ai

Samsung brings AI-powered service tool to India

Samsung, already the leading home appliance brand in India by volume, is now enhancing its after-sales service with an AI-powered support tool.

The tech company from South Korea has introduced the Home Appliances Remote Management (HRM) tool, designed to improve service speed, accuracy, and overall customer experience instead of sticking with traditional support methods.

The HRM tool allows customer care teams to remotely diagnose and resolve issues in Samsung smart appliances connected via SmartThings. If a problem can be fixed remotely, staff will ask for the user’s consent before taking control of the device.

If the issue can be solved by the customer, step-by-step instructions are provided instead of sending a technician straight away.

When neither of these options applies, the issue is forwarded directly to service technicians with full diagnostics already completed, cutting down the time spent on-site.

The new system reduces the need for in-home visits, shortens waiting times, and increases the uptime of appliances instead of leaving users waiting unnecessarily.

SmartThings also plays a proactive role by automatically detecting issues and offering solutions before customers even need to call.

Samsung India’s Vice President for Customer Satisfaction, Sunil Cutinha, noted that the tool significantly streamlines service, boosts maintenance efficiency, and helps ensure timely product support for users across the country.

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Nvidia brings AI supercomputer production to the US

Nvidia is shifting its AI supercomputer manufacturing operations to the United States for the first time, instead of relying on a globally dispersed supply chain.

In partnership with industry giants such as TSMC, Foxconn, and Wistron, the company is establishing large-scale facilities to produce its advanced Blackwell chips in Arizona and complete supercomputers in Texas. Production is expected to reach full scale within 12 to 15 months.

Over a million square feet of manufacturing space has been commissioned, with key roles also played by packaging and testing firms Amkor and SPIL.

The move reflects Nvidia’s ambition to create up to half a trillion dollars in AI infrastructure within the next four years, while boosting supply chain resilience and growing its US-based operations instead of expanding solely abroad.

These AI supercomputers are designed to power new, highly specialised data centres known as ‘AI factories,’ capable of handling vast AI workloads.

Nvidia’s investment is expected to support the construction of dozens of such facilities, generating hundreds of thousands of jobs and securing long-term economic value.

To enhance efficiency, Nvidia will apply its own AI, robotics, and simulation tools across these projects, using Omniverse to model factory operations virtually and Isaac GR00T to develop robots that automate production.

According to CEO Jensen Huang, bringing manufacturing home strengthens supply chains and better positions the company to meet the surging global demand for AI computing power.

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Zhipu AI launches free agent to rival DeepSeek

Chinese AI startup Zhipu AI has introduced a free AI agent, AutoGLM Rumination, aimed at assisting users with tasks such as web browsing, travel planning, and drafting research reports.

The product was unveiled by CEO Zhang Peng at an event in Beijing, where he highlighted the agent’s use of the company’s proprietary models—GLM-Z1-Air for reasoning and GLM-4-Air-0414 as the foundation.

According to Zhipu, the new GLM-Z1-Air model outperforms DeepSeek’s R1 in both speed and resource efficiency. The launch reflects growing momentum in China’s AI sector, where companies are increasingly focusing on cost-effective solutions to meet rising demand.

AutoGLM Rumination stands out in a competitive landscape by being freely accessible through Zhipu’s official website and mobile app, unlike rival offerings such as Manus’ subscription-only AI agent. The company positions this move as part of a broader strategy to expand access and adoption.

Founded in 2019 as a spinoff from Tsinghua University, Zhipu has developed the GLM model series and claims its GLM4 has surpassed OpenAI’s GPT-4 on several evaluation benchmarks.

In March, Zhipu secured major government-backed investment, including a 300 million yuan (US$41.5 million) contribution from Chengdu.

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Meta to use EU user data for AI training amid scrutiny

Meta Platforms has announced it will begin using public posts, comments, and user interactions with its AI tools to train its AI models in the EU, instead of limiting training data to existing US-based inputs.

The move follows the recent European rollout of Meta AI, which had been delayed since June 2024 due to data privacy concerns raised by regulators. The company said EU users of Facebook and Instagram would receive notifications outlining how their data may be used, along with a link to opt out.

Meta clarified that while questions posed to its AI and public content from adult users may be used, private messages and data from under-18s would be excluded from training.

Instead of expanding quietly, the company is now making its plans public in an attempt to meet the EU’s transparency expectations.

The shift comes after Meta paused its original launch last year at the request of Ireland’s Data Protection Commission, which expressed concerns about using social media content for AI development. The move also drew criticism from advocacy group NOYB, which has urged regulators to intervene more decisively.

Meta joins a growing list of tech firms under scrutiny in Europe. Ireland’s privacy watchdog is already investigating Elon Musk’s X and Google for similar practices involving personal data use in AI model training.

Instead of treating such probes as isolated incidents, the EU appears to be setting a precedent that could reshape how global companies handle user data in AI development.

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X faces EU probe over AI data use

Elon Musk’s X platform is under formal investigation by the Irish Data Protection Commission over its alleged use of public posts from EU users to train the Grok AI chatbot.

The probe is centred on whether X Internet Unlimited Company, the platform’s newly renamed Irish entity, has adhered to key GDPR principles while sharing publicly accessible data, like posts and interactions, with its affiliate xAI, which develops the chatbot.

Concerns have grown over the lack of explicit user consent, especially as other tech giants such as Meta signal similar data usage plans.

A move like this is part of a wider regulatory push in the EU to hold AI developers accountable instead of allowing unchecked experimentation. Experts note that many AI firms have deployed tools under a ‘build first, ask later’ mindset, an approach at odds with Europe’s strict data laws.

Should regulators conclude that public data still requires user consent, it could force a dramatic shift in how AI models are developed, not just in Europe but around the world.

Enterprises are now treading carefully. The investigation into X is already affecting AI adoption across the continent, with legal and reputational risks weighing heavily on decision-makers.

In one case, a Nordic bank halted its AI rollout midstream after its legal team couldn’t confirm whether European data had been used without proper disclosure. Instead of pushing ahead, the project was rebuilt using fully documented, EU-based training data.

The consequences could stretch far beyond the EU. Ireland’s probe might become a global benchmark for how governments view user consent in the age of data scraping and machine learning.

Instead of enforcement being region-specific, this investigation could inspire similar actions from regulators in places like Singapore and Canada. As AI continues to evolve, companies may have no choice but to adopt more transparent practices or face a rising tide of legal scrutiny.

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TheStage AI makes neural network optimisation easy

In a move set to ease one of the most stubborn hurdles in AI development, Delaware-based startup TheStage AI has secured $4.5 million to launch its Automatic NNs Analyzer (ANNA).

Instead of requiring months of manual fine-tuning, ANNA allows developers to optimise AI models in hours, cutting deployment costs by up to five times. The technology is designed to simplify a process that has remained inaccessible to all but the largest tech firms, often limited by expensive GPU infrastructure.

TheStage AI’s system automatically compresses and refines models using techniques like quantisation and pruning, adapting them to various hardware environments without locking users into proprietary platforms.

Instead of focusing on cloud-based deployment, their models, called ‘Elastic models’, can run anywhere from smartphones to on-premise GPUs. This gives startups and enterprises a cost-effective way to adjust quality and speed with a simple interface, akin to choosing video resolution on streaming platforms.

Backed by notable investors including Mehreen Malik and Atlantic Labs, and already used by companies like Recraft.ai, the startup addresses a growing need as demand shifts from AI training to real-time inference.

Unlike competitors acquired by larger corporations and tied to specific ecosystems, TheStage AI takes a dual-market approach, helping both app developers and AI researchers. Their strategy supports scale without complexity, effectively making AI optimisation available to teams of any size.

Founded by a group of PhD holders with experience at Huawei, the team combines deep academic roots with practical industry application.

By offering a tool that streamlines deployment instead of complicating it, TheStage AI hopes to enable broader use of generative AI technologies in sectors where performance and cost have long been limiting factors.

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Nvidia expands AI chip production in the US amid political pressure and global shifts

Nvidia is significantly ramping up its presence in the United States by commissioning over a million square feet of manufacturing space in Arizona and Texas to build and test its powerful AI chips. The tech giant has begun producing its Blackwell chips at TSMC facilities in Phoenix and is developing large-scale ‘supercomputer’ manufacturing plants in partnership with Foxconn in Houston and Wistron in Dallas.

The company projects mass production to begin within the next 12 to 15 months, with ambitions to manufacture up to half a trillion dollars’ worth of AI infrastructure in the US over the next four years. CEO Jensen Huang emphasised that this move marks the first time the core components of global AI infrastructure are being built domestically.

He cited growing global demand, supply chain resilience, and national security as key reasons for the shift. Nvidia’s decision follows an agreement with the Trump administration that helped the company avoid export restrictions on its H20 chip, a top-tier processor still eligible for export to China.

Nvidia joins a broader wave of AI industry leaders aligning with the Trump administration’s ‘America-first’ strategy. Companies like OpenAI and Microsoft have pledged massive investments in US-based AI infrastructure, hoping to secure political goodwill and avoid regulatory hurdles.

Trump has also reportedly pressured key suppliers like TSMC to expand American operations, threatening tariffs as high as 100% if they fail to comply. Despite the enthusiasm, Nvidia’s expansion faces headwinds.

A shortage of skilled workers and potential retaliation from China—particularly over raw material access—pose serious risks. Meanwhile, Trump’s recent moves to undermine the Chips Act, which provides critical funding for domestic chipmaking, have raised concerns about the long-term viability of US semiconductor investment.

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New AI tool helps spot cataracts in babies

A groundbreaking medical device designed to detect cataracts in newborns is being enhanced with the help of AI. The Neocam, a handheld digital imaging tool created by Addenbrooke’s eye surgeon, Dr Louise Allen, allows midwives to take photos of a baby’s eyes to spot congenital cataracts — the leading cause of preventable childhood blindness.

A new AI feature under development will instantly assess whether a photo is clear enough for diagnosis, streamlining the process and reducing the need for retakes. The improvements are being developed by Cambridgeshire-based consultancy 42 Technology (42T), whose software engineers train a machine-learning model using a vast dataset of 46,000 anonymised images.

The AI project is backed by an innovation grant from Addenbrooke’s Charitable Trust (ACT) to make Neocam more accurate and accessible, especially in areas with limited specialist care. Neocam is currently being trialled in maternity units across the UK as part of a large-scale study called DIvO, where over 140,000 babies will have their eyes screened using both traditional methods and the new device.

Although the final results are not expected until 2027, early findings suggest Neocam has already identified rare visual conditions that would have otherwise gone undetected. Dr Allen emphasised the importance of collaboration and public support for the project, saying that the AI-enhanced Neocam could make early detection of eye conditions more reliable worldwide.

Why does it matter?

With growing support from institutions like the National Institute for Health and Care Research and ACT, this innovation could significantly improve childhood eye care across both urban and remote settings.

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AI voice hacks put fake Musk and Zuckerberg at crosswalks

Crosswalk buttons in several Californian cities have been hacked to play AI-generated voices impersonating tech moguls Elon Musk and Mark Zuckerberg, delivering bizarre and satirical messages to pedestrians.

The spoof messages, which mock the CEOs with lines like ‘Can we be friends?’ and ‘Cooking our grandparents’ brains with AI slop,’ have been heard in Palo Alto, Redwood City, and Menlo Park.

US Palo Alto officials confirmed that 12 intersections were affected and the audio systems have since been disabled.

While the crosswalk signals themselves remain operational, authorities are investigating how the hack was carried out. Similar issues are being addressed in nearby cities, with local governments moving quickly to secure the compromised systems.

The prank, which uses AI voice cloning, appears to layer these spoofed messages on top of the usual accessibility features rather than replacing them entirely.

Though clearly comedic in intent, the incident has raised concerns about the growing ease with which public systems can be manipulated using generative technologies.

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AI could be Geneva’s lifeline in times of crisis

International Geneva is at a crossroads. With mounting budget cuts, declining trust in multilateralism, and growing geopolitical tensions, the city’s role as a hub for global cooperation is under threat.

In his thought-provoking blog, ‘Don’t waste the crisis: How AI can help reinvent International Geneva’, Jovan Kurbalija, Executive Director of Diplo, argues that AI could offer a way forward—not as a mere technological upgrade but as a strategic tool for transforming the city’s institutions and reviving its humanitarian spirit. Kurbalija envisions AI as a means to re-skill Geneva’s workforce, modernise its organisations, and preserve its vast yet fragmented knowledge base.

With professions such as translators, lawyers, and social scientists potentially playing pivotal roles in shaping AI tools, the city can harness its multilingual, highly educated population for a new kind of innovation. A bottom-up approach is key: practical steps like AI apprenticeships, micro-learning platforms, and ‘AI sandboxes’ would help institutions adapt at their own pace while avoiding the pitfalls of top-down tech imposition.

Organisations must also rethink how they operate. AI offers the chance to cut red tape, lighten the administrative burden on NGOs, and flatten outdated hierarchies in favour of more agile, data-driven decision-making.

At the same time, Geneva can lead by example in ethical AI governance—by ensuring accountability, protecting human rights and knowledge, and defending what Kurbalija calls our ‘right to imperfection’ in an increasingly optimised world. Ultimately, Geneva’s challenge is not technological—it’s organisational.

As AI tools become cheaper and more accessible, the real work lies in how institutions and communities embrace change. Kurbalija proposes a dedicated Geneva AI Fund to support apprenticeships, ethical projects, and local initiatives. He argues that this crisis could be Geneva’s opportunity to reinvent itself for survival and to inspire a global model of human-centred AI governance.

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