Fraudulent investment platform Nomani has surged, spreading from Facebook to YouTube. ESET blocked tens of thousands of malicious links this year, mainly in Czech Republic, Japan, Slovakia, Spain, and Poland.
The scam utilises AI-generated videos, branded posts, and social media advertisements to lure victims into fake investments that promise high returns. Criminals then request extra fees or sensitive personal data, and often attempt a secondary scam posing as Europol or INTERPOL.
Recent improvements make Nomani’s AI videos more realistic, using trending news or public figures to appear credible. Campaigns run briefly and misuse social media forms and surveys to harvest information while avoiding detection.
Despite overall growth, detections fell 37% in the second half of 2025, suggesting that scammers are adapting to more stringent law enforcement measures. Meta’s ad platforms earned billions from scams, demonstrating the global reach of Nomani fraud.
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Japan’s competition authority will probe AI search services from major domestic and international tech firms. The investigation aims to identify potential antitrust violations rather than impose immediate sanctions.
The probe is expected to cover LY Corp., Google, Microsoft and AI providers such as OpenAI and Perplexity AI. Concerns centre on how AI systems present and utilise news content within search results.
Legal action by Japanese news organisations alleges unauthorised use of articles by AI services. Regulators are assessing whether such practices constitute abuse of market dominance.
The inquiry builds on a 2023 review of news distribution contracts that warned against the use of unfair terms for publishers. Similar investigations overseas, including within the EU, have guided the commission’s approach.
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AI is extending the clinical value of chest X-rays beyond lung and heart assessment. Researchers are investigating whether routine radiographs can support broader disease screening without the need for additional scans. Early findings suggest existing images may contain underused diagnostic signals.
A study in Radiology: Cardiothoracic Imaging examined whether AI could detect hepatic steatosis from standard frontal chest X-rays. Researchers analysed more than 6,500 images from over 4,400 patients across two institutions. Deep learning models were trained and externally validated.
The AI system achieved area-under-curve scores above 0.8 in both internal and external tests. Saliency maps showed predictions focused near the diaphragm, where part of the liver appears on chest X-rays. Results suggest that reliable signal extraction can be achieved from routine imaging.
Researchers argue the approach could enable opportunistic screening during standard care. Patients flagged by AI could be referred for a dedicated liver assessment when appropriate. The method adds clinical value without increasing imaging costs or radiation exposure.
Experts caution that the model is not a standalone diagnostic tool and requires further prospective validation. Integration with clinical and laboratory data remains necessary to reduce false positives. If validated, AI-enhanced X-rays could support scalable risk stratification.
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Use of AI chatbots for everyday tasks, from structuring essays to analysing data, has become widespread. Researchers are increasingly examining whether reliance on such tools affects critical thinking and learning. Recent studies suggest a more complex picture than simple decline.
A research study published by MIT found reduced cognitive activity among participants who used ChatGPT to write essays. Participants also showed weaker recall than those who completed tasks without AI assistance, raising questions about how learning develops when writing is outsourced.
Similar concerns emerged from studies by Carnegie Mellon University and Microsoft. Surveys of white-collar workers linked higher confidence in AI tools with lower levels of critical engagement, prompting warnings about possible overreliance.
Studies involving students present a more nuanced outcome. Research published by Oxford University Press found that many pupils felt AI supported skills such as revision and creativity. At the same time, some reported that tasks became too easy, limiting deeper learning.
Experts emphasise that outcomes depend on how AI tools are used. Educators argue for clearer guidance, transparency, and further research into long-term effects. Used as a tutor rather than a shortcut, AI may support learning rather than weaken it.
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Chief financial officers predict AI will shift finance from experimentation to enterprise-wide impact in 2026. Real-time insights, scenario modelling and strategic decision-making are expected to become central to finance functions.
Success depends on trusted data, strong governance, modernised architectures and human judgement. AI will not replace expertise, but rather reveal gaps and reward organisations that integrate AI with their strategy.
CFOs plan to use AI for capital allocation, forecasting, risk management and operational efficiency. The focus is moving from efficiency gains to transformative, high-value work that drives measurable outcomes.
Enterprise-wide adoption of AI will require robust oversight and upskilling of finance teams. Leaders who modernise systems and combine AI with human expertise will gain a competitive edge.
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A new research paper published in Aging-US uses AI to map a century of global ageing research. The study analyses how scientific priorities have shifted over time. Underexplored areas are also identified.
Researchers analysed more than 460,000 scientific abstracts published between 1925 and 2023. Natural language processing and machine learning were used to cluster themes and track trends. The aim was to provide an unbiased view of the field’s evolution.
Findings show a shift from basic biological studies toward clinical research, particularly age-related diseases such as Alzheimer’s and dementia. Basic science continues to focus on cellular mechanisms. Limited overlap persists between laboratory and clinical research.
Several fast-growing topics, including autophagy, RNA biology, and nutrient sensing, remain weakly connected to clinical applications. Strong links endure in areas such as cancer and ageing. Other associations, including epigenetics and autophagy, are rarely explored.
The analysis highlights gaps that may shape future ageing research priorities. AI-based mapping provides insights into how funding and policy shape focus areas. Greater integration could support more effective translation into clinical outcomes.
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Amid growing attention on AI, Google DeepMind chief Demis Hassabis has argued that future systems could learn anything humans can.
He suggested that as technology advances, AI may no longer remain confined to single tasks. Instead of specialising narrowly, it could solve different kinds of problems and continue improving over time.
Supporters say rapid progress already shows how powerful the technology has become.
Other experts disagree and warn that human intelligence remains deeply complex. People rely on emotions, personal experience and social understanding when they think, while machines depend on data and rules.
Critics argue that comparing AI with the human mind oversimplifies how intelligence really works, and that even people vary widely in ability.
Elon Musk has supported the idea that AI could eventually learn as much as humans, while repeating his long-standing view that powerful systems must be handled carefully. His backing has intensified the debate, given his influence in the technology world.
The discussion matters because highly capable AI could reshape work, education and creativity, raising questions over safety and control.
For now, AI performs specific tasks extremely well yet cannot think or feel like humans, and no one can say for certain whether true human-level intelligence will ever emerge.
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Researchers at Lomonosov Moscow State University have developed a 72-qubit quantum computer prototype based on single neutral rubidium atoms. It marks the third Russian quantum computer to surpass the 70-qubit milestone.
The achievement was announced by Rosatom Quantum Technologies and highlights progress in reliable quantum operations.
The atom-based prototype features three zones: one for computing and two for storage and readout. Experiments have demonstrated two-qubit logical operations with 94% accuracy, enabling practical testing and development of quantum algorithms.
Scientists stress that lower error rates are vital for scaling quantum computers to solve complex industrial and financial problems. The work also supports Russia’s technological sovereignty and strengthens the competitiveness of domestic enterprises.
The project actively involves young researchers, graduate students, and undergraduates alongside leading specialists, ensuring the next generation gains hands-on experience in one of Russia’s most significant scientific initiatives.
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UNCTAD has launched the first global database to consolidate national estimates of e‑commerce value, aiming to provide clearer insights and highlight major gaps in digital economy data.
The announcement was made during the sixth meeting of the UN Trade and Development Working Group on Measuring E-commerce, with representatives from 42 countries participating.
E-commerce and digitally delivered services are among the fastest-growing sectors of the global economy, yet most countries lack robust statistics to capture online transactions, cross-border trade, and social-media-based commerce.
Experts warned that inadequate data hinders policymaking, masks inequalities in digital access, and limits the benefits of digital transformation.
The working group recommended a 2026 review of indicators, including AI, platform business models, remote work, and fully digital services. Guidelines will be promoted via expanded capacity-building programmes, supported by the Kingdom of Saudi Arabia.
Cooperation between governments, the private sector, and international organisations is vital for consistent global measurement and to avoid duplication.
Experts called for technology-neutral, comparable frameworks and innovative tools, such as payment records and data mining, to improve global e‑commerce measurement.
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Researchers at the Icahn School of Medicine at Mount Sinai have developed an AI tool capable of predicting which critically ill ventilated patients may be underfed, potentially enabling earlier nutritional intervention in intensive care units.
NutriSighT, the AI model, analyses routine ICU data, including vital signs, lab results, medications, and feeding information. Predictions are updated every four hours, allowing clinicians to identify patients at risk of underfeeding during days three to seven of ventilation.
The study found that 41–53% of patients were underfed by day three, while 25–35% remained underfed by day seven.
The model is dynamic and interpretable, highlighting key factors such as blood pressure, sodium levels, and sedation that influence underfeeding risk. Researchers emphasise that NutriSighT supports personalised nutrition and guides clinical decisions without replacing medical judgement.
Future research will focus on prospective multi-site trials, integration with electronic health records, and expansion to broader, individualised nutrition targets. Investigators hope these advances will enhance patient outcomes and enable more tailored ICU care.
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