Harvard Health Publishing has partnered with Microsoft to use its health content to train the Copilot AI system. The collaboration seeks to enhance the accuracy of healthcare responses on Microsoft’s AI platform, according to the Wall Street Journal.
HHP publishes consumer health resources reviewed by Harvard scientists, covering topics such as sleep, nutrition, and pain management. The institution confirmed that Microsoft has paid to license its articles, expanding a previous agreement made in 2022.
The move is designed to make medically verified information more accessible to the public through Copilot, which now reaches over 33 million users.
Harvard’s Soroush Saghafian said the deal could help cut errors in AI-generated medical advice, a key concern in healthcare. He emphasised the importance of rigorous testing before deployment, warning that unverified tools could pose serious risks to users.
Harvard continues to invest in AI research and integration across its academic programmes. Recent initiatives include projects to address bias in medical training and studies exploring AI’s role in drug development and cancer treatment.
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Under the proposed controls, parents will be able to turn off chats between teens and AI characters altogether, though the broader Meta AI chatbot remains accessible. They can also block specific characters if they wish. Parents will receive topic summaries of what teens are discussing with AI characters and with Meta AI itself.
The first deployment will be on Instagram, with initial availability in English for the US, UK, Canada and Australia. Meta says it recognises the challenges parents face in guiding children through new technology, and wants these tools to simplify oversight.
Meta also notes that AI content and experiences intended for teens will follow a PG-13 standard: avoiding extreme violence, nudity and graphic drug content. Teens currently interact with only a limited set of AI characters under age-appropriate guidelines.
Additionally, Meta plans to allow time limits on AI character use by teens. The company is also detecting and discouraging attempts by users to falsify their age to bypass restrictions.
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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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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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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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A new research partnership between Google and Commonwealth Fusion Systems (CFS) aims to accelerate the development of clean, abundant fusion energy. Fusion powers the sun and offers limitless, clean energy, but achieving it on Earth requires stabilising plasma at over 100 million degrees Celsius.
The collaboration builds on prior AI research in controlling plasma using deep reinforcement learning. Google and CFS are combining AI with the SPARC tokamak, using superconducting magnets to achieve net energy gain from fusion.
AI tools such as TORAX, a fast and differentiable plasma simulator, allow millions of virtual experiments to optimise plasma behaviour before SPARC begins operations.
AI is also being applied to find the most efficient operating paths for the tokamak, including optimising magnetic coils, fuel injection, and heat management.
Reinforcement learning agents can optimise energy output in real time while safeguarding the machine, potentially exceeding human-designed methods.
The partnership combines advanced AI with fusion hardware to develop intelligent, adaptive control systems for future clean and sustainable fusion power plants.
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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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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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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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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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