Sustainable development Archives | Page 2 of 305

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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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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European Investment Bank backs Allegro for AI expansion

The European Investment Bank has agreed to provide Polish e-commerce platform Allegro with a PLN 1 billion loan to support research, development, and AI initiatives.

The financing marks the largest private-sector research and development programme backed by the EIB in Poland and is intended to support Europe’s digital competitiveness and digital sovereignty.

The funding will cover nearly 40% of Allegro’s planned expenditure on research, development, and innovation in the coming years. The company plans to expand its use of AI, improve customer services, develop next-generation delivery systems, and strengthen its digital marketplace.

The investment forms part of the EIB Group’s TechEU initiative, which aims to support investment in strategic technologies, including AI, clean technology, and quantum computing. Allegro said the financing will support work by software engineers, data scientists, and AI specialists, while helping the company develop new algorithms, models, and system architectures.

Allegro is one of Europe’s largest homegrown online marketplaces and controls about a third of the Polish market. It is also expanding in Czechia, Slovakia, and Hungary, giving small and medium-sized enterprises access to new customers across the region.

The EIB said planned investments in several technical centres in Poland would also support social and territorial cohesion in the EU.

Why does it matter?

The loan shows how EU-backed financing is being used to support AI adoption and digital innovation in European platform companies. For the EIB, the Allegro deal fits into a wider push to strengthen Europe’s digital and industrial competitiveness through investment in strategic technologies. For Central and Eastern Europe, it also supports regional digital infrastructure, technical skills, and marketplace innovation.

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UNESCO highlights ethical AI integration in South Asian higher education

AI is transforming higher education systems across South Asia, creating opportunities to improve teaching, learning, research, and institutional management, while also exposing challenges around policy readiness, educator capacity, digital infrastructure, and equitable access.

A regional policy dialogue held in Kathmandu on 20 May 2026, jointly organised by UNESCO Kathmandu, Tribhuvan University, the Asian Development Bank, and UNESCO-ICHEI, highlighted the need for coordinated strategies to guide AI integration in higher education.

Key priorities include strengthening policies and strategies for AI use, investing in teacher professional development, improving collaboration between universities and industry, and better understanding the implications of generative AI for higher education and technical and vocational education and training.

The discussions also focused on inclusion, particularly the gender divide in AI. UNESCO said one of the most significant forms of AI bias in South Asia affects girls and women, underscoring the need for their participation in AI-related education and workplaces to build an inclusive AI ecosystem.

The launch of the IIOE Nepal National Centre at Tribhuvan University reflects the growing need for sustained national capacity-building mechanisms to support higher education institutions in adapting to digital transformation.

The dialogue also reinforced the importance of evidence-based policymaking, following the release of the Report on Digital Transformation in Higher Education in South Asia. UNESCO said such knowledge can help governments and universities move beyond experimentation towards more coherent and future-oriented strategies for AI integration.

Why does it matter?

AI integration in higher education is becoming a structural policy issue, not only a classroom technology question. UNESCO’s regional dialogue points to the risk that unequal digital infrastructure, weak institutional capacity, limited AI literacy, and gender gaps could deepen existing inequalities if clear policies, ethical safeguards, and investment in educators do not guide AI adoption.

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EU consultation closes on AI energy measurement

The European Commission has moved forward with work on measuring the energy consumption and emissions of AI models and systems, as part of preparations for a possible AI energy measurement framework under the EU AI Act.

The targeted consultation forms part of a Commission-procured study on measuring and promoting energy-efficient and low-emission AI in the European Union. Responses will help refine the study, contribute to a measurement framework for the AI Act’s energy-related objectives and support the design of a potential AI energy and emissions label.

The process focuses on how to measure energy use across the AI lifecycle, including development and training, as well as operational use and inference. The Commission says a comprehensive picture of AI’s energy efficiency and carbon footprint requires data on computational resources, electricity consumption and hardware details.

Under Annex XI of the AI Act, providers of general-purpose AI models must document known or estimated energy consumption as part of their technical documentation obligations. The consultation, therefore, targets developers and deployers of general-purpose AI models and AI systems, as well as component and service suppliers.

Stakeholders were asked about the accessibility of data needed to assess AI energy consumption and emissions, as well as the suitability of different AI performance indicators. The Commission said the aim is to develop a robust and practical industry-informed framework for measuring AI energy consumption and efficiency.

The AI Office will publish a summary of the consultation results based on aggregated data, with respondents not directly quoted.

Why does it matter?

AI’s growing energy demand is becoming a regulatory and environmental policy concern, especially as general-purpose AI models require substantial computing resources for training and inference. A common EU framework for measuring AI energy use and emissions could make environmental impacts more visible, support future transparency obligations and help compare systems more consistently. A possible AI energy and emissions label would also push sustainability into AI governance alongside safety, transparency and accountability.

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World Bank highlights ‘Small AI’ for farmers and rural communities

According to Hindustan Times, World Bank President Ajay Banga highlighted the potential of ‘Small AI’ systems to support farmers and rural communities through locally deployed and lower-cost technologies.

Examples discussed included farmers in India using mobile phones to share images of diseased crops and receive agricultural advice remotely. Banga also referred to healthcare workers in Indonesia using basic internet connections to access local diagnostic support systems in remote areas.

At the summit, entrepreneur Saurav Mukherjee said AI adoption was expanding into sectors including agriculture and food production. Mukherjee said AI tools may support agricultural decision-making through analysis of seed quality and environmental conditions such as soil, weather, and water availability.

He also noted that wider internet connectivity and 5G access could support wider AI adoption in underserved regions. However, he cautioned that shortages of skilled workers could limit implementation capacity in some regions.

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EU lawmakers challenge confidentiality rules on data centre emissions data

A group of 35 Members of the European Parliament has called on the European Commission to review confidentiality rules affecting public access to environmental data from data centres. The request focused on the disclosure of information related to emissions, energy use, and water consumption.

According to reporting by Investigate Europe, the disputed wording was linked to proposals submitted during consultations by Microsoft and DIGITALEUROPE. The clause was later incorporated into the EU Energy Efficiency Directive and limits disclosure of certain information related to individual data centres.

Critics argue that the measure may reduce transparency regarding the environmental impact of expanding digital infrastructure. Some lawmakers and advocacy groups have also raised questions about compatibility with transparency principles under the Aarhus Convention. Reports said critics believe the rules reduce scrutiny of the environmental impact linked to expanding AI and cloud infrastructure.

The lawmakers called on the European Commission to reconsider the provision and publish more detailed environmental reporting data. The issue has contributed to broader discussions in the EU regarding environmental accountability and oversight of digital infrastructure.

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NASA develops AI system to track harmful algal blooms using satellite data

NASA researchers have developed an AI system designed to combine satellite datasets to improve monitoring of harmful algal blooms.

The system uses self-supervised machine learning to analyse patterns across five satellite missions and instruments, helping researchers identify blooms in regions including western Florida and Southern California. According to researchers, the approach could support environmental monitoring and earlier identification of marine health risks.

Harmful algal blooms can affect ecosystems, wildlife, coastal environments, and public health. In parts of Florida, blooms caused by Karenia brevis have disrupted coastal communities for decades, while toxic blooms along the US West Coast have harmed dolphins, sea lions, and other marine species.

NASA researchers said the system combines information from multiple satellite observation technologies. Instruments such as the PACE satellite and the TROPOMI monitoring instrument help identify algae characteristics, including pigment, fluorescence, and biological activity across ocean surfaces.

The researchers said the self-supervised AI model identifies relationships between datasets without relying heavily on manually labelled data. The system was trained using satellite observations collected during 2018 and 2019 before being tested on later bloom events.

Michelle Gierach of NASA’s Jet Propulsion Laboratory said the system could help environmental agencies identify areas for water sampling earlier during bloom development. Researchers said combining satellite observations with field data may improve coordination between scientific and public health teams.

The project team said the system is being expanded using additional coastal and freshwater datasets.

Why does it matter?

NASA’s development highlights growing use of AI and satellite intelligence for environmental monitoring and climate-related risk management. Harmful algal blooms are becoming an increasing concern for coastal economies, fisheries, tourism, biodiversity, and public health systems worldwide.

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Study examines local warming effects linked to data centre expansion

New research suggests that expanding data centre infrastructure may contribute to localised warming effects similar to urban heat islands.

The study, published in the Journal of Engineering for Sustainable Buildings and Cities, examined several data centres in the Phoenix metropolitan area and found measurable increases in surrounding air temperatures. Researchers reported temperature increases ranging from approximately 1.5 to 4 degrees Fahrenheit within areas located downwind from facilities.

Data centres generate waste heat through cooling systems used to support high-performance computing operations.

According to the researchers, large data centre campuses can generate concentrated thermal output associated with high energy consumption.

The findings come as global demand for AI, cloud computing, and digital services continues to drive the construction of new facilities across the US and other regions. Northern Virginia, Phoenix, and several European locations have become major hubs for hyperscale infrastructure development.

The researchers said the observed effects differ from traditional urban heat islands because of continuous cooling activity and continuous energy consumption. The study noted that clusters of facilities may produce cumulative effects that require further investigation.

The researchers discussed potential implications for energy demand, infrastructure planning, and surrounding communities. The study said elevated local temperatures could influence cooling demand and related environmental conditions.

Furthermore, scientists stressed that additional peer-reviewed research remains necessary to determine the long-term climatic significance of large-scale data centre expansion.

Why does it matter?

The findings reflect growing scrutiny surrounding the environmental footprint of AI infrastructure. Data centres already face criticism over electricity consumption, water usage, and grid pressure. The possibility that concentrated AI infrastructure may also influence local temperatures introduces another dimension to debates surrounding sustainable digital expansion.

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ITU Radiocommunication Bureau outlines key aspects future connectivity

ITU Radiocommunication Bureau has highlighted the critical role of radio-frequency spectrum in ensuring digital resilience, emphasising that reliable connectivity underpins essential services such as healthcare, transport and emergency communications.

According to the Bureau, resilience begins before disruption through coordinated spectrum management, international standards and regulatory frameworks. These systems enable wireless networks and satellite services to operate reliably and avoid harmful interference.

The organisation stressed that growing demand for connectivity, including 5G, satellite broadband and AI-enabled systems, increases pressure on spectrum resources. Technical standards and global coordination are therefore essential to maintain interoperability and support innovation.

ITU also pointed to the importance of satellite systems and early warning technologies in responding to climate risks and disasters. Future decisions at the World Radiocommunication Conference 2027 in China will further shape how resilient digital infrastructure develops globally.

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