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Nvidia’s Cosmos-Transfer1 makes robot training freakishly realistic—and that changes everything

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia has released Cosmos-Transfer1, an innovative AI model that enables developers to create highly realistic simulations for training robots and autonomous vehicles. Available now on Hugging Face, the model addresses a persistent challenge in physical AI […]

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Nvidia has released Cosmos-Transfer1, an innovative AI model that enables developers to create highly realistic simulations for training robots and autonomous vehicles. Available now on Hugging Face, the model addresses a persistent challenge in physical AI development: bridging the gap between simulated training environments and real-world applications.

“We introduce Cosmos-Transfer1, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmentation, depth, and edge,” Nvidia researchers state in a paper published alongside the release. “This enables highly controllable world generation and finds use in various world-to-world transfer use cases, including Sim2Real.”

Unlike previous simulation models, Cosmos-Transfer1 introduces an adaptive multimodal control system that allows developers to weight different visual inputs—such as depth information or object boundaries—differently across various parts of a scene. This breakthrough enables more nuanced control over generated environments, significantly improving their realism and utility.

How adaptive multimodal control transforms AI simulation technology

Traditional approaches to training physical AI systems involve either collecting massive amounts of real-world data — a costly and time-consuming process — or using simulated environments that often lack the complexity and variability of the real world.

Cosmos-Transfer1 addresses this dilemma by allowing developers to use multimodal inputs (like blurred visuals, edge detection, depth maps, and segmentation) to generate photorealistic simulations that preserve crucial aspects of the original scene while adding natural variations.

“In the design, the spatial conditional scheme is adaptive and customizable,” the researchers explain. “It allows weighting different conditional inputs differently at different spatial locations.”

This capability proves particularly valuable in robotics, where a developer might want to maintain precise control over how a robotic arm appears and moves while allowing more creative freedom in generating diverse background environments. For autonomous vehicles, it enables the preservation of road layout and traffic patterns while varying weather conditions, lighting, or urban settings.

Physical AI applications that could transform robotics and autonomous driving

Dr. Ming-Yu Liu, one of the core contributors to the project, explained why this technology matters for industry applications.

“A policy model guides a physical AI system’s behavior, ensuring that the system operates with safety and in accordance with its goals,” Liu and his colleagues note in the paper. “Cosmos-Transfer1 can be post-trained into policy models to generate actions, saving the cost, time, and data needs of manual policy training.”

The technology has already demonstrated its value in robotics simulation testing. When using Cosmos-Transfer1 to enhance simulated robotics data, Nvidia researchers found the model significantly improves photorealism by “adding more scene details and complex shading and natural illumination” while preserving the physical dynamics of robot movement.

For autonomous vehicle development, the model enables developers to “maximize the utility of real-world edge cases,” helping vehicles learn to handle rare but critical situations without needing to encounter them on actual roads.

Inside Nvidia’s strategic AI ecosystem for physical world applications

Cosmos-Transfer1 represents just one component of Nvidia’s broader Cosmos platform, a suite of world foundation models (WFMs) designed specifically for physical AI development. The platform includes Cosmos-Predict1 for general-purpose world generation and Cosmos-Reason1 for physical common sense reasoning.

“Nvidia Cosmos is a developer-first world foundation model platform designed to help Physical AI developers build their Physical AI systems better and faster,” the company states on its GitHub repository. The platform includes pre-trained models under the Nvidia Open Model License and training scripts under the Apache 2 License.

This positions Nvidia to capitalize on the growing market for AI tools that can accelerate autonomous system development, particularly as industries from manufacturing to transportation invest heavily in robotics and autonomous technology.

Real-time generation: How Nvidia’s hardware powers next-gen AI simulation

Nvidia also demonstrated Cosmos-Transfer1 running in real-time on its latest hardware. “We further demonstrate an inference scaling strategy to achieve real-time world generation with an Nvidia GB200 NVL72 rack,” the researchers note.

The team achieved approximately 40x speedup when scaling from one to 64 GPUs, enabling the generation of 5 seconds of high-quality video in just 4.2 seconds — effectively real-time throughput.

This performance at scale addresses another critical industry challenge: simulation speed. Fast, realistic simulation enables more rapid testing and iteration cycles, accelerating the development of autonomous systems.

Open-source Innovation: Democratizing Advanced AI for Developers Worldwide

Nvidia’s decision to publish both the Cosmos-Transfer1 model and its underlying code on GitHub removes barriers for developers worldwide. This public release gives smaller teams and independent researchers access to simulation technology that previously required substantial resources.

The move fits into Nvidia’s broader strategy of building robust developer communities around its hardware and software offerings. By putting these tools in more hands, the company expands its influence while potentially accelerating progress in physical AI development.

For robotics and autonomous vehicle engineers, these newly available tools could shorten development cycles through more efficient training environments. The practical impact may be felt first in testing phases, where developers can expose systems to a wider range of scenarios before real-world deployment.

While open source makes the technology available, putting it to effective use still requires expertise and computational resources — a reminder that in AI development, the code itself is just the beginning of the story.

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What is Nvidia Dynamo and why it matters to enterprises?

It uses disaggregated serving to separate the processing and generation phases of large language models (LLMs) on different GPUs, which allows each phase to be optimized independently for its specific needs and ensures maximum GPU resource utilization, the chipmaker explained.   The efficiency gain is made possible as Dynamo has

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TotalEnergies Eyes Brazil Green Hydrogen for European Refineries

TotalEnergies SE is considering importing green hydrogen from a multi-billion dollar project under development in northeastern Brazil to supply its European refineries.  The French energy company would be a key buyer from a plant planned by Brazilian renewables developer Casa dos Ventos, said people familiar with the matter who asked not to be identified because the deliberations are private. TotalEnergies, which has a 34 percent stake in the developer’s wind and solar generation unit, also is considering taking a direct shareholding in the project, the people said. TotalEnergies and Casa dos Ventos have yet to make a final decision on the deal, according to the people. Both companies declined to comment.  The project, which would be developed in stages at the port of Pecem, eventually may reach a capacity of 1.2 gigawatts of electrolysis, which could produce 160,000 tons of green hydrogen per year. Production of green ammonia, which is easier to ship than hydrogen, could reach 900,000 tons a year, according to Casa dos Ventos.  Green hydrogen has been touted as a key fuel for a carbon-free future as it is produced by splitting water into hydrogen and oxygen molecules using renewable energy. However, few large-scale projects to make it have progressed to the construction phase due to costs and technological complexities. TotalEnergies’ contract would be a new step in the oil major’s plan to reduce its carbon emissions by replacing the 500,000 tons of gray hydrogen – made out of fossil fuels – that is used in its refining processes in Europe with green hydrogen by 2030.  The switch to the clean, but far more expensive, gas is motivated by a planned European Union tax on fuel distributors who fail to meet greener standards, the company has said. So far, the company has signed contracts for more than 130,000 tons of

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NewMed Energy Farms into OMV Petrom Block Offshore Bulgaria

NewMed Energy LP has completed the acquisition of a 50 percent stake in the Han Asparuh block on Bulgaria’s side of the Black Sea from OMV Petrom SA. “With the closing of the transaction, NewMed Balkan now holds 50 percent of the interests in the Bulgaria License”, NewMed Energy, a gas and condensate exploration and production company owned by Israel’s Delek Group, confirmed in a regulatory filing. OMV Offshore Bulgaria GmbH, a subsidiary of Austrian-Romanian company OMV Petrom, holds the remaining 50 percent as operator. Cristian Hubati, OMV Petrom board member responsible for exploration and production, said in a separate statement by the company, “Access to local resources is essential for energy security, and through the upcoming exploration activities we are opening new perspectives in the Bulgarian Black Sea area”. The partners expect to start drilling a new exploration well this year, OMV Petrom said. The leasehold, also called Block 1-21, spans about 13,712 square kilometers (5,294.23 square miles) with water depths of just below 2,000 meters (6,561.68 feet) in the country’s exclusive economic zone, according to OMV Petrom. “This collaboration allows both parties to share the risks and costs associated with the project, thereby facilitating the advancement of exploration efforts”, OMV Petrom, an integrated energy company with investments from Austria’s state-backed OMV AG and the Romanian government, said November 28, 2024, announcing the farm-down agreement. NewMed Energy said separately at the time it had agreed to spend up to EUR 100 million ($108.71 million) on two wells in the block, if the farm-out is completed. NewMed Energy’s initial investment consists of EUR 50 million for the next exploration well to be drilled in the block’s Vinekh prospect. This well would be followed by another drilling on either an exploration well in another prospect or an appraisal well in Vinekh

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New Ofgem rules aim to unlock £4bn of grid investment

Ofgem will grant the UK’s electricity transmission owners with early access to almost £4 billion of grid investment for equipment and services. The regulator’s new Advanced Procurement Mechanism (APM) aims to unblock supply chains and help renewable energy projects connect quicker to the grid. Through the new process, Ofgem hopes that green-lit projects will be ready to break ground as soon as planning approval is granted, reducing delays, control costs and helping attract international investment. Ofgem director general for infrastructure Akshay Kaul said: “Building a modern, clean and secure energy system is the key to ending our reliance on international gas markets responsible for volatile prices, so we must do everything we can to clear the way for trailblazing projects to move forward. “The APM is an innovative model that could be extended in the future to develop other areas of the energy sector, and possibly mirrored by other regulatory bodies supporting the delivery of national infrastructure.” According to Ofgem, the APM framework could help UK transmission owners reduce risk of costly supply chain delays and lowering or control build costs by purchasing materials in advance. In addition, it could help support growth in domestic manufacturing and attract international investment to British projects while accelerating project delivery. To minimise the risk of stranded procurement – pre-ordered equipment for projects that do not progress – Ofgem will ensure that only equipment that is transferable between many different projects is eligible for APM funding. More bespoke procurement will be considered on a case-by-case basis to assess the benefit and risk. Reacting to Ofgem’s announcement, SP Energy Networks CEO Nicola Connelly said: “The £75bn proposed investment in the transmission system is the foundation of the government’s growth ambitions, unlocking grid capacity for homes and businesses, moving homegrown clean energy around the country and

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GB Energy shouldn’t be sold as ‘saviour’ to oil and gas industry, says union chief

GB Energy won’t create enough new green jobs at scale to replace those potentially being lost in oil and gas, a union boss has claimed. Derek Thomson, Unite Scotland regional secretary, said the jury is out when it comes to the impact of the new publicly-owned energy firm being set up in Aberdeen. It comes just days after the board of GB Energy met for the first time ever in the north-east to discuss their plans to scale up the firm’s presence in the city. Thomson told MPs on the Scottish Affairs Committee that despite “great fanfare” it should not be seen as “some sort of saviour”. “For Unite, it’s a wait and see with GB Energy”, the union chief said. “We’ll engage with it where we can. I don’t think it should be sold as some kind of saviour to the oil and gas industry. “I don’t think it’s going to come in and create the amount of jobs in Aberdeen to replace the amount of jobs that are potentially going to go in oil and gas.” ‘It’s not looking promising’ His concerns were echoed by Robert Deavy, Scotland senior organiser from GMB union, who raised questions over the funding on the table. The union organiser said that while £8.3 billion was committed to GB Energy over the lifetime of the current parliament, only £100m was allocated in the latest budget. He said: “Of course we’ll support it, we want it to work, we want it to create green jobs. “But at this moment in time, it’s not looking promising.” Meanwhile, union leaders shared concerns over the “catastrophic effect” moving away from oil and gas too quickly could have on the local economy and jobs. They want to see a proper plan put in place to replace North Sea

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GB Energy advertises for Aberdeen-based chief executive with £350,000 salary

GB Energy has started a new search for a chief executive to be based at its Aberdeen headquarters. The job advertisement comes after the new publicly founded company faced recruitment struggles earlier this year. Dan McGrail was appointed interim chief executive last month. But the permanent chief executive role has now appeared on Linkedin with a base salary of £350,000 a year. ‘Truly extraordinary opportunity’ GB Energy is working alongside London headquartered global executive search and organisational firm Odgers Berndston to find the right candidate. The job description states the appointment will start in Autumn this year, taking over from Mr McGrail who was hired on an initial six-month contract. Aberdeen will be the location with “expectation for the Chief Executive is to attend the office or non-home-based location for at least 60% of the time.” The job advert states: “We are delighted to be working with Great British Energy to appoint their permanent Chief Executive. “This is a truly extraordinary opportunity to lead a pioneering new publicly owned company that will sit at the very heart of the UK’s clean energy transformation. “As CEO, you will be responsible for shaping and delivering the strategy that will turn this vision into reality.” Closing date for applications is April 20 with interviews to be held in early June. GB Energy jobs boost GB Energy chairman Juergen Maier has previously said the publicly owned company could create 200-300 jobs in Aberdeen after the Granite City was chosen as its headquarters location. It started advertising roles based in the city, including Energy Project Development Lead and a Engagement Lead, in August last year. GB Energy will be focused on clean energy will invest more than £8 billion over the next five years. Speaking ahead of attending GB Energy’s first ever board meeting in

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Baker Hughes, Petrobras Tie Up to Solve Flexible Pipes Corrosion Cracking

Energy technology major Baker Hughes Co. has launched a joint technology program with Petróleo Brasileiro S.A. (Petrobras) to solve stress corrosion cracking due to carbon dioxide (SCC-CO2) in flexible pipe systems. The pre-commercial agreement includes development and testing, along with an option to purchase the next-generation flexible pipes that will offer a prolonged service life of 30 years in high-CO2 conditions, Baker Hughes said in a media release. The partnership will mainly be executed at Baker Hughes’ Energy Technology Innovation Center in Rio de Janeiro and the adjacent manufacturing facility for flexible pipe systems. “Baker Hughes has led the way in addressing SCC-CO2, and we will bring that expertise and experience to bear in developing the definitive solution to this critical industry challenge”, Amerino Gatti, executive vice president for Oilfield Services and Equipment at Baker Hughes, said. “By deploying flexible pipe systems that last for decades, Petrobras can more efficiently unlock the vital natural resources that power the region, while also safely returning CO2 deep underground”. SCC-CO2 was discovered in 2016 and can impact flexible pipes in pre-salt fields, which contain high levels of naturally occurring CO2. When water enters a pipe’s annulus area, it can lead to corrosion of the steel reinforcement layers, compromising structural integrity and shortening the system’s lifespan, Baker Hughes said. This challenge is especially pronounced in Brazil’s pre-salt fields, where Petrobras is reinjecting CO2 from its production processes into wells to decrease flaring and improve oil recovery, it said. Until now, operators in high-CO2 environments have relied on solutions that mitigate the impact of SCC-CO2 while limiting the service life of risers and flowlines, Baker Hughes said. Its flexible pipe systems and advanced monitoring technologies have proven effective at minimizing this impact, and the company is a major supplier of flexible pipe systems to Petrobras,

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Nvidia, xAI and two energy giants join genAI infrastructure initiative

The new AIP members will “further strengthen the partnership’s technology leadership as the platform seeks to invest in new and expanded AI infrastructure. Nvidia will also continue in its role as a technical advisor to AIP, leveraging its expertise in accelerated computing and AI factories to inform the deployment of next-generation AI data center infrastructure,” the group’s statement said. “Additionally, GE Vernova and NextEra Energy have agreed to collaborate with AIP to accelerate the scaling of critical and diverse energy solutions for AI data centers. GE Vernova will also work with AIP and its partners on supply chain planning and in delivering innovative and high efficiency energy solutions.” The group claimed, without offering any specifics, that it “has attracted significant capital and partner interest since its inception in September 2024, highlighting the growing demand for AI-ready data centers and power solutions.” The statement said the group will try to raise “$30 billion in capital from investors, asset owners, and corporations, which in turn will mobilize up to $100 billion in total investment potential when including debt financing.” Forrester’s Nguyen also noted that the influence of two of the new members — xAI, owned by Elon Musk, along with Nvidia — could easily help with fundraising. Musk “with his connections, he does not make small quiet moves,” Nguyen said. “As for Nvidia, they are the face of AI. Everything they do attracts attention.” Info-Tech’s Bickley said that the astronomical dollars involved in genAI investments is mind-boggling. And yet even more investment is needed — a lot more.

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IBM broadens access to Nvidia technology for enterprise AI

The IBM Storage Scale platform will support CAS and now will respond to queries using the extracted and augmented data, speeding up the communications between GPUs and storage using Nvidia BlueField-3 DPUs and Spectrum-X networking, IBM stated. The multimodal document data extraction workflow will also support Nvidia NeMo Retriever microservices. CAS will be embedded in the next update of IBM Fusion, which is planned for the second quarter of this year. Fusion simplifies the deployment and management of AI applications and works with Storage Scale, which will handle high-performance storage support for AI workloads, according to IBM. IBM Cloud instances with Nvidia GPUs In addition to the software news, IBM said its cloud customers can now use Nvidia H200 instances in the IBM Cloud environment. With increased memory bandwidth (1.4x higher than its predecessor) and capacity, the H200 Tensor Core can handle larger datasets, accelerating the training of large AI models and executing complex simulations, with high energy efficiency and low total cost of ownership, according to IBM. In addition, customers can use the power of the H200 to process large volumes of data in real time, enabling more accurate predictive analytics and data-driven decision-making, IBM stated. IBM Consulting capabilities with Nvidia Lastly, IBM Consulting is adding Nvidia Blueprint to its recently introduced AI Integration Service, which offers customers support for developing, building and running AI environments. Nvidia Blueprints offer a suite pre-validated, optimized, and documented reference architectures designed to simplify and accelerate the deployment of complex AI and data center infrastructure, according to Nvidia.  The IBM AI Integration service already supports a number of third-party systems, including Oracle, Salesforce, SAP and ServiceNow environments.

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Nvidia’s silicon photonics switches bring better power efficiency to AI data centers

Nvidia typically uses partnerships where appropriate, and the new switch design was done in collaboration with multiple vendors across different aspects, including creating the lasers, packaging, and other elements as part of the silicon photonics. Hundreds of patents were also included. Nvidia will licensing the innovations created to its partners and customers with the goal of scaling this model. Nvidia’s partner ecosystem includes TSMC, which provides advanced chip fabrication and 3D chip stacking to integrate silicon photonics into Nvidia’s hardware. Coherent, Eoptolink, Fabrinet, and Innolight are involved in the development, manufacturing, and supply of the transceivers. Additional partners include Browave, Coherent, Corning Incorporated, Fabrinet, Foxconn, Lumentum, SENKO, SPIL, Sumitomo Electric Industries, and TFC Communication. AI has transformed the way data centers are being designed. During his keynote at GTC, CEO Jensen Huang talked about the data center being the “new unit of compute,” which refers to the entire data center having to act like one massive server. That has driven compute to be primarily CPU based to being GPU centric. Now the network needs to evolve to ensure data is being fed to the GPUs at a speed they can process the data. The new co-packaged switches remove external parts, which have historically added a small amount of overhead to networking. Pre-AI this was negligible, but with AI, any slowness in the network leads to dollars being wasted.

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Critical vulnerability in AMI MegaRAC BMC allows server takeover

“In disruptive or destructive attacks, attackers can leverage the often heterogeneous environments in data centers to potentially send malicious commands to every other BMC on the same management segment, forcing all devices to continually reboot in a way that victim operators cannot stop,” the Eclypsium researchers said. “In extreme scenarios, the net impact could be indefinite, unrecoverable downtime until and unless devices are re-provisioned.” BMC vulnerabilities and misconfigurations, including hardcoded credentials, have been of interest for attackers for over a decade. In 2022, security researchers found a malicious implant dubbed iLOBleed that was likely developed by an APT group and was being deployed through vulnerabilities in HPE iLO (HPE’s Integrated Lights-Out) BMC. In 2018, a ransomware group called JungleSec used default credentials for IPMI interfaces to compromise Linux servers. And back in 2016, Intel’s Active Management Technology (AMT) Serial-over-LAN (SOL) feature which is part of Intel’s Management Engine (Intel ME), was exploited by an APT group as a covert communication channel to transfer files. OEM, server manufacturers in control of patching AMI released an advisory and patches to its OEM partners, but affected users must wait for their server manufacturers to integrate them and release firmware updates. In addition to this vulnerability, AMI also patched a flaw tracked as CVE-2024-54084 that may lead to arbitrary code execution in its AptioV UEFI implementation. HPE and Lenovo have already released updates for their products that integrate AMI’s patch for CVE-2024-54085.

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HPE, Nvidia broaden AI infrastructure lineup

“Accelerated by 2 NVIDIA H100 NVL, [HPE Private Cloud AI Developer System] includes an integrated control node, end-to-end AI software that includes NVIDIA AI Enterprise and HPE AI Essentials, and 32TB of integrated storage providing everything a developer needs to prove and scale AI workloads,” Corrado wrote. In addition, HPE Private Cloud AI includes support for new Nvidia GPUs and blueprints that deliver proven and functioning AI workloads like data extraction with a single click, Corrado wrote. HPE data fabric software HPE has also extended support for its Data Fabric technology across the Private Cloud offering. The Data Fabric aims to create a unified and consistent data layer that spans across diverse locations, including on-premises data centers, public clouds, and edge environments to provide a single, logical view of data, regardless of where it resides, HPE said. “The new release of Data Fabric Software Fabric is the data backbone of the HPE Private Cloud AI data Lakehouse and provides an iceberg interface for PC-AI users to data hosed throughout their enterprise. This unified data layer allows data scientists to connect to external stores and query that data as iceberg compliant data without moving the data,” wrote HPE’s Ashwin Shetty in a blog post. “Apache Iceberg is the emerging format for AI and analytical workloads. With this new release Data Fabric becomes an Iceberg end point for AI engineering. This makes it simple for AI engineering data scientists to easily point to the data lakehouse data source and run a query directly against it. Data Fabric takes care of metadata management, secure access, joining files or objects across any source on-premises or in the cloud in the global namespace.” In addition, HPE Private Cloud AI now supports pre-validated Nvidia blueprints to help customers implement support for AI workloads.  AI infrastructure optimization  Aiming to help customers

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Schneider Electric Adds Data Center and Microgrid Testing Labs to Andover, MA Global R&D Center

Schneider Electric, a global leader in energy management and automation, has established its Global Innovation Hubs as key centers for technological advancement, collaboration, and sustainable development. These hub facilities serve as ecosystems where cutting-edge solutions in energy efficiency, industrial automation, and digital transformation are designed, tested, and deployed to address the world’s most pressing energy and sustainability challenges. Energy Management and Industrial Automation Focus Strategically located around the world, Schneider Electric’s Global Innovation Hubs are positioned to drive regional and global innovation in energy management and industrial automation. The hubs focus on developing smart, connected, and sustainable solutions across various sectors, including data centers, smart buildings, industrial automation, and renewable energy. Key aspects of the Schneider Global Innovation Hubs include: Collaboration and Co-Innovation: Partnering with startups, industry leaders, and research institutions to accelerate innovation. Fostering an open ecosystem where ideas can be rapidly developed and tested. Digital Transformation and Automation: Leveraging IoT, AI, and cloud technologies to enhance energy efficiency. Implementing digital twin technology for real-time monitoring and predictive maintenance. Sustainability and Energy Efficiency: Developing solutions that contribute to decarbonization and net-zero emissions. Creating energy-efficient systems for buildings, industries, and critical infrastructure. Customer-focused Innovation: Offering live demonstrations, simulation environments, and test labs for customers. Customizing solutions to meet specific industry challenges and regulatory requirements. Schneider’s Andover R&D Lab Highlights While there are 11 hubs worldwide to give the global customer base more convenient locations where they can evaluate Schneider product, the new lab facilities have also been added to one of the company’s five global R&D locations. The selected location is co-located with Schneider’s US research labs in Andover, Massachusetts. With the addition of these two new labs there are now 41 labs located in Andover. Over the last year, Schneider Electric has invested approximately $2.4 billion in R&D. The

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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