In the face of growing labor shortages and need for sustainability, European manufacturers are racing to reinvent their processes to become software-defined and AI-driven. To achieve this, robot developers and industrial digitalization solution providers are working with NVIDIA to build safe, AI-driven robots and industrial technologies to drive modern, sustainable manufacturing. At NVIDIA GTC Paris at VivaTech, Europe’s leading robotics companies including Agile Robots, Extend Robotics, Humanoid, idealworks, Neura Robotics, SICK, Universal Robots, Vorwerk and Wandelbots are showcasing their latest AI-driven robots and automation breakthroughs, all accelerated by NVIDIA technologies. In addition, NVIDIA is releasing new models and tools to support the entire robotics ecosystem. NVIDIA Releases Tools for Accelerating Robot Development and Safety NVIDIA Isaac GR00T N1.5, an open foundation model for humanoid robot reasoning and skills, is now available for download on Hugging Face. This update enhances the model’s adaptability and ability to follow instructions, significantly improving its performance in material handling and manufacturing tasks. The NVIDIA Isaac Sim 5.0 and Isaac Lab 2.2 open-source robotics simulation and learning frameworks, optimized for NVIDIA RTX PRO 6000 workstations, are available on GitHub for developer preview. In addition, NVIDIA announced that NVIDIA Halos — a full-stack, comprehensive safety system that unifies hardware architecture, AI models, software, tools and services — now expands to robotics, promoting safety across the entire development lifecycle of AI-driven robots. The NVIDIA Halos AI Systems Inspection Lab has earned accreditation from the ANSI National Accreditation Board (ANAB) to perform inspections across functional safety for robotics, in addition to automotive vehicles. “NVIDIA’s latest evaluation with ANAB verifies the demonstration of competence and compliance with internationally recognized standards, helping ensure that developers of autonomous machines — from automotive to robotics — can meet the highest benchmarks for functional safety,” said R. Douglas Leonard Jr., executive director of ANAB. Arcbest, Advantech, Bluewhite, Boston Dynamics, FORT, Inxpect, KION, NexCobot — a NEXCOM company, and Synapticon are among the first robotics companies to join the Halos Inspection Lab, ensuring their products meet NVIDIA safety and cybersecurity requirements. To support robotics leaders in strengthening safety across the entire development lifecycle of AI-driven robots, Halos will now provide: Safety extension packages for the NVIDIA IGX platform, enabling manufacturers to easily program safety functions into their robots, supported by TÜV Rheinland’s inspection of NVIDIA IGX. A robotic safety platform, which includes IGX and NVIDIA Holoscan Sensor Bridge for a unified approach to designing sensor-to-compute architecture with built-in AI safety. An outside-in safety AI inspector — an AI-powered agent for monitoring robot operations, helping improve worker safety. Europe’s Robotics Ecosystem Builds on NVIDIA’s Three Computers Europe’s leading robotics developers and solution providers are integrating the NVIDIA Isaac robotics platform to train, simulate and deploy robots across different embodiments. Agile Robots is post-training the GR00T N1 model in Isaac Lab to train its dual-arm manipulator robots, which run on NVIDIA Jetson hardware, to execute a variety of tasks in industrial environments. Meanwhile, idealworks has adopted the Mega NVIDIA Omniverse Blueprint for robotic fleet simulation to extend the blueprint’s capabilities to humanoids. Building on the VDA 5050 framework, idealworks contributes to the development of guidance that supports tasks uniquely enabled by humanoid robots, such as picking, moving and placing objects. Neura Robotics is integrating NVIDIA Isaac to further enhance its robot development workflows. The company is using GR00T-Mimic to post-train the Isaac GR00T N1 robot foundation model for its service robot MiPA. Neura is also collaborating with SAP and NVIDIA to integrate SAP’s Joule agents with its robots, using the Mega NVIDIA Omniverse Blueprint to simulate and refine robot behavior in complex, realistic operational scenarios before deployment. Vorwerk is using NVIDIA technologies to power its AI-driven collaborative robots. The company is post-training GR00T N1 models in Isaac Lab with its custom synthetic data pipeline, which is built on Isaac GR00T-Mimic and powered by the NVIDIA Omniverse platform. The enhanced models are then deployed on NVIDIA Jetson AGX, Jetson Orin or Jetson Thor modules for advanced, real-time home robotics. Humanoid is using NVIDIA’s full robotics stack, including Isaac Sim and Isaac Lab, to cut its prototyping time down by six weeks. The company is training its vision language action models on NVIDIA DGX B200 systems to boost the cognitive abilities of its robots, allowing them to operate autonomously in complex environments using Jetson Thor onboard computing. Universal Robots is introducing UR15, its fastest collaborative robot yet, to the European market. Using UR’s AI Accelerator — developed on NVIDIA Isaac’s CUDA-accelerated libraries and AI models, as well as NVIDIA Jetson AGX Orin — manufacturers can build AI applications to embed intelligence into the company’s new cobots. Wandelbots is showcasing its NOVA Operating System, now integrated with Omniverse, to simulate, validate and optimize robotic behaviors virtually before deploying them to physical robots. Wandelbots also announced a collaboration with EY and EDAG to offer manufacturers a scalable automation platform on Omniverse that speeds up the transition from proof of concept to full-scale deployment. Extend Robotics is using the Isaac GR00T platform to enable customers to control and train robots for industrial tasks like visual inspection and handling radioactive materials. The company’s Advanced Mechanics Assistance System lets users collect demonstration data and generate diverse synthetic datasets with NVIDIA GR00T-Mimic and GR00T-Gen to train the GR00T N1 foundation model. SICK is enhancing its autonomous perception solutions by integrating new certified sensor models — as well as 2D and 3D lidars, safety scanners and cameras — into NVIDIA Isaac Sim. This enables engineers to virtually design, test and validate machines using SICK’s sensing models within Omniverse, supporting processes spanning product development to large-scale robotic fleet management. Toyota Material Handling Europe is working with SoftServe to simulate its autonomous mobile robots working alongside human workers, using the Mega NVIDIA Omniverse Blueprint. Toyota Material Handling Europe is testing and simulating a multitude of traffic scenarios — allowing the company to refine its AI algorithms before real-world deployment. NVIDIA’s partner ecosystem is enabling European industries to tap into intelligent, AI-powered robotics. By harnessing advanced simulation, digital twins and generative AI, manufacturers are rapidly developing and deploying safe, adaptable robot fleets that address labor shortages, boost sustainability and drive operational efficiency. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions. See notice regarding software product information.

AI is packing and shipping efficiency for the retail and consumer packaged goods (CPG) industries, with a majority of surveyed companies in the space reporting the technology is increasing revenue and reducing operational costs. Global brands are reimagining every facet of their businesses with AI, from how products are designed and manufactured to how they’re marketed, shipped and experienced in-store and online. At NVIDIA GTC Paris at VivaTech, industry leaders including L’Oréal, LVMH and Nestlé shared how they’re using tools like AI agents and physical AI — powered by NVIDIA AI and simulation technologies — across every step of the product lifecycle to enhance operations and experiences for partners, customers and employees. 3D Digital Twins and AI Transform Marketing, Advertising and Product Design The meeting of generative AI and 3D product digital twins results in unlimited creative potential. Nestlé, the world’s largest food and beverage company, today announced a collaboration with NVIDIA and Accenture to launch a new, AI-powered in-house service that will create high-quality product content at scale for e-commerce and digital media channels. The new content service, based on digital twins powered by the NVIDIA Omniverse platform, creates exact 3D virtual replicas of physical products. Product packaging can be adjusted or localized digitally, enabling seamless integration into various environments, such as seasonal campaigns or channel-specific formats. This means that new creative content can be generated without having to constantly reshoot from scratch. Image courtesy of Nestlé The service is developed in partnership with Accenture Song, using Accenture AI Refinery built on NVIDIA Omniverse for advanced digital twin creation. It uses NVIDIA AI Enterprise for generative AI, hosted on Microsoft Azure for robust cloud infrastructure. Nestlé already has a baseline of 4,000 3D digital products — mainly for global brands — with the ambition to convert a total of 10,000 products into digital twins in the next two years across global and local brands. LVMH, the world’s leading luxury goods company, home to 75 distinguished maisons, is bringing 3D digital twins to its content production processes through its wine and spirits division, Moët Hennessy. The group partnered with content configuration engine Grip to develop a solution using the NVIDIA Omniverse platform, which enables the creation of 3D digital twins that power content variation production. With Grip’s solution, Moët Hennessy teams can quickly generate digital marketing assets and experiences to promote luxury products at scale. The initiative, led by Capucine Lafarge and Chloé Fournier, has been recognized by LVMH as a leading approach to scaling content creation. Image courtesy of Grip L’Oréal Gives Marketing and Online Shopping an AI Makeover Innovation starts at the drawing board. Today, that board is digital — and it’s powered by AI. L’Oréal Groupe, the world’s leading beauty player, announced its collaboration with NVIDIA today. Through this collaboration, L’Oréal and its partner ecosystem will leverage the NVIDIA AI Enterprise platform to transform its consumer beauty experiences, marketing and advertising content pipelines. “AI doesn’t think with the same constraints as a human being. That opens new avenues for creativity,” said Anne Machet, global head of content and entertainment at L’Oréal. “Generative AI enables our teams and partner agencies to explore creative possibilities.” CreAItech, L’Oréal’s generative AI content platform, is augmenting the creativity of marketing and content teams. Combining a modular ecosystem of models, expertise, technologies and partners — including NVIDIA — CreAltech empowers marketers to generate thousands of unique, on-brand images, videos and lines of text for diverse platforms and global audiences. The solution empowers L’Oréal’s marketing teams to quickly iterate on campaigns that improve consumer engagement across social media, e-commerce content and influencer marketing — driving higher conversion rates. Noli.com, the first AI-powered multi-brand marketplace startup founded and backed by the  L’Oréal Groupe, is reinventing how people discover and shop for beauty products. Noli’s AI Beauty Matchmaker experience uses L’Oréal Groupe’s century-long expertise in beauty, including its extensive knowledge of beauty science, beauty tech and consumer insights, built from over 1 million skin data points and analysis of thousands of product formulations. It gives users a BeautyDNA profile with expert-level guidance and personalized product recommendations for skincare and haircare. “Beauty shoppers are often overwhelmed by choice and struggling to find the products that are right for them,” said Amos Susskind, founder and CEO of Noli. “By applying the latest AI models accelerated by NVIDIA and Accenture to the unparalleled knowledge base and expertise of the L’Oréal Groupe, we can provide hyper-personalized, explainable recommendations to our users.”  https://blogs.nvidia.com/wp-content/uploads/2025/06/Noli_Demo.mp4 The Accenture AI Refinery, powered by NVIDIA AI Enterprise, will provide the platform for Noli to experiment and scale. Noli’s new agent models will use NVIDIA NIM and NVIDIA NeMo microservices, including NeMo Retriever, running on Microsoft Azure. Rapid Innovation With the NVIDIA Partner Ecosystem NVIDIA’s ecosystem of solution provider partners empowers retail and CPG companies to innovate faster, personalize customer experiences, and optimize operations with NVIDIA accelerated computing and AI. Global digital agency Monks is reshaping the landscape of AI-driven marketing, creative production and enterprise transformation. At the heart of their innovation lies the Monks.Flow platform that enhances both the speed and sophistication of creative workflows through NVIDIA Omniverse, NVIDIA NIM microservices and Triton Inference Server for lightning-fast inference. AI image solutions provider Bria is helping retail giants like Lidl and L’Oreal to enhance marketing asset creation. Bria AI transforms static product images into compelling, dynamic advertisements that can be quickly scaled for use across any marketing need. The company’s generative AI platform uses NVIDIA Triton Inference Server software and the NVIDIA TensorRT software development kit for accelerated inference, as well as NVIDIA NIM and NeMo microservices for quick image generation at scale. Physical AI Brings Acceleration to Supply Chain and Logistics AI’s impact extends far beyond the digital world. Physical AI-powered warehousing robots, for example, are helping maximize efficiency in retail supply chain operations. Four in five retail companies have reported that AI has helped reduce supply chain operational costs, with 25% reporting cost reductions of at least 10%. Technology providers Lyric, KoiReader Technologies and Exotec are tackling the challenges of integrating AI into complex warehouse environments. Lyric is using the NVIDIA cuOpt GPU-accelerated solver for warehouse network planning and route optimization, and is collaborating with NVIDIA to apply the technology to broader supply chain decision-making problems. KoiReader Technologies is tapping the NVIDIA Metropolis stack for its computer vision solutions within logistics, supply chain and manufacturing environments using the KoiVision Platform. And Exotec is using NVIDIA CUDA libraries and the NVIDIA JetPack software development kit for embedded robotic systems in warehouse and distribution centers. From real-time robotics orchestration to predictive maintenance, these solutions are delivering impact on uptime, throughput and cost savings for supply chain operations. Learn more by joining a follow-up discussion on digital twins and AI-powered creativity with Microsoft, Nestlé, Accenture and NVIDIA at Cannes Lions on Monday, June 16. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions.

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[Ruud], the creator of [Capturing Dust], started his latest video with what most of us would consider a solved problem: the dust collection system for his shop already had a …read more

The choice between hardware and software for electronics projects is generally a straighforward one. For simple tasks we might build dedicated hardware circuits out of discrete components for reliability and …read more

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La industria marítima está experimentando una transformación importante gracias a la impresión 3D de gran formato. El grupo holandés CEAD, especialista en fabricación aditiva a gran escala, ha inaugurado recientemente su Maritime Application Center (

Monitoring and Support EngineerKeyword StudiosPasig City Metro Manila Philippines2 hours agoApplyWe are seeking an experienced Monitoring and Support Engineer to support the technology initiatives of the IT Infrastructure team at Keywords. The Monitoring and Support Engineer will be responsible for follow-the-sun monitoring of IT infrastructure, prompt reaction on all infrastructure incident, primary resolution of infrastructure incidents and support requests.ResponsibilitiesFull scope of tasks including but not limited to:Ensure that all incidents are handled within SLAs.Initial troubleshooting of Infrastructure incidents.Ensure maximum network & service availability through proactive monitoring.Ensure all the incident and alert tickets contain detailed technical information.Initial troubleshooting of Infrastructure incidents, restoration of services and escalation to level 3 experts if necessary.Participate in Problem management processes.Ensure that all incidents and critical alerts are documented and escalated if necessary.Ensure effective communication to customers about incidents and outages.Identify opportunities for process improvement and efficiency enhancements.Participate in documentation creation to reduce BAU support activities by ensuring that the Service Desks have adequate knowledge articles to close support tickets as level 1.Participate in reporting on monitored data and incidents on company infrastructure.Implement best practices and lessons learned from initiatives and projects to optimize future outcomes.RequirementsBachelor's degree in a relevant technical field or equivalent experience.Understanding of IT Infrastructure technologies, standards and trends.Technical background with 3+ years’ experience in IT operations role delivering IT infrastructure support, monitoring and incident management.Technical knowledge of the Microsoft Stack, Windows networking, Active Directory, ExchangeTechnical knowledge of Network, Storage and Server equipment, virtualization and production setupsExceptional communication and presentation skills, with the ability to articulate technical concepts to non-technical audiences.Strong analytical and problem-solving skills.Strong customer service orientation.BenefitsGreat Place to Work certified for 4 consecutive yearsFlexible work arrangementGlobal exposure Create Your Profile — Game companies can contact you with their relevant job openings. Apply

Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)

The government has axed a scheme for upgrading energy efficiency in public sector buildings. The Public Sector Decarbonisation Scheme (PSDS) delivered more than £2.5bn in its first three phases for measures such as heat pumps, solar panels, insulation and double glazing, with further funding of nearly £1bn recently announced. But the Department for Energy Security and Net Zero (DESNZ) has told Building Design that the scheme has been dropped after the spending review, leaving uncertainty about how upgrades will be funded when the current phase expires in 2028. Source: UK Government/FlickrEd Miliband’s Department for Energy Security and Net Zero is responsible for the scheme The department said it would set out plans for the period after 2028 in due course. In a post on LinkedIn, Dave Welkin, director of sustainability at Gleeds, said he had waited for the release of the spending review with a “sense of trepidation” and was unable to find mention of public sector decarbonisation when Treasury documents were released. “I hoped because it was already committed in the Budget that its omission wasn’t ominous,” he wrote. Yesterday, he was told by Salix Finance, the non-departmental public body that delivers funding for the scheme, that it was no longer being funded. It comes after the withdrawal of funding for the Low Carbon Skills Fund (LCSF) in May. According to the government’s website, PSDS and LCSF were intended to support the reduction of emissions from public sector buildings by 75% by 2037, compared to a 2017 baseline. “Neither LCSF or PSDS were perfect by any means, but they did provide a vital source of funding for local authorities, hospitals, schools and many other public sector organisations to save energy, carbon and money,” Welkin said. “PSDS has helped replace failed heating systems in schools, keeping students warm. It’s replaced roofs on hospitals, helping patients recover from illness. It’s replaced windows in our prisons, improving security and stopping drugs getting behind bars.” However, responding to Welkin’s post, Steve Connolly, chief executive at Arriba Technologies, a low carbon heating and cooling firm, said that the scheme was being “mismanaged” with a small number of professional services firms “scooping up disproportionately large grants for their clients”. The fourth phase of the scheme was confirmed last September, with allocations confirmed only last month. This latest phase, which covers the financial years between 2025/26 and 2027/28, saw the distribution of £940m across the country. A DESNZ spokesperson said: “Our settlement is about investing in Britain’s renewal to create energy security, sprint to clean power by 2030, encourage investment, create jobs and bring down bills for good. “We will deliver £1bn in current allocations of the Public Sector Decarbonisation Scheme until 2028 and, through Great British Energy, have invested in new rooftop solar power and renewable schemes to lower energy bills for schools and hospitals across the UK. “We want to build on this progress by incentivising the public sector to decarbonise, so they can reap the benefits in lower bills and emissions, sharing best practice across government and exploring the use of repayable finance, where appropriate.” A government assessment of phase 3a and 3b projects identified a number of issues with the scheme, including delays and cost inflation, with more than a tenth being abandoned subsequent to grants being offered. Stakeholders interviewed for the report also identified “difficulties in obtaining skilled contractors and equipment”, especially air source heat pumps. The first come first served approach to awarding funding was also said to be “encouraging applicants to opt for more straightforward projects” and “potentially undermining the achievement of PSDS objective by restricting the opportunity for larger [and] more complex measures which may have delivered greater carbon reduction benefits”. But the consensus among stakeholders and industry representatives interviewed for the report was that the scheme was “currently key to sustaining the existing UK heat pump market” and that it was “seen as vital in enabling many public sector organisations to invest in heat decarbonisation”.