Future of work

Rapid Fusion unveils AI assistant for 3D printing

Exeter technology firm Rapid Fusion has introduced an AI-powered print assistant to enhance its robotic additive manufacturing systems. Known as Bob, the system has been in development for eight months and is now being rolled out to clients.

The AI aims to simplify machine operation, provide greater control and reduce downtime through predictive maintenance.

It is compatible with the company’s Apollo, Zeus and Medusa models, including the first UK large-format hybrid 3D gantry printer.

Rapid Fusion’s chief technology officer, Martin Jewell, said the system represents a breakthrough in making complex 3D printing more accessible. A standard version will be released in early 2026, while select partners and universities will act as super users to refine future updates.

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Apple creates Asa chatbot for staff training

Apple is moving forward with its integrated approach to AI by testing an internal chatbot designed for retail training. The company focuses on embedding AI into existing services rather than launching a consumer-facing chatbot like Google’s Gemini or ChatGPT.

The new tool, Asa, is being tested within Apple’s SEED app, which offers training resources for store employees and authorised resellers. Asa is expected to improve learning by allowing staff to ask open-ended questions and receive tailored responses.

Screenshots shared by analyst Aaron Perris show Asa handling queries about device features, comparisons, and use cases. Although still in testing, the chatbot is expected to expand across Apple’s retail network in the coming weeks.

The development occurs amid broader AI tensions, as Elon Musk’s xAI sued Apple and OpenAI for allegedly colluding to limit competition. Apple’s focus on internal AI tools like Asa contrasts with Musk’s legal action, highlighting disputes over AI market dominance and platform integration.

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Walmart rolls out AI agents to transform shopping and operations

Walmart has unveiled four AI agents to ease the workloads of shoppers, employees, and suppliers. The tools, revealed at the company’s Retail Rewired event, include Marty for suppliers, Sparky for customers, an Associate Agent for staff, and a Developer Agent.

The retailer is leaning on AI as inflation, tariffs, and policy pressures weigh on consumer spending. Its agents cover payroll, time-off requests, merchandising, and personalised shopping recommendations.

Sparky is set to eventually handle automatic reordering of staples, aiming to simplify everyday restocking for households.

Walmart is also investing in ‘digital twins,’ virtual replicas of stores that allow early detection of operational issues. The company says this technology cut emergency alerts by 30% last year and reduced refrigeration maintenance costs by nearly a fifth.

Machine learning is further being applied to improve delivery-time predictions, helping to boost efficiency and customer satisfaction.

Rival retailers are making similar moves. Amazon reported a surge in generative AI use during its Prime Day sales, while Google Cloud AI has partnered with Lush to cut training costs.

Analysts suggest such tools could reshape the retail experience as companies search for ways to hold margins in a tighter economy.

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Beijing seeks to curb excess AI investment while sustaining growth

China has pledged to rein in excessive competition in AI, signalling Beijing’s desire to avoid wasteful investment while keeping the technology central to its economic strategy.

The National Development and Reform Commission stated that provinces should develop AI in a coordinated manner, leveraging local strengths to prevent duplication and overlap. Officials in China emphasised the importance of orderly flows of talent, capital, and resources.

The move follows President Xi Jinping’s warnings about unchecked local investment. Authorities aim to prevent overcapacity problems, such as those seen in electric vehicles, which have fueled deflationary pressures in other industries.

While global investment in data centres has surged, Beijing is adopting a calibrated approach. The state also vowed stronger national planning and support for private firms, aiming to nurture new domestic leaders in AI.

At the same time, policymakers are pushing to attract private capital into traditional sectors, while considering more central spending on social projects to ease local government debt burdens and stimulate long-term consumption.

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Meta faces turmoil as AI hiring spree backfires

Mark Zuckerberg’s ambitious plan to assemble a dream team of AI researchers at Meta has instead created internal instability.

High-profile recruits poached from rival firms have begun leaving within weeks of joining, citing cultural clashes and frustration with the company’s working style. Their departures have disrupted projects and unsettled long-time executives.

Meta had hoped its aggressive hiring spree would help the company rival OpenAI, Google, and Anthropic in developing advanced AI systems.

Instead of strengthening the company’s position, the strategy has led to delays in projects and uncertainty about whether Meta can deliver on its promises of achieving superintelligence.

The new arrivals were given extensive autonomy, fuelling tensions with existing teams and creating leadership friction. Some staff viewed the hires as destabilising, while others expressed concern about the direction of the AI division.

The resulting turnover has left Meta struggling to maintain momentum in its most critical area of research.

As Meta faces mounting pressure to demonstrate progress in AI, the setbacks highlight the difficulty of retaining elite talent in a fiercely competitive field.

Zuckerberg’s recruitment drive, rather than propelling Meta ahead, risks slowing down the company’s ability to compete at the highest level of AI development.

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Stethoscope with AI identifies heart issues in seconds

A new stethoscope powered by AI could enable doctors to identify three serious heart conditions in just seconds, according to UK researchers.

The device replaces the traditional chest piece with a small sensor that records both electrical signals from the heart and the sound of blood flow, which are then analysed in the cloud by AI trained on large datasets.

The AI tool has shown strong results in trials across more than 200 GP practices, with patients tested using the stethoscope being more than twice as likely to be diagnosed with heart failure within 12 months compared with those assessed through usual care.

It was also 3.45 times more likely to detect atrial fibrillation and almost twice as likely to identify heart valve disease.

Researchers from Imperial College London and Imperial College Healthcare NHS Trust said the technology could help doctors provide treatment at an earlier stage instead of waiting until patients present in hospital with advanced symptoms.

The findings, known as Tricorder, will be presented at the European Society of Cardiology Congress in Madrid.

The project, supported by the National Institute for Health and Care Research, is now preparing for further rollouts in Wales, south London and Sussex. Experts described the innovation as a significant step in updating a medical tool that has remained largely unchanged for over 200 years.

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How local LLMs are changing AI access

As AI adoption rises, more users explore running large language models (LLMs) locally instead of relying on cloud providers.

Local deployment gives individuals control over data, reduces costs, and avoids limits imposed by AI-as-a-service companies. Users can now experiment with AI on their own hardware thanks to software and hardware capabilities.

Concerns over privacy and data sovereignty are driving interest. Many cloud AI services retain user data for years, even when privacy assurances are offered.

By running models locally, companies and hobbyists can ensure compliance with GDPR and maintain control over sensitive information while leveraging high-performance AI tools.

Hardware considerations like GPU memory and processing power are central to local LLM performance. Quantisation techniques allow models to run efficiently with reduced precision, enabling use on consumer-grade machines or enterprise hardware.

Software frameworks like llama.cpp, Jan, and LM Studio simplify deployment, making local AI accessible to non-engineers and professionals across industries.

Local models are suitable for personalised tasks, learning, coding assistance, and experimentation, although cloud models remain stronger for large-scale enterprise applications.

As tools and model quality improve, running AI on personal devices may become a standard alternative, giving users more control over cost, privacy, and performance.

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India to host OpenAI’s new Stargate data centre

OpenAI is preparing to build a significant new data centre in India as part of its Stargate AI infrastructure initiative. The move will expand the company’s presence in Asia and strengthen its operations in its second-largest market by user base.

OpenAI has already registered as a legal entity in India and begun assembling a local team.

The company plans to open its first office in New Delhi later this year. Details regarding the exact location and timeline of the proposed data centre remain unclear, though CEO Sam Altman may provide further information during his upcoming visit to India.

The project represents a strategic step to support the company’s growing regional AI ambitions.

OpenAI’s Stargate initiative, announced by US President Donald Trump in January, involves private sector investment of up to $500 billion for AI infrastructure, backed by SoftBank, OpenAI, and Oracle.

The initiative seeks to develop large-scale AI capabilities across major markets worldwide, with the India data centre potentially playing a key role in the efforts.

The expansion highlights OpenAI’s focus on scaling its AI infrastructure while meeting regional demand. The company intends to strengthen operational efficiency, improve service reliability, and support its long-term growth in Asia by establishing local offices and a significant data centre.

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Legal barriers and low interest delay Estonia’s AI rollout in schools

Estonia’s government-backed AI teaching tool, developed under the €1 million TI-Leap programme, faces hurdles before reaching schools. Legal restrictions and waning student interest have delayed its planned September rollout.

Officials in Estonia stress that regulations to protect minors’ data remain incomplete. To ensure compliance, the Ministry of Education is drafting changes to the Basic Schools and Upper Secondary Schools Act.

Yet, engagement may prove to be the bigger challenge. Developers note students already use mainstream AI for homework, while the state model is designed to guide reasoning rather than supply direct answers.

Educators say success will depend on usefulness. The AI will be piloted in 10th and 11th grades, alongside teacher training, as studies have shown that more than 60% of students already rely on AI tools.

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Microsoft launches new AI models MAI-Voice-1 and MAI-1 Preview

Microsoft has unveiled two new AI models, marking a major step in its efforts to build its own technology rather than rely solely on OpenAI.

The first model, MAI-Voice-1, generates high-fidelity audio and supports both single and multi-speaker scenarios. Microsoft said the system can create a full minute of expressive audio in under a second on a single GPU, making it one of the fastest of its kind.

MAI-Voice-1 is already available in Copilot Daily and Podcasts, while Copilot Labs allows users to experiment with storytelling and speech demos. Microsoft sees voice as a vital interface for future AI companions.

MAI-1 Preview is currently undergoing community testing on LMArena and will soon be integrated into selected Copilot use cases. Microsoft said it plans to expand its family of specialised models, aiming to orchestrate different systems for diverse user needs.

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