Development Archives | Digital Watch Observatory

India and South Africa deepen cooperation on AI and emerging technologies

India and South Africa have agreed to strengthen bilateral cooperation in emerging technologies, with AI, digital infrastructure and advanced manufacturing identified as key areas for future collaboration.

The agreement was reached during a meeting between India’s Minister of Science and Technology, Dr Jitendra Singh, and South Africa’s Deputy Minister of Science, Technology and Innovation, Dr Nomalungelo Gina. Both sides emphasised the need to expand traditional scientific cooperation into innovation-driven partnerships aimed at delivering economic and societal benefits.

Discussions covered biotechnology, genomics, vaccine development, health technologies, renewable energy, hydrogen, advanced manufacturing and digital innovation. The two countries also explored opportunities to deepen cooperation in quantum technologies, geospatial technologies and digital infrastructure.

The meeting reaffirmed the long-standing scientific relationship between the two countries and concluded with a commitment to strengthen innovation ecosystems through research collaboration, startup partnerships, technology deployment and industry engagement.

Why does it matter?

India and South Africa are among the leading technology and innovation hubs in the Global South. Expanding cooperation in AI, digital infrastructure, healthcare and advanced manufacturing could help accelerate technological development while fostering greater knowledge exchange and investment opportunities.

The partnership also reflects a broader trend of emerging economies seeking to strengthen innovation ecosystems and reduce reliance on technology supply chains and platforms concentrated in a small number of countries.

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MIT develops ChartNet dataset to improve AI chart understanding

MIT researchers have developed a new dataset, ChartNet, to improve how vision-language models interpret charts and other graphical data.

The dataset is designed to help AI systems better combine visual, numerical, and linguistic information, a task that remains difficult even for advanced models. MIT said chart understanding is important for applications such as business trend analysis, financial reporting, and scientific figure interpretation.

ChartNet contains more than one million synthetic chart images, each paired with supporting code, numerical tables, textual descriptions, and question-and-answer pairs. The dataset was created through an automated pipeline that generates and augments chart examples, supported by quality checks to ensure that the code is executable and the resulting charts are accurate and clean.

The researchers developed ChartNet to address a key limitation in current AI systems: the lack of large, high-quality training data for robust chart interpretation. Many existing datasets rely on limited chart images collected from the internet and lack the supporting information needed for models to understand the underlying data.

MIT researchers used ChartNet to train several open-source vision-language models, including IBM’s Granite Vision series. The dataset improved model accuracy across chart reconstruction, chart data extraction, chart summarisation, and chart question answering.

In MIT’s testing, smaller open-source models trained with ChartNet consistently outperformed much larger commercial models on several chart-interpretation tasks. The researchers said the dataset could help smaller organisations use AI for analytical work without relying only on large proprietary systems.

Why does it matter?

ChartNet shows how better training data can improve AI performance in specialised analytical tasks. If smaller open-source models can interpret charts more accurately after training on high-quality datasets, organisations with limited budgets may gain access to stronger AI tools for business analytics, research, financial reporting, and scientific communication. The work also highlights a broader point in AI development: model capability depends not only on size, but also on the quality and structure of training data.

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Armenia expands AI ecosystem through research, infrastructure and investment

Armenian Prime Minister Nikol Pashinyan said government initiatives have helped position Armenia as an emerging centre for technology and AI, according to remarks reported by state news agency Armenpress. Speaking during the election campaign, Pashinyan highlighted several projects that he said demonstrate the government’s efforts to strengthen Armenia’s technology sector.

Pashinyan highlighted agreements signed with US President Donald Trump last year, including cooperation on AI. He argued that subsequent developments in the sector have validated the government’s approach.

As examples of progress, the Prime Minister cited the establishment of an AI centre at Yerevan State University and the launch of the Eleveight AI data centre. He also linked developments in the sector to increased public investment in science and higher salaries for researchers.

Pashinyan said investment in the defence sector has supported technological development and stated that Armenian defence companies are exporting products internationally. He made the remarks during campaigning ahead of Armenia’s parliamentary elections.

Why does it matter?

Armenia is seeking to expand its role in emerging technologies at a time when countries are increasingly investing in AI infrastructure, research capacity and digital innovation as drivers of economic growth and competitiveness.

The government’s focus on AI cooperation, research institutions and data centre infrastructure reflects broader efforts to strengthen domestic technological capabilities and attract investment in the digital economy.

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Hong Kong launches AI-focused cybersecurity initiatives for 2026

Hong Kong’s Digital Policy Office has announced a series of AI-related cybersecurity initiatives for the second half of 2026, following a briefing on cyber resilience and emerging technology risks. The office said it would focus on improving AI security awareness and digital literacy among both organisations and the public.

Planned initiatives include a Secure AI@Work Enablement Campaign, organised with the Hong Kong Internet Registration Corporation, to help enterprises develop secure and compliant AI ecosystems. The Digital Policy Office will also collaborate with industry on an AI x Cybersecurity Challenge focused on AI-powered threat detection, cyber resilience and cybersecurity skills development.

The office said it would continue enterprise support and practical drills, including an enhanced Cybersec One+, the Cybersecurity Service Providers Connect Programme and the third Hong Kong Cybersecurity Attack and Defence Drill. Hong Kong will also consolidate the Cyber Security Summit Hong Kong and the Cybersecurity Symposium into a single Cybersecurity Symposium and Summit in December.

The Cyber Security and Technology Crime Bureau said the volume of cyber threat intelligence related to threats targeting Hong Kong continues to increase. Its Cyber Security Centre analysed more than 330,000 threat intelligence records during the first quarter of 2026, identifying phishing as the most prevalent threat category.

The bureau said it would deepen international law enforcement cooperation, strengthen intelligence sharing with sectors including critical infrastructure, and use AI and big data to improve cyber threat detection, early warning analysis, and incident response. The Hong Kong Police Force and Cyberport have also established the Smart Policing Joint AI Lab to develop technologies for detecting deepfakes and strengthening network defence capabilities.

Why does it matter?

The initiatives reflect growing efforts by governments to address the cybersecurity implications of wider AI adoption. As organisations increasingly integrate AI into business operations, concerns around secure deployment, cyber resilience and workforce readiness are becoming key policy priorities.

The programme also highlights how AI is being used both as a potential source of cyber risk and as a tool for improving threat detection, incident response and cyber defence capabilities.

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Anthropic expands AI cybersecurity programme for critical infrastructure

AI company Anthropic has announced a major expansion of Project Glasswing, an initiative aimed at strengthening the security of critical software through AI-assisted vulnerability detection.

After initially providing access to around 50 organisations, the programme will expand to approximately 150 additional partners across more than 15 countries.

Project Glasswing provides selected organisations with access to Claude Mythos Preview, Anthropic’s cybersecurity-focused AI model. According to Anthropic, participating organisations have identified more than 10,000 high- and critical-severity software vulnerabilities through the programme.

The newly added participants include operators and vendors across critical infrastructure sectors such as power, water, healthcare, communications and hardware manufacturing.

Anthropic argues that increasingly capable AI systems could significantly reshape cybersecurity, creating both new defensive opportunities and new risks. The company says future AI models may enable defenders to identify, analyse and remediate vulnerabilities at greater scale, while also potentially enhancing the capabilities available to malicious actors.

Project Glasswing is intended to help critical organisations adapt before such capabilities become widely accessible.

Alongside the expansion, Anthropic said it plans to provide additional cybersecurity tools, support vulnerability remediation efforts and work with industry, governments and open-source software maintainers to strengthen cyber resilience.

Why does it matter?

The expansion of Project Glasswing highlights the growing role of AI in cybersecurity, particularly in vulnerability discovery and software security testing. As critical infrastructure operators face increasingly sophisticated cyber threats, AI-assisted tools may help identify and address security weaknesses more quickly.

At the same time, the initiative reflects broader concerns that advances in AI could benefit both defenders and attackers, increasing the importance of responsible deployment, coordinated security research and resilience planning across critical sectors.

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ILO chief calls for human-centred AI governance at labour conference

International Labour Organization (ILO) Director-General Gilbert F. Houngbo has called for a human-centred approach to AI at the opening of the 114th International Labour Conference in Geneva. He said the future of work would depend not only on technological advances, but also on the policies, institutions and social dialogue shaping their impact on people’s lives.

Drawing on his report ‘A Moment of Choice: Harnessing Artificial Intelligence for Decent Work‘, Houngbo outlined an agenda focused on rights, employment and skills, social protection, and social dialogue. He argued that productivity gains generated by AI should be shared through higher wages, stronger labour protections and more inclusive economic growth.

Houngbo warned that decisions taken today would determine whether AI expands opportunity and shared prosperity or contributes to greater inequality and insecurity. He also situated AI governance within a broader context of economic uncertainty, citing ILO estimates that a prolonged oil-price shock could reduce global working hours by the equivalent of millions of full-time jobs and lead to significant labour income losses by 2027.

Delegates will also hold a second discussion on decent work in the platform economy, with the aim of developing new international labour standards. The draft Convention and Recommendation cover employment promotion, protections for digital platform workers, and provisions relating to automated systems used by digital labour platforms.

Delegates from governments, employers, and workers will also address gender equality, social dialogue, tripartism, and the application of labour standards. The conference, which brings together representatives from the ILO’s 187 Member States, will run until 12 June.

Why does it matter?

As AI becomes increasingly integrated into workplaces, governments, employers and workers are debating how productivity gains, skills requirements and labour protections should evolve. The ILO’s focus on human-centred AI reflects growing international efforts to ensure that technological change supports decent work rather than exacerbating inequality.

The discussions are also significant because they could influence future international labour standards for platform work and the use of automated systems in employment, helping shape how AI affects workers worldwide.

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WHO outlines opportunities and risks of AI in health policy

The World Health Organization (WHO) has published a discussion paper examining how AI could reshape evidence-informed health policymaking. The paper, titled ‘Artificial intelligence and evidence-informed policy – emerging challenges and opportunities’, examines how AI can affect the way health problems are defined, policy options are designed, and impact is assessed.

The paper was developed jointly by WHO’s Department of Data, Digital Health, Analytics and AI and its Department of Science for Health. It is intended for policy-makers, regulators, health managers, and AI developers, and organises its analysis around the policy cycle, from understanding problems to designing solutions and monitoring implementation.

According to the paper, AI can strengthen policy analysis through the use of larger datasets, continuous evidence synthesis and faster scenario modelling. The paper also identifies risks throughout the policy cycle, including data bias, excessive focus on measurable indicators, digital divides, cybersecurity vulnerabilities and the possibility that automated monitoring systems could gradually shift policy implementation away from its original objectives.

A recurring concern is what the paper describes as ‘epistemic injustice’, whereby AI systems may prioritise quantifiable and data-rich evidence while overlooking lived experience, local expertise, Indigenous knowledge and community-based perspectives. WHO says existing evidence-informed policymaking tools and AI governance frameworks already converge on transparency, participatory engagement, rights protection, and risk-based oversight.

WHO recommends conducting algorithmic impact assessments and technology readiness reviews before deploying AI systems in policymaking processes. Once systems are deployed, WHO recommends continuous evidence-review processes, human verification mechanisms and multidisciplinary oversight, emphasising that AI should support rather than replace human judgement in health policymaking.

Why does it matter?

AI is increasingly being used to analyse large datasets, model policy scenarios and support public-sector decision-making. As governments and international organisations explore these capabilities, questions about transparency, accountability, bias and human oversight are becoming more important.

WHO’s recommendations highlight the need to balance AI’s analytical potential with safeguards that protect human rights, ensure inclusive policymaking and maintain human responsibility for policy decisions.

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Greece advances digital transformation with AI, interoperability and cybersecurity measures

Greece’s Minister of Digital Governance and Artificial Intelligence, Dimitris Papastergiou, has outlined a broad digital transformation agenda in an interview with the newspaper Manifesto, highlighting new legislation, AI deployment, cybersecurity measures and digital public services.

A key element of the agenda is the implementation of the EU’s ‘once-only’ principle, which allows citizens and businesses in Greece to avoid repeatedly submitting the same information to public authorities across the EU. The legislation also introduces more than 800 new interoperability connections between government systems, aiming to reduce bureaucracy and improve service delivery.

Papastergiou highlighted the growing use of AI in public administration, including the mAigov digital assistant, which has handled more than 4.4 million citizen queries. Greece is also investing in AI infrastructure projects, including the Daedalus supercomputer and the Pharos AI Factory, while preparing national legislation aligned with the EU AI Act.

The minister also highlighted a memorandum of understanding with voice AI company ElevenLabs aimed at improving accessibility and public services through voice-based technologies. Additional initiatives include the creation of a Unified Property Hub, stronger anti-phishing measures, a National Malicious Websites Blocking List, the Defective Vehicle Recall Registry and enhancements to the MyStreet application.

On child online safety, Greece plans to introduce age-verification requirements for users under 15 through the Kids Wallet application from January 2027. According to the minister, the system will verify age without exposing or storing unnecessary personal information.

Why does it matter?

Greece’s plans illustrate how governments are increasingly combining AI deployment, digital public services and cybersecurity measures within broader digital transformation strategies.

The initiatives also reflect wider European efforts to improve interoperability, strengthen digital infrastructure, enhance online safety for children and prepare for the implementation of the EU AI Act.

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Microsoft unveils Majorana 2 and advances quantum computing roadmap

Microsoft has introduced Majorana 2, its next-generation topological quantum chip, alongside the general availability of Microsoft Discovery, an AI-powered research platform designed to accelerate scientific discovery.

The company says the new chip delivers a 1,000-fold improvement in qubit reliability compared with the previous generation, representing a step towards more scalable quantum computing.

Majorana 2 incorporates a new materials stack based on lead superconductors, enabling a mean qubit lifetime of 20 seconds, with some qubits remaining stable for up to 1 minute. Microsoft says the improvement has allowed it to shorten its projected timeline for a scalable quantum computer, aiming for 2029.

A key element of the announcement is the role of Microsoft Discovery, the company’s agentic AI platform for scientific research and development. Microsoft said its quantum team used specialised AI agents to automate measurements, optimise fabrication processes, analyse large datasets, identify previously unnoticed flaws, and generate new research hypotheses.

According to Microsoft, agentic AI has become a regular part of its quantum research workflow, supporting scientists and engineers as they manage complex materials, fabrication, software, and measurement challenges.

The company also announced that Microsoft Discovery is now generally available for organisations conducting research in sectors such as life sciences, materials science, chemicals, energy, manufacturing, and consumer goods. A free local application is also being released in preview, allowing individual researchers to access core AI-driven research capabilities through a GitHub Copilot account.

Why does it matter?

Quantum computing still faces major barriers around qubit stability, reliability, error correction, and scalability. Microsoft’s announcement is significant because it links progress in quantum hardware with the use of agentic AI in scientific workflows. If the company’s roadmap holds, AI-assisted research could help accelerate progress towards practical quantum systems, with potential long-term implications for materials science, energy, health, chemistry, and other fields that depend on complex simulation.

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Quantum research opens new paths for energy and computing

Researchers at the University of California, Riverside, have advanced understanding of how quantum wave functions behave in ultra-thin layered materials, a development that could eventually improve solar energy technologies and support future quantum computing systems.

The findings show that electric fields can be used to control the position and behaviour of quantum wave functions in materials only a few atoms thick. Experiments showed that wave functions can shift between layers or exist in multiple layers simultaneously through quantum superposition, affecting a material’s optical properties.

The researchers also drew parallels with natural systems such as photosynthesis, where quantum processes are believed to support highly efficient energy transfer. By studying similar mechanisms in engineered materials, scientists hope to improve control over energy conversion and transport, particularly in solar technologies where energy losses remain a major challenge.

Researchers are also exploring whether vibrations can be used to control quantum states, potentially enabling new types of ‘quantum vibronic switches’. The findings could have applications beyond energy systems, including quantum computing, sensing and photonic technologies.

Why does it matter?

The research highlights progress towards actively controlling quantum behaviour in engineered materials, an important step in the development of practical quantum technologies. Such control could enable more efficient energy systems and improve the performance of future quantum devices.

The findings also illustrate how insights from natural processes such as photosynthesis can inform the design of next-generation materials for computing, sensing and renewable energy applications.

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