Digital standards

Data Act now in force, more data sharing in EU

The EU’s Data Act is now in force, marking a major shift in European data governance. The regulation aims to expand access to industrial and Internet of Things data, giving users greater control over information they generate while maintaining safeguards for trade secrets and privacy.

Adopted as part of the EU’s Digital Strategy, the act seeks to promote fair competition, innovation, and public-sector efficiency. It enables individuals and businesses to share co-generated data from connected devices and allows public authorities limited access in emergencies or matters of public interest.

Some obligations take effect later. Requirements on product design for data access will apply to new connected devices from September 2026, while certain contract rules are deferred until 2027. Member states will set national penalties, with fines in some cases reaching up to 10% of global annual turnover.

The European Commission will assess the law’s impact within three years of its entry into force. Policymakers hope the act will foster a fairer, more competitive data economy, though much will depend on consistent enforcement and how businesses adapt their practices.

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Data labelling transforms rural economies in Tamil Nadu

India’s small towns are fast becoming global hubs for AI training and data labelling, as outsourcing firms move operations beyond major cities like Bangalore and Chennai. Lower costs and improved connectivity have driven a trend known as cloud farming, which has transformed rural employment.

In Tamil Nadu, workers annotate and train AI models for global clients, preparing data that helps machines recognise objects, text and speech. Firms like Desicrew pioneered this approach by offering digital careers close to home, reducing migration to cities while maintaining high technical standards.

Desicrew’s chief executive, Mannivannan J K, says about a third of the company’s projects already involve AI, a figure expected to reach nearly all within two years. Much of the work focuses on transcription, building multilingual datasets that teach machines to interpret diverse human voices and dialects.

Analysts argue that cloud farming could make rural India the world’s largest AI operations base, much as it once dominated IT outsourcing. Yet challenges remain around internet reliability, data security and client confidence.

For workers like Dhanalakshmi Vijay, who fine-tunes models by correcting their errors, the impact feels tangible. Her adjustments, she says, help AI systems perform better in real-world applications, improving everything from shopping recommendations to translation tools.

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Public consultation: EU clarifies how DMA and GDPR work together

The European Commission and European Data Protection Board have jointly published long-awaited guidelines clarifying how the Digital Markets Act aligns with the GDPR. It aims to remove uncertainty for large online platforms over consent requirements, data sharing amongst other things.

Under the new interpretation, gatekeepers must obtain specific and separate consent when combining user data across different services, including when using it for AI training. They cannot rely on legitimate interest or contractual necessity for such processing, closing a loophole long debated in EU privacy law.

The Guidelines also set limits on how often consent can be re-requested, prohibiting repeated or slightly altered requests for the same purpose within a year. In addition, they make clear that offering users a binary choice between accepting tracking or paying a fee will rarely qualify as freely given consent.

The Guidance also introduces a practical standard for anonymisation, requiring platforms to prevent re-identification using technical and organisational safeguards. Consultation on the Guidelines runs until 4 December 2025, after which they are expected to shape future enforcement.

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AI system could help reduce childhood obesity risk

Researchers at Penn State have developed an AI model that measures children’s bite rate during meals, aiming to address a key risk factor for obesity. Eating quickly hinders fullness signals and, combined with larger bites, increases the risk of obesity.

The AI system, named ByteTrack, was trained using over 1,400 minutes of video from a study of 94 children aged seven to nine. It recognises children’s faces with 97% accuracy and detects bites about 70% as successfully as humans.

Although the system requires further refinement, the pilot study shows promise for large-scale research and potential real-world applications. With further training, ByteTrack could become a smartphone app alerting children when they eat too quickly to encourage healthier habits.

The research was funded by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Sciences, and Penn State’s computational and clinical research institutes.

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Labels and Spotify align on artist-first AI safeguards

Spotify partners with major labels on artist-first AI tools, putting consent and copyright at the centre of product design. The plan aims to align new features with transparent labelling and fair compensation while addressing concerns about generative music flooding platforms.

The collaboration with Sony, Universal, Warner, and Merlin will give artists control over participation in AI experiences and how their catalogues are used. Spotify says it will prioritise consent, clearer attribution, and rights management as it builds new tools.

Early direction points to expanded labelling via DDEX, stricter controls against mass AI uploads, and protections against search and recommendation manipulation. Spotify’s AI DJ and prompt-based playlists hint at how engagement features could evolve without sidelining creators.

Future products are expected to let artists opt in, monitor usage, and manage when their music feeds AI-generated works. Rights holders and distributors would gain better tracking and payment flows as transparency improves across the ecosystem.

Industry observers say the tie-up could set a benchmark for responsible AI in music if enforcement matches ambition. By moving in step with labels, Spotify is pitching a path where innovation and artist advocacy reinforce rather than undermine each other.

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Veo 3.1 brings audio and control to AI filmmaking

Google DeepMind has unveiled Veo 3.1, the newest upgrade to its video generation model, bringing more artistic freedom, realism and sound integration to its AI filmmaking tool, Flow.

The update gives creators advanced scene control and introduces generated audio across existing features like ‘Ingredients to Video’, ‘Frames to Video’ and ‘Extend’.

Users can now fine-tune visuals by combining multiple reference images, seamlessly link frames into longer clips, and edit scenes with new insert and removal tools that handle shadows and lighting automatically.

Flow’s new precision tools mark a significant step toward cinematic-level storytelling powered by AI.

Veo 3.1 is also accessible through the Gemini API, Vertex AI and the Gemini app, broadening its availability to developers and enterprises alike.

These enhancements signal Google’s ongoing ambition to push the boundaries of generative video technology for creative and professional applications.

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Google and Salesforce deepen AI partnership across Agentforce 360 and Gemini Enterprise

Salesforce and Google have expanded their long-term partnership, introducing new integrations between Salesforce’s Agentforce 360 platform and Google’s Gemini Enterprise. The collaboration aims to enhance productivity and build a new foundation for intelligent, connected business operations.

Through the expansion, Gemini models now power Salesforce’s Atlas Reasoning Engine, combining multimodal intelligence with hybrid reasoning to improve how AI agents handle complex, multistep enterprise tasks.

These integrations also extend across Google Workspace, bringing Agentforce 360 capabilities directly into Gmail, Meet, Docs, Sheets and Drive for sales, service and IT teams.

Salesforce highlights that fine-tuned Gemini models outperform competing LLMs on key CRM benchmarks, enabling businesses to automate workflows more reliably and consistently.

The companies also reaffirm their commitment to open standards like Model Context Protocol and Agent2Agent, allowing multi-agent collaboration and interoperability across enterprise systems.

A partnership that further integrates Gemini Enterprise with Slack’s real-time search API, enabling users to draw insights directly from organisational data within conversations.

Both companies stress that these advances mark a major step toward an ‘Agentic Enterprise’, where AI systems work alongside people to drive innovation, improve service quality and streamline decision-making.

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Between trips, Uber pilots paid AI data work

Uber is piloting ‘Digital Tasks’ in the US, letting select drivers and couriers earn by training AI models between trips.

Tasks include short selfie videos in any language, uploading multilingual documents, and uploading category-tagged images; each takes minutes, and pay varies by task.

Uber says demand came from US drivers seeking off-road income; participants can opt in via the Work Hub and need no extra experience.

Partners commissioning the data aren’t named. The pilot starts later this year, with potential expansion to non-drivers and wider markets.

The move diversifies beyond rides and delivery as robotaxis loom. Uber argues for more earning channels now, while autonomy scales over time.

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Nurses gain AI support as Microsoft evolves Dragon Copilot in healthcare

Microsoft has announced major AI upgrades to Dragon Copilot, its healthcare assistant, extending ambient and generative AI capabilities to nursing workflows and third-party partner integrations.

The update is designed to improve patient journeys, reduce administrative workloads and enhance efficiency across healthcare systems.

The new features allow partners to integrate their own AI applications directly into Dragon Copilot, helping clinicians access trusted information, automate documentation and streamline financial management without leaving their workflow.

Partnerships with Elsevier, Wolters Kluwer, Atropos Health, Canary Speech and others will provide real-time decision support, clinical insights and revenue cycle automation.

Microsoft is also introducing the first commercial ambient AI solution built for nurses, designed to reduce burnout and enhance care quality.

A technology that automatically records nurse-patient interactions and transforms them into editable documentation for electronic health records, saving time and supporting accuracy.

Nurses can also access medical content within the same interface and automate note-taking and summaries, allowing greater focus on patient care.

The company says these developments mark a new phase in its AI strategy for healthcare, strengthening its collaboration with providers and partners.

Microsoft aims to make clinical workflows more connected, reliable and human-centred, while supporting safe, evidence-based decision-making through its expanding ecosystem of AI tools.

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AI predicts how cells respond to drugs and genes

KAIST researchers have developed AI that predicts cell responses to drugs and genes, with potential to transform drug discovery, cancer therapy, and regenerative medicine. The method models cell-drug interactions in a modular ‘Lego block’ approach, enabling analysis of previously untested combinations.

The AI separates representations of cell states and drug effects in a ‘latent space’ and recombines them to forecast reactions. The system can predict gene effects on cells, providing a quantitative view of drug and genetic impacts.

Validation using real experimental data demonstrated the AI’s ability to identify molecular targets that restored colorectal cancer cells to a normal-like state.

Beyond cancer treatment, the platform is versatile, capable of predicting diverse cell-state transitions and drug responses. The technology shows how drugs work inside cells, offering a powerful tool to design therapies that guide cells toward desired outcomes.

The study, led by Professor Kwang-Hyun Cho with his KAIST team, was published in Cell Systems and supported by the National Research Foundation of Korea. Researchers highlight the AI framework’s broad use, from restoring cells to developing new therapies.

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