Meta has restricted access in India to two AI-generated videos posted by the Congress party. The clips depicted Prime Minister Narendra Modi alongside Gautam Adani, Chairman of the Adani Group.
The company stated that the content did not violate its community standards. Action followed takedown notices issued by Delhi Police under India’s information technology laws.
Meta warned that ignoring the orders could jeopardise safe harbour protections. Loss of those protections would expose platforms to direct legal liability.
The case highlights growing scrutiny of political AI content in India. Recent rule changes have tightened procedures for ordering online takedowns.
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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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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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Prime Minister Kim Min-seok has called for punitive fines of up to 10 percent of company sales for repeated and serious data breaches, as public anger grows over large-scale leaks.
The government is seeking swift legislation to impose stronger sanctions on firms that fail to safeguard personal data, reflecting President Lee Jae Myung’s stance that violations require firm penalties instead of lenient warnings.
Kim said corporate responses to recent breaches had fallen far short of public expectations and stressed that companies must take full responsibility for protecting customer information.
Under the proposed framework, affected individuals would receive clearer notifications that include guidance on their rights to seek damages.
The government of South Korea also plans to strengthen investigative powers through coercive fines for noncompliance, while pursuing rapid reforms aimed at preventing further harm.
The tougher line follows a series of major incidents, including a leak at Shinhan Card that affected around 190,000 merchant records and a large-scale breach at Coupang that exposed the data of 33.7 million users.
Officials have described the Coupang breach as a serious social crisis that has eroded public trust.
Authorities have launched an interagency task force to identify responsibility and ensure tighter data protection across South Korea’s digital economy instead of relying on voluntary company action.
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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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If you are piecing together a new workstation or gaming rig, or just hunting for extra RAM or SSD storage, you have stumbled into the worst possible moment. With GPU prices already sky-high, the recent surge in RAM and storage costs has hit consumers hard, leaving wallets lighter and sparking fresh worries about where the tech market is headed.
On the surface, the culprit behind these soaring prices is a sudden RAM shortage. Prices for 32GB and 64GB sticks have skyrocketed by as much as 600 percent, shelves are emptying fast, and the balance between supply and demand has completely unraveled.
But blaming the sky-high prices on empty shelves only tells part of the story. Why has affordable RAM vanished? How long will this chaos last? And most intriguingly, what role does AI play in this pricing storm?
Tracing the causes of RAM pricing spikes
The US tariffs imposed on China on 1 August 2025, played a substantial role in the increase in DRAM prices. Global imports of various goods have become more costly, investments and workforce onboarding have been halted, and many businesses relying on imports have adopted a ‘wait-and-see’ approach to how they will do business going forward.
However, the worst was yet to come. On 3 December, Micron, one of the world’s leading manufacturers of data storage and computer memory components, announced its withdrawal from the RAM consumer market, citing a ‘surge in demand for memory and storage’ driven by supply shortages of memory and storage for AI data centres.
With Micron out of the picture, we are left with only two global consumer RAM and high-bandwidth memory (HBM) manufacturers: Samsung and SK Hynix. While there are countless RAM brands on the market, with Corsair, Kingston, and Crucial leading the charge, all of them rely on the three aforementioned suppliers for memory chips.
Micron’s exit was likely met with obscured glee by Samsung and SK Hynix of South Korea, who seized the opportunity to take over Crucial’s surrendered territory and set the stage for their DRAM/HBM supply duel. The latter supplier was quick to announce the completion of its M15X semiconductor fabrication plant (fab), but warned that RAM supply constraints are likely to last until 2028 at the earliest.
Amid the ruckus, rumours surfaced that Samsung would be sunsetting its SATA SSD production, which the company quickly extinguished. On the contrary, the Korean giant announced its intention to dethrone SK Hynix as the top global RAM provider, with more than 80 percent of its projected profits coming directly from Samsung Electronics.
Despite their established market shares, both enterprises were caught off guard when their main rival threw in the towel, and their production facilities are unable, at current capacity, to accommodate the resulting market void. It is nigh certain that the manufacturers will use their newly gained market dominance to their advantage, setting prices based on their profit margins and customers’ growing demand for their products. In a nutshell, they have the baton, and we must play to their tune.
AI infrastructure and the reallocation of RAM supply
Micron, deeming commodity RAM a manufacturing inconvenience, made a move that was anything but rash. In October, Samsung and SK Hynix joined forces with OpenAI to supply the AI giant with a monthly batch of 900,000 DRAM wafers. OpenAI’s push to enhance its AI infrastructure and development was presumably seen by Micron as a gauntlet thrown by its competitors, and Crucial’s parent company took no time in allocating its forces to a newly opened front.
Lured by lucrative, long-term, high-volume contracts, all three memory suppliers saw AI as an opportunity to open new income streams that would not dry up for years to come. While fears of the AI bubble bursting are omnipresent and tangible, neither Samsung, SK Hynix, nor Micron are overly concerned about what the future holds for LLMs and AGI, as long as they continue to get their RAM money’s worth (literally).
AI has expanded across multiple industries, and three competitors judged Q4 2025 the opportune time to put all their RAM eggs in one basket. AI as a business model has yet to reach profitability, but corporate investors poured more than USD 250 billion into AI in 2024 alone. Predictions for 2025 have surpassed the USD 500 billion mark, but financiers will inevitably grow more selective as the AI startup herd thins and predicted cash cows fail to deliver future profits.
To justify massive funding rounds, OpenAI, Microsoft, Google, and other major AI players need to keep their LLMs in a perpetual growth cycle by constantly expanding their memory capacity. A hyperscale AI data centre can contain tens of thousands to hundreds of thousands of GPUs, each with up to 180 gigabytes of VRAM. Multiply that by 1,134, the current number of hyperscale data centres, and it is easy to see why Micron was eager to ditch the standard consumer market for more bankable opportunities.
The high demand for RAM has changed the ways manufacturers view risk and opportunity. AI infrastructure brings more volume, predictability, and stable contracts than consumer markets, especially during uncertain times and price swings. Even if some areas of AI do not meet long-term hopes, the need for memory in the near and medium term is built into data centre growth plans. For memory makers, shifting capacity to AI is a practical response to current market incentives, not just a risky bet on a single trend.
The aftermath of the RAM scarcity
The sudden price inflation and undersupply of RAM have affected more than just consumers building high-end gaming PCs and upgrading laptops. Memory components are critical to all types of devices, thereby affecting the prices of smartphones, tablets, TVs, game consoles, and many other IoT devices. To mitigate production costs and maintain profit margins, device manufacturers are tempted to offer their products with less RAM, resulting in substandard performance at the same price.
Businesses that rely on servers, cloud services, or data processing are also expected to get caught in the RAM crossfire. Higher IT costs are predicted to slow down software upgrades, digital services, and cybersecurity improvements. Every SaaS company, small or large, risks having its platforms overloaded or its customers’ data compromised.
Public institutions, such as schools, hospitals, and government agencies, will also have to bend backwards to cover higher hardware costs due to more expensive RAM. Operating on fixed budgets allows only so much wiggle room to purchase the required software and hardware, likely leading to delays in public digital projects and the continued use of outdated electronic equipment.
Rising memory costs also influence innovation and competition. When basic components become more expensive, it is harder for new companies to enter the market or scale up their services. This can favour large, well-funded firms and reduce diversity in the tech ecosystem. Finally, higher RAM prices can indirectly affect digital access and inclusion. More expensive devices and services make it harder for individuals and communities to afford modern technology, widening existing digital divides.
In short, when RAM becomes scarce or expensive, the effects extend far beyond memory pricing, influencing how digital services are accessed, deployed, and maintained across the economy. While continued investment in more capable AI models is a legitimate technological goal, it also raises a practical tension.
Advanced systems deliver limited value if the devices and infrastructure most people rely on lack the memory capacity required to run them efficiently. The challenge of delivering advanced AI models and AI-powered apps to subpar devices is one that AI developers will have to take into account moving forward. After all, what good is a state-of-the-art LLM if a run-of-the-mill PC or smartphone lacks the RAM to handle it?
The road ahead for RAM supply and pricing
As mentioned earlier, some memory component manufacturers predict that the RAM shortage will remain a burr under consumers’ saddles for at least a few years. Pompous predictions of the AI bubble’s imminent bursting have mostly ended up in the ‘I’ll believe it when I see it’ archive section, across the hall from the ‘NFTs are the future of digital ownership’ district.
Should investments continue to fill the budgets of OpenAI, Perplexity, Anthropic, and the rest, they will have the resources to reinforce their R&D departments, acquire the necessary memory components, and further develop their digital infrastructure. In the long run, the technology powering AI models may become more sophisticated to the point where energy demands reach a plateau. In that case, opportunities for expansion would be limitless.
Even though one of the biggest RAM manufacturers has fully shifted to making AI infrastructure components, there is still a gap large enough to be filled by small- and medium-sized producers. Companies such as Nanya Technology from Taiwan or US-based Virtium hold a tenth of the overall market share, but they have been given the opportunity to carry Micron’s torch and maintain competitiveness in their own capacities.
The current RAM price crisis is not caused by a single event, but by the way new technologies are changing the foundations of the digital economy. As AI infrastructure takes up more of the global memory supply, higher prices and limited availability are likely to continue across consumer, business, and public-sector markets. How governments, manufacturers, and buyers respond will shape not only the cost of hardware but also how accessible and resilient digital systems remain.
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