Claude Science has been launched as an AI workbench designed to streamline scientific research by bringing data analysis, coding and research tools into a single integrated environment. The platform is designed to help researchers analyse data, run multi-step workflows, and generate publication-ready outputs with full transparency.
The platform consolidates research tools such as databases, coding environments and analysis software, enabling scientists to work across disciplines without switching between applications. Outputs are fully auditable, with embedded code, workflow histories and documentation to support validation and reproducibility.
Claude Science also uses a multi-agent architecture comprising specialist agents and a reviewer agent that verifies calculations and citations. It can be deployed on local infrastructure or high-performance computing systems, allowing institutions to scale AI-assisted research while keeping sensitive data within their own environments.
Why does it matter?
Claude Science reflects a broader evolution of AI from a standalone assistant to an integrated research platform. By combining specialised AI agents, computational tools and transparent workflows in a single environment, it aims to simplify scientific research while improving reproducibility and collaboration.
The platform also raises broader questions about the future of AI in science. As researchers increasingly rely on AI to support data analysis and experimentation, ensuring transparency, validation and institutional control over sensitive research data will be essential to maintaining scientific integrity and trust.
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OpenAI has introducedGeneBench-Pro, a research benchmark designed to assess whether AI agents can perform the complex, judgment-intensive analysis required in real-world computational biology.
Unlike conventional benchmarks that focus on factual recall or routine workflows, GeneBench-Pro is designed to measure what OpenAI calls ‘research taste‘, the sequence of judgement calls involved in scientific analysis, from interpreting ambiguous data and revising assumptions to deciding whether findings are robust enough to inform downstream research.
The benchmark comprises 129 problems spanning ten domains within computational biology, including statistical genetics, cancer genomics, clinical diagnostics, and pharmacogenomics. Each problem presents an AI agent with a realistic and deliberately messy dataset, brief experimental context, and a target to estimate.
To answer correctly, the model must explore the data iteratively, select an appropriate analytical approach, and supply a final answer without exploiting shortcuts or matching arbitrary author preferences. To prevent common benchmark shortcuts, every problem uses synthetically generated data whose underlying causal structure is fully known, allowing performance to be measured against a controlled ground truth.
OpenAI said its flagship model, GPT-5.6 Sol, achieved a pass rate of 28.7% at the highest reasoning setting, increasing to 31.5% in Pro mode. By comparison, the strongest model available when the original GeneBench was introduced scored below 5%.
External reviewers estimated that completing a typical GeneBench-Pro task would require 20 to 40 hours of expert work and cost thousands of dollars, whereas AI inference currently costs only a few dollars per run. OpenAI argues this suggests substantial economic potential even before models achieve expert-level performance.
OpenAI acknowledged that frontier models still solve fewer than one-third of the benchmark problems, often making partial progress but failing to complete the full chain of scientific reasoning expected from experienced researchers. To encourage independent evaluation, the company is open-sourcing ten representative tasks on Hugging Face and providing a 50-question subset to Artificial Analysis for third-party benchmarking.
Why does it matter?
GeneBench-Pro reflects a broader shift in AI evaluation from testing factual knowledge and coding ability to assessing whether models can support complex scientific reasoning. As computational biology increasingly becomes limited by data interpretation rather than data generation, reliable AI assistance in analytical workflows could accelerate research in areas such as genomics, drug discovery and precision medicine.
The benchmark also highlights the importance of rigorous evaluation methods for frontier AI. By using controlled synthetic datasets with known ground truth, GeneBench-Pro seeks to measure not only whether models reach the correct answer but also how well they make the sequence of judgements required in real-world scientific research.
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Ask an image-generation model to create a CEO, a software engineer, or a successful entrepreneur, and chances are the result will be male. Ask for a nurse, a personal assistant, or a caregiver, and a woman is far more likely to appear.
Such outputs have fuelled growing concerns about gender bias in AI and the broader relationship between women and synthetic intelligence. Yet a more complicated question lies beneath the surface: are AI systems creating these stereotypes, or are they simply learning them from society?
AI learns patterns, not values
AI is not neutral; it learns from historical and social data. From books and news archives to websites, social media posts, and workplace statistics, modern AI systems are trained on enormous quantities of human-generated content. If society has historically associated men with leadership and women with caregiving, AI is likely to learn those associations as statistical patterns. The real challenge emerges when these patterns are reproduced millions of times every day, shaping perceptions of what is normal, expected, or achievable.
The debate surrounding gender bias in AI is therefore not only about technology. It is also about how existing inequalities are translated into digital systems and whether AI ultimately reinforces or challenges them.
image via Magnific
How AI systems learn and reproduce gender bias
AI has often been portrayed as objective, rational, and free from human prejudice. Reality is more complicated. Machine learning models do not distinguish between desirable and undesirable social patterns. Their purpose is to identify relationships within data and use them to make predictions or generate outputs.
A landmark 2017 study published in Sciencedemonstrated that AI language models learned many of the same implicit biases found among humans. Researchers discovered that word associations frequently linked men with careers, science, and leadership, while women were more closely associated with family and domestic roles. Importantly, the systems were not instructed to adopt these views. They simply learned them from the data available to them.
From a machine-learning perspective, stereotypes are not recognised as stereotypes. They are recognised as recurring patterns.
That distinction matters. AI does not understand concepts such as fairness, equality, or discrimination. It understands probabilities. If particular associations dominate books, websites, news reports, and online discussions, AI systems are likely to absorb those associations and reproduce them in their outputs.
Much of the discussion about women and AI begins here. Gender bias in AI is often less a product of malicious design and more a reflection of the social realities embedded in training data.
image via Magnific
How AI amplifies gender stereotypes and inequality
Many experts argue that AI acts as a mirror of society. In some respects, that assessment is correct. If men currently occupy a majority of senior corporate leadership positions, the AI model that frequently depicts CEOs as male may simply be reflecting existing labour-market realities.
However, reflection is only part of the story.
Historically, stereotypes have spread through institutions, media, education systems, and interpersonal interactions. AI introduces a new dynamic because it operates at a scale no individual human can match. Search engines, recommendation systems, chatbots, virtual assistants, and generative AI platforms interact with millions of users simultaneously.
The concern, therefore, is not that AI can be biassed. Humans have always been biassed. The concern is that AI can replicate and distribute those biases with unprecedented speed, consistency, and reach.
A stereotype expressed by one individual has limited influence. A stereotype repeated by an algorithm millions of times can gradually shape expectations about who belongs in positions of authority, innovation, or expertise.
Questions surrounding AI and gender equality extend beyond technical accuracy. Even if an AI system reflects current realities, repeated exposure to those realities may reinforce the perception that they are natural, inevitable, or desirable.
image via Magnific
How AI systems portray women and gender roles
Evidence of gender stereotypes in AI has appeared across a wide range of technologies.
Image-generation systems have repeatedly associated women with caregiving and support roles while portraying men as executives, scientists, engineers, entrepreneurs, and political leaders. Similar patterns have emerged in language models, search algorithms, and recommendation systems.
Such outputs raise concerns because representation influences perception. When leadership, technical expertise, and innovation are consistently presented through a male lens, AI may unintentionally reinforce assumptions about gender and professional capability.
Researchers often describe this phenomenon as representational harm. Unlike direct discrimination, representational harm does not necessarily involve financial loss or exclusion from opportunities. Instead, it affects how groups are perceived in society and how individuals understand their own potential.
For younger generations growing up alongside AI-powered technologies, these representations may become part of the digital environment through which social norms are learned. AI increasingly shapes the way people search for information, discover role models, and imagine future careers. As a result, the way women are portrayed by AI systems has implications that extend far beyond the technology sector itself.
image via Magnific
The gender bias feedback loop in AI
One of the most important concepts in discussions about gender bias in AI is the feedback loop.
Society creates patterns and inequalities.
These patterns are recorded in digital data.
AI learns from that data.
AI systems reproduce these patterns in their outputs.
People consume these outputs and may internalise them.
New data is generated that reflects the same assumptions.
The cycle then repeats itself.
Viewed through this lens, AI becomes part of a system through which existing inequalities can be continuously reproduced and normalised.
Understanding this feedback loop shifts the debate away from the simple question of whether AI is biassed. A more important question emerges: what happens when social inequalities become embedded in technologies that many people perceive as objective and trustworthy?
That question sits at the heart of contemporary debates surrounding AI ethics, responsible AI development, and digital governance.
image via Magnific
Why women in AI governance and development still matter
Discussions about gender bias in AI often focus on the underrepresentation of women in AI and the broader technology sector. While diversity remains an important issue, it should not be viewed as a simple explanation for biassed outputs.
Increasing the number of women working in AI would not automatically eliminate stereotypes from the training data. Models trained on historical information would still learn many of the same social patterns.
However, representation becomes significant at the level of governance.
Decisions about whether biassed outputs should be corrected, contextualised, or left unchanged are ultimately human decisions. Diverse teams may be better positioned to identify harms that homogeneous groups overlook and to challenge assumptions that might otherwise remain embedded in AI systems.
The importance of women in AI, therefore, extends beyond mere representation. It relates to participation in the governance structures that determine how AI is developed, evaluated, and deployed.
The questions about fairness, accountability, and responsible AI are not purely technical. They are social and political questions that require a broad range of perspectives.
image via Magnific
The future of gender equality in AI
AI is frequently described as a transformative technology, yet its most disruptive impact may not be what it creates, but what it reveals. For centuries, societies have debated equality through laws, institutions, and cultural norms. AI introduces a different form of scrutiny. By converting human behaviour into data and data into predictions, it exposes patterns that often remain invisible until they are reflected back at scale.
In that sense, debates about women and AI are not merely debates about technology. They are discussions about who gets represented in the collective knowledge, whose experiences become part of the historical record, and which assumptions are treated as facts simply because they have been repeated often enough. As societies increasingly rely on algorithms to organise information and inform decisions, the line between what is statistically common and what is socially acceptable may become one of the defining questions of the digital age.
AI may never tell society what is right. Yet by revealing the patterns embedded in human history, it is forcing a deeper question: when machines learn from us, what exactly are we teaching them?
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The European Commission’s Directorate-General for Agriculture and Rural Development (DG AGRI) and Directorate-General for Communications Networks, Content and Technology (DG CONNECT) jointly organised an online expert workshop on 24 June to explore how to accelerate AI adaption and scale trusted AI solutions across the agriculture sector.
The workshop was organised within the framework of the Commission’s Apply AI Strategy, which aims to accelerate AI adoption in strategic sectors, including agri-food, while strengthening European competitiveness, technological sovereignty and uptake among small and medium-sized enterprises. Participants discussed AI applications already being deployed in farm management, precision agriculture, crop and livestock monitoring, advisory services, agricultural robotics and the simplification of administrative processes.
The workshop focused on three priorities: assessing the current level of AI adoption in EU agriculture, identifying barriers to wider deployment and exploring policy measures that could support greater uptake. An interactive session also examined what is needed to ensure AI solutions in agriculture are developed, tested, and validated in a trustworthy and responsible manner.
The workshop’s findings will inform a stakeholder input note identifying priority AI use cases, barriers to adoption, infrastructure and data requirements, and potential follow-up actions under the Apply AI Strategy and related EU programmes supporting the digital transition of agriculture.
Why does it matter?
The workshop illustrates how the European Commission is moving from promoting AI in principle to addressing the practical conditions needed for large-scale deployment. In agriculture, AI has the potential to improve productivity, reduce resource use and simplify administrative tasks, but broader adoption will depend on access to high-quality data, digital infrastructure, trusted solutions and support for farmers and SMEs.
The initiative also reinforces the EU’s wider strategy of linking AI deployment with competitiveness and technological sovereignty. By connecting the Apply AI Strategy with the Common Agricultural Policy, the Common European Agricultural Data Space and Horizon Europe, the Commission is seeking to build an ecosystem in which AI can be adopted responsibly while supporting the long-term digital transformation of Europe’s agri-food sector.
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Africa’s place in an evolving digital governance landscape
As AI, cybersecurity, and digital technologies become increasingly central to international policymaking, African countries are seeking to strengthen their role in shaping global digital governance. Questions of representation, digital sovereignty, capacity development, and regional coordination are becoming more prominent as governments prepare for negotiations on AI governance, cybersecurity, telecommunications, and internet governance.
These issues formed the focus of a recent Diplo webinar on Cyber Diplomacy in Africa: Regional, National and Continental Initiatives, moderated by Mwende Njiraini, African Initiative Coordinator at Diplo and Chair of the ITU-T Study Group 17 Regional Group for Africa on security. The discussion brought together policymakers, diplomats, academics, and regional organisations to examine how African interests can be more effectively represented in international digital governance processes.
Although the discussion focused on Africa, many of the issues raised, including AI governance, digital sovereignty, capacity development, and multistakeholder cooperation, reflect broader challenges facing digital governance worldwide.
From cyber diplomacy to diplomacy in the AI era
Opening the discussion, Kurbalija suggested that the distinction between cyber diplomacy, digital diplomacy, and technology diplomacy is becoming less significant as digital technologies permeate virtually every area of international relations. Rather than focusing on terminology, he argued that the central question is how countries, communities, and citizens represent their interests in an increasingly digital world.
‘Cyber diplomacy, digital diplomacy, or AI diplomacy is ultimately diplomacy. It is about representing interests, negotiating, and finding common solutions.’, he said.
According to Kurbalija, technological developments are no longer confined to specialised policy discussions. AI, cybersecurity, digital infrastructure, and data governance increasingly influence trade, security, education, healthcare, humanitarian action, and economic development, making digital issues part of mainstream diplomacy.
This evolution also raises questions about whether Africa is sufficiently represented in international discussions shaping the future of digital technologies.
Image via Magnific
Kurbalija noted that African diplomats are becoming more active in negotiations related to AI, cybersecurity, and internet governance, but argued that stronger participation will be necessary to ensure that the continent’s priorities are reflected in emerging international frameworks.
Rather than approaching these meetings individually, Kurbalija encouraged participants to prepare coordinated positions that reflect African priorities across different policy areas.
Regional coordination remains a work in progress
A recurring theme throughout the discussion was the gap between continental ambitions and national implementation.
However, she questioned whether these processes consistently translate into practical outcomes across the continent.
To illustrate this point, Getao presented the results of a live audience poll measuring familiarity with African digital governance initiatives. While approximately half of the participants recognised the AU Convention on Cyber Security and Personal Data Protection (the Malabo Convention), significantly fewer were familiar with other continental initiatives, including the AU Digital Transformation Strategy and the African Union’s position on international law in cyberspace.
The findings suggested that awareness of Africa’s existing digital governance architecture remains uneven, even among participants engaged in digital policy discussions.
Ambassador Bitange Ndemo argued that implementation presents an even greater challenge than awareness. He observed that agreements adopted at the African Union level often take considerable time to influence national policymaking, with countries frequently developing their own legal and regulatory approaches rather than building on common continental frameworks.
Using the Malabo Convention as an example, Ndemo suggested that many governments introduced separate data protection legislation without fully integrating broader continental approaches. According to him, one contributing factor is reliance on external funding for many regional digital initiatives.
‘If we continue depending on external partners to finance our priorities, ownership becomes more difficult’, Ndemo added.
Ndemo argued that stronger African investment in digital governance initiatives would improve both implementation and long-term sustainability.
Getao echoed this concern, noting that important achievements at the continental level do not always ‘percolate’ effectively to national implementation.
Building common African positions
Despite these challenges, speakers highlighted several examples of growing regional coordination.
Meriem Slimani described how the African Telecommunications Union (ATU) has worked to strengthen cooperation among member states in preparing common African positions for international telecommunications negotiations.
When she joined the organisation in 2015, Slimani recalled, many countries submitted proposals independently at international meetings, often without consulting neighbouring states.
ATU responded by creating a coordination platform through which member countries discuss priorities, identify common interests, exchange experiences, and gradually develop shared positions before major international conferences.
‘Our objective has been to ensure that Africa speaks with one voice where common interests exist.’
Image via Magnific
According to Slimani, this collaborative approach has become particularly important in preparation for major meetings of the International Telecommunication Union (ITU), where coordinated regional positions can strengthen Africa’s influence during negotiations.
While acknowledging that implementation challenges remain, he argued that progress has been more visible in some sectors than others.
In particular, COMESA has advanced several practical digital trade initiatives, including electronic trade documentation, digital logistics systems, electronic certificates of origin, and simplified digital trade procedures designed to facilitate cross-border commerce.
Governance issues such as cybersecurity and cybercrime, however, have generally progressed more slowly because they often involve more politically sensitive discussions and require broader legal coordination among participating states.
Chinemhute suggested that smaller regional organisations can sometimes move more quickly than continental institutions because they involve fewer actors and more focused policy priorities.
Looking ahead
While speakers approached Africa’s digital future from different institutional and regional perspectives, several common priorities emerged throughout the discussion. These included strengthening Africa’s participation in global digital governance processes, improving coordination among national, regional, and continental initiatives, investing in capacity development, and ensuring that digital policies reflect local realities and priorities.
The discussion also highlighted that digital governance extends beyond technology. Questions of AI, cybersecurity, connectivity, language, education, and financing were presented as interconnected challenges that require cooperation among governments, regional organisations, academia, the private sector, and civil society.
Image via Magnific
As international discussions on AI and digital governance continue through forums such as the AI for Good Global Summit, the World Summit on the Information Society (WSIS)+20 process, and the Internet Governance Forum (IGF), speakers stressed that African participation will be most effective when supported by coordinated regional positions and sustained investment in local expertise and digital capabilities.
Ultimately, the webinar underscored that Africa’s role in shaping the future of digital governance will depend not only on engagement in international negotiations but also on translating continental ambitions into practical national implementation and ensuring that African perspectives contribute to global debates on AI, cybersecurity, and digital development.
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As AI reshapes economies, societies, and governance systems worldwide, Geneva is increasingly emerging as one of the most important global centres for discussions on the future of digital technologies.
In a recent interview, Diplo Executive Director Jovan Kurbalija described Geneva as a place where multiple dimensions of AI governance intersect. From technical standards and international trade to human rights, humanitarian action, and diplomacy, the city hosts institutions and processes that shape how digital technologies are developed, governed, and used worldwide.
According to Kurbalija, a significant share of global discussions on AI and digital governance takes place within a relatively small area surrounding Geneva’s international district. The concentration of international organisations, diplomatic missions, standards-setting bodies, and expert communities has positioned the city as a unique meeting point for addressing the opportunities and challenges associated with AI.
A hub for global digital governance
Geneva’s importance in digital governance stems largely from the presence of international organisations whose work directly affects the digital ecosystem.
Among them is the World Trade Organization (WTO), which plays a role in shaping the global rules governing trade, supply chains, e-commerce, and the international movement of goods and services that underpin the digital economy. Decisions and discussions within the WTO influence the broader environment in which digital technologies are produced, exchanged, and deployed.
Another key institution is the International Telecommunication Union (ITU), the UN specialised agency for information and communication technologies. ITU has long served as a forum for international cooperation on telecommunications and digital technologies, and today plays an increasingly prominent role in discussions related to AI and digital governance.
Although often invisible to users, technical standards play a fundamental role in ensuring interoperability, connectivity, and trust in digital systems. As AI technologies become more integrated into everyday life, standards are expected to play an increasingly important role in areas such as safety, transparency, and accountability.
From Frankenstein to AI: Geneva’s intellectual legacy
Kurbalija also highlighted a less visible but equally important dimension of Geneva’s role in AI governance, its intellectual and historical heritage.
He referred to what Diplo describes as the EspriTech de Genève, the intersection between technological developments and ideas that have emerged from thinkers associated with Geneva throughout history.
One of the most notable examples is Mary Shelley, who wrote Frankenstein near Lake Geneva in 1816. Often regarded as one of the earliest works of science fiction, the novel explores the relationship between creators and their creations, raising questions about responsibility, unintended consequences, and the limits of human control.
More than two centuries later, similar questions continue to shape contemporary debates on AI governance. Discussions surrounding increasingly capable AI systems frequently return to concerns about human oversight, accountability, and the potential consequences of technologies that may act in ways not fully anticipated by their creators.
Kurbalija also pointed to the work of Argentine writer Jorge Luis Borges, whose reflections on knowledge, information, and human cognition continue to resonate in an era characterised by large-scale data processing and machine-generated content.
The intellectual traditions associated with Geneva provide a broader context for understanding contemporary AI debates, linking present-day governance questions to longer-standing discussions about technology, knowledge, and humanity.
Geneva as a centre for AI diplomacy
Beyond its historical and institutional significance, Geneva has become an increasingly active venue for international discussions on AI governance.
The city hosts a growing number of meetings, conferences, and policy dialogues dedicated to the governance of AI and other emerging technologies. Among the most prominent is the annual AI for Good Summit, organised by ITU in partnership with other UN agencies and stakeholders. The event brings together governments, international organisations, researchers, private sector representatives, and civil society to explore the societal implications of AI and identify opportunities for international cooperation.
Geneva also hosts a range of other initiatives focused on AI governance, including policy dialogues, expert consultations, and multistakeholder discussions addressing issues such as human rights, health, humanitarian action, sustainable development, trade, and technical standards.
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According to Kurbalija, AI is now on the agenda of many international organisations based in Geneva. Whether addressing healthcare, humanitarian assistance, trade, education, telecommunications, or development, institutions increasingly examine how AI affects their respective mandates and policy objectives.
This growing presence reflects the recognition that AI is not solely a technological issue. Instead, it spans multiple policy domains, requiring coordination among technical experts, policymakers, diplomats, regulators, and affected communities.
Reducing ‘lost in translation’ in AI governance
As AI discussions become more widespread, one challenge frequently identified by policymakers and international organisations is the gap between technological developments and policy understanding.
Kurbalija argues that many stakeholders remain ‘lost in translation’ when trying to understand the implications of AI. Technical terminology, rapidly evolving technologies, and complex governance debates often create barriers for diplomats, policymakers, and officials who are expected to make decisions about AI despite not having technical backgrounds.
To address this challenge, Diplo combines research, capacity development, and practical experimentation.
The organisation conducts research on both the historical roots of AI-related thinking and contemporary governance challenges. At the same time, it develops tools and educational programmes designed to help policymakers better understand the technology and its implications.
A central component of this effort is Diplo’s AI Apprenticeship programme.
Rather than teaching AI solely through theory, the programme encourages participants to learn by building AI applications themselves. Diplomats and officials from different countries work directly with AI tools, gaining practical experience with concepts such as neural networks, large language models (LLMs), and AI systems development.
According to Kurbalija, direct engagement with AI technologies allows participants to move beyond abstract discussions and develop a more practical understanding of how these systems function and where their limitations lie.
Where technology meets humanity
Kurbalija described Geneva as a place where several distinct but interconnected forces converge.
The first is the technological dimension, represented by organisations working on telecommunications, standards, digital infrastructure, and emerging technologies.
The second is the historical and intellectual dimension, reflected in the ideas of thinkers associated with Geneva and the broader region, whose work continues to inform contemporary discussions about technology and society.
Image via Freepik
The third is the diplomatic dimension. Geneva remains one of the world’s most active centres of multilateral diplomacy, hosting permanent missions and representatives from nearly every country. Discussions in Geneva frequently shape global approaches to issues ranging from trade and humanitarian affairs to digital governance and AI.
The fourth is what Kurbalija describes as the human dimension. Many Geneva-based institutions focus on protecting and advancing human welfare through work on human rights, humanitarian action, health, labour, migration, and development.
Together, these dimensions create an environment in which technological innovation can be discussed alongside its social, ethical, economic, and political implications.
Looking ahead
As governments, international organisations, and societies continue to grapple with the opportunities and risks associated with AI, Geneva’s role as a centre for digital governance is likely to become increasingly significant.
The city’s unique combination of technical expertise, standards-setting institutions, diplomatic networks, and human-centred governance traditions provides a platform for addressing complex questions that no single actor or sector can solve alone.
For Kurbalija, this convergence of technology, diplomacy, and humanity represents one of Geneva’s defining characteristics. In a period marked by rapid technological change and growing uncertainty, the city continues to serve as a place where different perspectives can meet to shape the future of AI governance.
As debates around AI evolve, Geneva is likely to remain one of the key venues where those discussions are translated into international cooperation, governance frameworks, and practical solutions with global impact.
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Google has introduced Gemini for Science, a collection of AI experiments and tools designed to support scientific discovery across research fields.
The initiative includes three experimental tools on Google Labs. Hypothesis Generation, built with Co-Scientist, helps researchers define research challenges, generate hypotheses and evaluate them through a multi-agent process. Google said the tool uses an ‘idea tournament’ in which agents generate, debate and assess possible research directions, with claims supported by clickable citations.
Computational Discovery, built with AlphaEvolve and Empirical Research Assistance, is designed to generate and score large numbers of code variations in parallel. Google said the prototype could help scientists test modelling approaches in areas such as solar forecasting and epidemiology.
Literature Insights, built with NotebookLM, searches scientific literature and organises results into structured tables for side-by-side analysis. Researchers can use it to identify research gaps, synthesise findings across papers and create outputs such as reports, slide decks and audio or video overviews.
Google said access to the experiments will open gradually through Google Labs. The company is also bringing related capabilities to enterprise organisations through Google Cloud, with partners testing tools for pharmaceutical research, crop science, supply chain optimisation and work linked to the US Department of Energy’s Genesis Mission.
As part of Gemini for Science, Google is also launching Science Skills, a bundle that integrates more than 30 life science databases and tools, including UniProt, the AlphaFold Database, AlphaGenome API and InterPro. Google said the tools can support workflows such as structural bioinformatics and genomic analysis on agentic platforms such as Google Antigravity.
The company said it is working with more than 100 institutions to validate its scientific AI systems and has created a trusted tester community that includes PhD students, industry researchers and Nobel laureates.
The launch shows how major AI developers are moving from specialised scientific models towards broader agentic tools that support hypothesis generation, literature analysis and computational testing.
Why does it matter?
Gemini for Science points to a wider shift in AI-assisted research: AI systems are moving beyond literature search or single-task modelling towards multi-step scientific workflows. Such tools help researchers navigate large bodies of literature, test computational ideas faster and identify new hypotheses. But their value will depend on evidence quality, reproducibility, peer review and clear limits around what AI-generated scientific suggestions can and cannot prove.
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Researchers led by the University of Oxford have developed an AI tool called ‘HyperScore’ that could help doctors better understand how high blood pressure affects different organs and individuals in different ways. The approach could support more personalised treatment strategies in the future.
Using the AI tool, researchers identified six distinct patterns of hypertension-related disease by analysing hundreds of measurements, including cardiac imaging, brain MRI scans, blood tests and assessments of the kidneys, liver and vascular system.
The study found that individuals with higher HyperScores faced a greater risk of future cardiovascular events, even when conventional blood pressure measurements did not fully capture that risk. Changes detected through brain MRI imaging emerged as some of the strongest indicators of hypertension-related organ damage.
The researchers analysed data from more than 27,000 participants in the UK Biobank and validated their findings in an additional cohort of more than 5,500 individuals in the US. The researchers cautioned that the approach remains at an early stage and is not yet ready for routine clinical use in the UK.
Why does it matter?
High blood pressure is one of the world’s leading risk factors for heart disease, stroke and other chronic conditions, yet patients with similar blood pressure readings can experience very different health outcomes. The study suggests that AI may help identify hidden patterns of organ damage that are not captured by conventional measurements, potentially enabling more accurate risk assessment and personalised treatment strategies.
The research also highlights the growing role of AI in precision medicine. By combining imaging, laboratory data and clinical information, AI systems may help clinicians move beyond one-size-fits-all approaches to disease management. Although HyperScore remains at an early research stage, the findings demonstrate how AI could support earlier intervention and more targeted care for patients with complex cardiovascular risks.
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Dataland, a Los Angeles museum dedicated to AI-based art, has opened to the public with Google serving as a technology and creative collaborator.
The museum was co-founded by media artist Refik Anadol and Efsun Erkılıç and is located at The Grand LA in downtown Los Angeles. Google says the 25,000-square-foot space is designed as an interactive environment where data, machine learning and sensory experiences form part of the artwork.
Its inaugural exhibition, ‘Machine Dreams: Rainforest’, uses Anadol’s Large Nature Model, an AI system trained on environmental datasets, to transform natural-world data into large-scale generative visuals.
Google Cloud provides infrastructure for the museum’s real-time image generation, soundscapes, scent augmentation and interactive visitor experiences. Google says the system uses tools including Gemini, diffusion models and generative adversarial networks.
The project builds on a decade of collaboration between Google and Anadol, including work using LA Philharmonic archives, Google Quantum AI data, planetary datasets and the ‘Machine Dreams: Biophilia’ installation at Google’s Mountain View campus.
Google Arts & Culture is also supporting the Dataland AI Artist Residency, a six-month programme for four artists. The residency will provide grants, mentorship from Refik Anadol Studio and access to Google Cloud tools and machine learning models.
Why does it matter?
Dataland shows how AI art is moving from experimental installations into permanent cultural infrastructure. It also highlights the role of cloud providers and large AI platforms in shaping creative production, exhibition design and access to machine-learning tools. For cultural institutions, the project raises broader questions about authorship, data provenance, sustainability, audience interaction and the dependence of new creative formats on private technology infrastructure.
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The opening day of Viva Technology 2026 in Paris highlighted the growing influence of AI, with discussions focusing on execution, digital sovereignty and the infrastructure needed to support rapid technological change.
Jeff Bezos introduced Prometheus, an AI venture focused on physical engineering applications, while consultancy McKinsey & Company reported that 80% of large businesses now invest in AI, although only 6% report a measurable impact on profits.
The event also highlighted Europe’s ambition to strengthen its technology ecosystem and reduce strategic dependencies in key digital sectors. European Commission Executive Vice President Henna Virkkunen outlined initiatives aimed at expanding semiconductor production, increasing data centre capacity and supporting open-source technologies across Europe.
Alongside the conference, French startup Fairpatterns was selected to represent France at the Startup World Cup in November of this year, where participants will compete for a US$1 million investment prize. The event highlighted the strength of the French startup ecosystem in Paris.
Why does it matter?
VivaTech is one of Europe’s most influential technology events and provides a useful snapshot of emerging priorities in the global digital economy. The strong focus on AI execution rather than experimentation reflects a broader shift from testing AI technologies to generating measurable business and economic value from them.
The discussions also underscore the growing importance of digital sovereignty. As governments and businesses invest in AI, semiconductors, cloud infrastructure and data centres, competitiveness is increasingly linked to control over critical digital capabilities. The event highlights how Europe is seeking to strengthen its technological position while ensuring that innovation is supported by the infrastructure and investment needed to scale advanced technologies.
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