An AI algorithm paired with smartwatch sensors has successfully detected structural heart diseases, including valve damage and weakened heart muscles, in adults. The study, conducted at Yale School of Medicine, will be presented at the American Heart Association’s 2025 Scientific Sessions in New Orleans.
The AI model was trained on over 266,000 electrocardiogram recordings and validated across multiple hospitals and population studies. When tested on 600 participants using single-lead ECGs from a smartwatch, it achieved an 88% accuracy in detecting heart disease.
Researchers said smartwatches could offer a low-cost, accessible method for early screening of structural heart conditions that usually require echocardiograms. The algorithm’s ability to analyse single-lead ECG data could enable preventive detection before symptoms appear.
Experts emphasised that smartwatch data cannot replace medical imaging, but it could complement clinical assessments and expand access to screening. Larger studies in the US are planned to confirm effectiveness and explore community-based use in preventive heart care.
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The US R&D company, OpenAI, has introduced IndQA, a new benchmark designed to test how well AI systems understand and reason across Indian languages and cultural contexts. The benchmark covers 2,278 questions in 12 languages and 10 cultural domains, from literature and food to law and spirituality.
Developed with input from 261 Indian experts, IndQA evaluates AI models through rubric-based grading that assesses accuracy, cultural understanding, and reasoning depth. Questions were created to challenge leading OpenAI models, including GPT-4o and GPT-5, ensuring space for future improvement.
India was chosen as the first region for the initiative, reflecting its linguistic diversity and its position as ChatGPT’s second-largest market.
OpenAI aims to expand the approach globally, using IndQA as a model for building culturally aware benchmarks that help measure real progress in multilingual AI performance.
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Researchers at MIT’s Computer Science and AI Lab (CSAIL) are collaborating with Adobe to create Refashion, a new AI-driven design tool promoting sustainable fashion. The software deconstructs clothing into modules, allowing designers and consumers to reimagine garments for reuse or transformation.
Users can utilise the AI to sketch shapes and combine elements to create adaptable pieces, such as a skirt that transforms into a dress or maternity wear that evolves throughout pregnancy. The system provides blueprints for flexible, reconfigurable designs that reduce waste.
Lead researcher Rebecca Lin said the project encourages reuse from the outset, contrasting with the disposable nature of fast fashion. By making clothing easy to resize, repair and restyle, Refashion aims to extend each item’s lifespan and reduce environmental impact.
MIT Professor Erik Demaine described Refashion as a bridge between computation, art and design, envisioning it as a tool that makes creative fashion accessible while embedding sustainability into every stage of garment creation.
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Amazon has launched Alexa+ within the Amazon Music app, introducing a new era of AI-powered music discovery. The updated experience allows users to engage in natural conversations about songs, artists and genres, making music searches feel more like chatting with a knowledgeable friend.
Early Access users on iOS and Android can now explore the feature, which has already tripled user engagement compared with the original Alexa. Listeners can uncover artist influences, trace song origins, and generate playlists through dynamic, dialogue-based AI interactions.
Alexa+ creates contextually rich recommendations based on moods, activities, or cultural styles, enabling highly personalised playlists that evolve in real-time. Users can request specific vibes, such as upbeat 2010s hits or relaxed Sunday tunes, all crafted through natural language.
Amazon said Alexa+ is redefining how people connect with music by merging conversational AI with deep cultural knowledge. A full rollout is expected following the Early Access phase, with broader availability to Prime and non-Prime users.
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AI is inserting itself between companies and customers, Cloudflare CEO Matthew Prince warned in Toronto. More people ask chatbots before visiting sites, dulling brands’ impact. Even research teams lose revenue as investors lean on AI summaries.
Frontier models devour data, pushing firms to chase exclusive sources. Cloudflare lets publishers block unpaid crawlers to reclaim control and compensation. The bigger question, said Prince, is which business model will rule an AI-mediated internet.
Policy scrutiny focuses on platforms that blend search with AI collection. Prince urged governments to separate Google’s search access from AI crawling to level the field. Countries that enforce a split could attract publishers and researchers seeking predictable rules and payment.
Licensing deals with news outlets, Reddit, and others coexist with scraping disputes and copyright suits. Google says it follows robots.txt, yet testimony indicated AI Overviews can use content blocked by robots.txt for training. Vague norms risk eroding incentives to create high-quality online content.
A practical near-term playbook combines technical and regulatory steps. Publishers should meter or block AI crawlers that do not pay. Policymakers should require transparency, consent, and compensation for high-value datasets, guiding the shift to an AI-mediated web that still rewards creators.
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Alibaba unveiled Qwen3-Max-Thinking, which scored 100 percent on AIME 2025 and HMMT, matching OpenAI’s top model on reasoning tests. It targets high-precision problem-solving across algebra, number theory, and probability. Researchers regard elite maths contests as strong proxies for reasoning.
Built on Qwen3-Max, a trillion-parameter flagship, the thinking variant emphasises step-by-step solutions. Alibaba says it matches or beats Claude Opus 4, DeepSeek V3.1, Grok 4, and GPT-5 Pro. Positioning stresses accuracy, traceability, and controllable latency.
Signal from a live trading trial added momentum. In a two-week crypto experiment, Qwen3-Max returned 22.3 percent on 10,000 US dollars. Competing systems underperformed, with DeepSeek at 4.9 percent and several US models booking losses.
Access is available via the Qwen web chatbot and Alibaba Cloud APIs. Early adopters can test tool use and stepwise reasoning on technical tasks. Enterprises are exploring finance, research, and operations cases requiring reliability and auditability.
Alibaba researchers say further tuning will broaden task coverage without diluting peak maths performance. Plans include multilingual reasoning, safety alignment, and robustness under distribution shift. Community benchmarks and contests will track progress.
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People Inc. has joined Microsoft’s publisher content marketplace in a pay-per-use deal that compensates media for AI access. Copilot will be the first buyer, while People Inc. continues to block most AI crawlers via Cloudflare to force paid licensing.
People Inc., formerly Dotdash Meredith, said Microsoft’s marketplace lets AI firms pay ‘à la carte’ for specific content. The agreement differs from its earlier OpenAI pact, which the company described as more ‘all-you-can-eat’, but the priority remains ‘respected and paid for’ use.
Executives disclosed a sharp fall in Google search referrals: from 54% of traffic two years ago to 24% last quarter, citing AI Overviews. Leadership argues that crawler identification and paid access should become the norm as AI sits between publishers and audiences.
Blocking non-paying bots has ‘brought almost everyone to the table’, People Inc. said, signalling more licences to come. Such an approach by Microsoft is framed as a model for compensating rights-holders while enabling AI tools to use high-quality, authorised material.
IAC reported People Inc. digital revenue up 9% to $269m, with performance marketing and licensing up 38% and 24% respectively. The publisher also acquired Feedfeed, expanding its food vertical reach while pursuing additional AI content partnerships.
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Scientists at UC San Diego used AI and molecular biology to show how a broken NOD2–girdin partnership causes chronic inflammation in Crohn’s disease. The study explains why some macrophages become inflammatory instead of restorative, leading to intestinal damage.
The study analysed thousands of macrophage genes, identifying 53 that separate inflammatory cells from healing ones. One key discovery revealed that NOD2 normally binds to girdin in non-inflammatory macrophages, keeping inflammation under control.
Mutations in NOD2, common in Crohn’s patients, disrupt this connection, tipping the immune system toward persistent gut inflammation.
Animal studies confirmed the findings. Mice lacking girdin developed severe intestinal inflammation, altered gut microbiomes, and in many cases, fatal sepsis.
The experiments showed that without the NOD2–girdin interaction, the gut’s immune balance collapses, highlighting the importance of this partnership for intestinal health.
By combining AI, genetic analysis, and animal models, the study opens new avenues for Crohn’s therapies. Researchers aim to restore the NOD2–girdin interaction to rebalance macrophages and ease chronic inflammation.
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Mercedes-Benz reported faster decisions and better on-time delivery at Celosphere 2025. Using Celonis within MO360, it unifies production and logistics data, extending visibility across every order, part, and process.
Order-to-delivery operations use AI copilots to forecast timelines, optimise sequencing, and cut delays. After-sales teams surface bottlenecks in service parts logistics and speed customer responses. Quality management utilises anomaly detection to identify deviations early, preventing them from impacting production output.
Executives say complete data transparency enables teams to act faster and with greater precision across production and supply chains. The approach helps anticipate change and react to market shifts. Hundreds of active users are expanding adoption as data-driven practices scale across the company.
Celonis positions process intelligence as the backbone that makes enterprise AI valuable. Integrated process data and business context create a live operational twin. The goal is moving from visibility to action, unlocking value through targeted fixes and intelligent automation.
Conference sessions highlighted broader momentum for process intelligence and AI in industry. Leaders discussed governance, standards, and measurable outcomes from digital platforms. Mercedes-Benz framed its results as proof that structured data and AI can lift performance at a global scale.
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Nissan and Monolith have extended their strategic partnership for three years to apply AI across more vehicle programmes in Europe. The collaboration supports Nissan. RE: Nissan Plans to Compress Development Timelines and Improve Operational Efficiency. Early outcomes are guiding a wider rollout.
Engineers at Nissan Technical Centre Europe will utilise Monolith to predict test results based on decades of data and simulations. Reducing prototypes and conducting targeted, high-value experiments enables teams to focus more effectively on design decisions. Ensuring both accuracy and coverage remains essential.
A prior project on chassis bolt joints saw AI recommend optimal torque ranges and prioritise the following best tests for engineers. Compared with the non-AI process, physical testing fell by 17 percent in controlled comparisons. Similar approaches are being prepared for future models beyond LEAF.
Leaders say that a broader deployment could halve testing time across European programmes if comparable gains are achieved. Governance encompasses rigorous validation before changes are deployed to production. Operational benefits include faster iteration cycles and reduced test waste.
Monolith’s toolkit includes next-test recommendation and anomaly detection to flag outliers before rework. Nissan frames the push as an innovation with sustainability benefits, cutting material use while maintaining quality across a complex supply chain. Partners will share results as adoption scales.
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