertise in technology, law, public policy and social impact, the project reflects the multidisciplinary nature of AI governance. The team will present its findings at the AI for Good Global Summit in Geneva, during ITU’s Kaleidoscope sessions on Thursday, 9 July 2026, from 15:30 to 16:30.

Why does this matter in AI world?

AI for the Global Majority (AI4GM) is a joint initiative of the Geneva Graduate Institute, Microsoft and the International Telecommunication Union. The initiative supports research on how AI can benefit majority populations in areas including governance, education, health, finance, and digital innovation.

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UAE laboratory introduces AI-powered prostate cancer diagnostics

M42’s National Reference Laboratory has introduced an AI-powered tool for prostate cancer diagnostics in the UAE in partnership with digital pathology company Qritive. The platform will be integrated into the laboratory’s diagnostic workflow at Cleveland Clinic Abu Dhabi.

The system analyses digital pathology slides, highlights suspicious findings and provides structured insights to help pathologists detect prostate cancer and assess disease severity. It is designed to identify cancerous tissue, assess tumour patterns, support grading according to internationally recognised standards and measure tumour burden.

M42 said the technology is intended to assist medical professionals rather than replace them, with final clinical decisions remaining the responsibility of physicians. The company said AI can help pathologists make complex decisions more efficiently, reduce interpretation variation, and support better patient outcomes.

NRL said the platform could help healthcare providers manage growing diagnostic demand while giving patients and clinicians faster access to critical information. Prostate cancer remains a growing health concern in the Middle East, with officials citing an estimated 50,000 new cases diagnosed each year.

The introduction of the platform forms part of NRL’s wider strategy to strengthen oncology services and expand the use of digital pathology and AI-enabled diagnostics. Officials said the initiative supports the UAE’s broader objectives of advancing healthcare innovation, improving patient outcomes and building a more data-driven health system under UAE Vision 2031.

Why does it matter?

AI-assisted diagnostics are increasingly being adopted to help healthcare professionals manage growing workloads, improve consistency in clinical assessments and accelerate access to diagnostic results. In pathology, AI tools can help identify patterns in medical images and support decision-making, particularly in areas where demand for specialist expertise is rising.

The deployment also reflects the UAE’s broader strategy of integrating AI into healthcare services as part of its digital transformation agenda. As healthcare systems seek to improve efficiency and patient outcomes, AI-enabled diagnostics are becoming an increasingly important component of modern medical practice.

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United Kingdom launches AI Growth Lab to support legal sector innovation

The UK government has launched an advisory AI Growth Lab to support responsible AI adoption in regulated industries, starting with the legal services sector.

The Ministry of Justice said the advisory sandbox is designed to accelerate the development and deployment of AI products and services by helping innovators navigate existing regulatory frameworks with greater confidence.

Legal services will be the first sector to participate, following strong industry demand for clearer and more coordinated regulatory guidance. The Lab will bring together the Council for Licensed Conveyancers, the Solicitors Regulation Authority, the Information Commissioner’s Office, and the Legal Services Board.

The participating regulators will work with innovators to identify cross-regulatory challenges, address unintended barriers in existing rules, and develop a clearer understanding of what effective AI oversight looks like in practice.

The initiative will support AI innovators, LawTech companies, legal service providers, and conveyancing firms as they test AI products within current regulatory frameworks. Applications are expected to open later this summer.

The government said the Lab aims to support responsible innovation, economic growth, and improved access to justice by enabling faster and more affordable legal services while maintaining quality.

Participation in the Lab will not amount to regulatory approval, endorsement, or authorisation, and existing legal and regulatory requirements will remain unchanged.

Why does it matter?

The Lab reflects the UK’s preference for structured, regulator-led experimentation rather than immediate new AI-specific legislation for every sector. Legal services are a useful test case because AI tools could improve access to justice and reduce costs, but they also raise questions around professional duties, data protection, accountability, confidentiality, and consumer protection. The initiative could help identify where existing rules create uncertainty for AI adoption without lowering regulatory standards.

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Dutch study explores how to scale AI across government organisations

Dutch research organisation TNO has conducted an exploratory study examining how AI applications can be scaled across government organisations in the Netherlands. The study was commissioned by the Ministry of the Interior and Kingdom Relations because AI offers opportunities for public sector services and operations.

The study supports the Netherlands’ Digitalisation Strategy, which calls for a more proactive government role in the development and adoption of AI. One option under consideration is an AI scaling facility that would support the reuse and further development of successful AI applications, helping deploy them more quickly and across a wider range of organisations.

According to the study, scaling AI is not a linear or one-size-fits-all process. Depending on their goals, context, and partnerships, organisations may follow different approaches, including scaling within one organisation, replicating solutions across similar organisations, adapting them to new sectors or tailoring broad solutions to local needs.

TNO identifies seven approaches to AI scaling: scaling in, scaling out, scaling beyond, scaling together, scaling down, scaling up and scaling deep. The strategies cover internal adoption, cross-organisational reuse, sectoral adaptation, collaborative development, localisation, policy and standards work, and cultural or behavioural change inside organisations.

A related ‘Conversation starter’ has also been developed to help organisations assess AI scaling initiatives at the outset. The recommendations include treating scaling as a strategic decision, selecting an approach aligned with intended outcomes, addressing governance and organisational culture, reusing existing solutions where possible, investing in AI literacy and documentation, clarifying ownership and funding arrangements, and regularly assessing whether scaling remains desirable, feasible and legally appropriate.

Why does it matter?

Many governments are moving beyond AI experimentation and focusing on how successful projects can be deployed at scale. However, expanding AI use across public institutions often involves organisational, governance and cultural challenges that extend beyond technology itself.

The Dutch study highlights the need for structured approaches to AI adoption, emphasising reuse, collaboration and institutional capacity. Its findings could help governments accelerate AI deployment while maintaining accountability, effectiveness and compliance with legal requirements.

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Cambridge researchers test AI-designed vaccine in human trial

Researchers at the University of Cambridge have developed an experimental vaccine using AI, marking what they describe as the first human test of a vaccine component designed entirely by AI. The experimental approach aims to provide broad protection against entire families of viruses, including coronaviruses with pandemic potential.

The AI system analysed genetic data from multiple coronaviruses and designed a ‘super-antigen’ intended to help the immune system recognise and respond to a broad range of viral variants, including those that may emerge through future mutations. An initial trial involving 39 volunteers focused primarily on safety, while a larger follow-up study is planned to evaluate immune responses and effectiveness in greater detail.

Researchers say the approach could help vaccine development keep pace with rapidly evolving threats, including influenza, emerging COVID-19 variants and viruses with the potential to spread from animals to humans. The team is also exploring similar AI-designed vaccines for influenza, bird flu, and Ebola-like viral haemorrhagic fevers, where current protection options remain limited.

Researchers describe the findings as an early but significant step towards using AI to accelerate vaccine design and strengthen preparedness for future disease outbreaks. The study highlights growing expectations that AI may become a central tool in global pandemic prevention strategies.

Why does it matter?

Traditional vaccine development often focuses on responding to specific pathogens after they emerge. By contrast, AI-assisted design could help researchers develop vaccines that provide protection against entire families of viruses before outbreaks occur.

If successful, the approach could shorten development timelines, improve preparedness for future pandemics and support efforts to address rapidly evolving infectious diseases. The research also highlights the growing role of AI in scientific discovery and biomedical innovation.

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