Tech AI robots help nurses beat burnout and transform hospital care Hospitals using AI-powered robots to support nurses, redefine patient care Published June 4, 2025 6:00am EDT close AI robots help nurses beat burnout and transform hospital care Artificial intelligence and robotics may help with nursing shortage. NEWYou can now listen to Fox News articles! The global healthcare system is expected to face a shortage of 4.5 million nurses by 2030, with burnout identified as a leading cause for this deficit. In response, Taiwan's hospitals are taking decisive action by integrating artificial intelligence and robotics to support their staff and maintain high standards of patient care. AI-powered Nurabot (Nvidia)Nurabot: The AI nursing robot changing patient careNurabot, a collaborative nursing robot developed by Foxconn and Kawasaki Heavy Industries with Nvidia's AI technology, is designed to take on some of the most physically demanding and repetitive tasks in clinical care. These include delivering medications, transporting samples, patrolling wards and guiding visitors through hospital corridors. By handling these responsibilities, Nurabot allows nurses to focus on more meaningful aspects of patient care and helps reduce the physical fatigue that often leads to burnout. AI-powered Nurabot (Nvidia)Using AI to build the hospitals of the futureFoxconn's approach to smart hospitals goes beyond deploying robots. The company has developed a suite of digital tools using Nvidia platforms, including AI models that monitor patient vitals and digital twins that simulate hospital environments for planning and training purposes.The process starts in the data center, where large AI models are trained on Nvidia supercomputers. Hospitals then use digital twins to test and train robots in virtual settings before deploying them in real-world scenarios, ensuring that these systems are both safe and effective.ARTIFICIAL INTELLIGENCE TRANSFORMS PATIENT CARE AND REDUCES BURNOUT, PHYSICIAN SAYS AI-powered Nurabot (Nvidia)AI robots in real hospitals: Results from Taiwan's Healthcare SystemTaichung Veterans General Hospital (TCVGH), along with other top hospitals in Taiwan, is at the forefront of this digital transformation. TCVGH has built digital twins of its wards and nursing stations, providing a virtual training ground for Nurabot before it is introduced to real hospital floors. According to Shu-Fang Liu, deputy director of the nursing department at TCVGH, robots like Nurabot are augmenting the capabilities of healthcare staff, enabling them to deliver more focused and meaningful care to patients. AI-powered Nurabot (Nvidia)Ways Nurabot reduces nurse burnout and boosts efficiencyNurabot is already making a difference in daily hospital operations. The robot handles medicine deliveries, ward patrols and visitor guidance, which Foxconn estimates can reduce nurse workloads by up to 30%. In one ward, Nurabot delivers wound care kits and educational materials directly to patient bedsides, saving nurses multiple trips to supply rooms and allowing them to dedicate more time to their patients. The robot is also especially helpful during visiting hours and night shifts, when staffing levels are typically lower.Nurses hope future versions of Nurabot will be able to converse with patients in multiple languages, recognize faces for personalized interactions and even assist with lifting patients when needed. For example, a lung patient who needs two nurses to sit up for breathing exercises might only require one nurse with Nurabot's help, freeing the other to care for other patients. AI-powered Nurabot (Nvidia)Kurt's key takeawaysWhen it comes to addressing the nursing shortage, Taiwan is demonstrating that AI and robotics can make a significant difference in hospitals. Instead of spending their shifts running errands or handling repetitive tasks, nurses now have robots like Nurabot to lend a hand. This means nurses can focus their energy on what matters most – caring for patients – while robots handle tasks such as delivering medication or guiding visitors around the hospital.It's a team effort between people and technology, and it's already helping healthcare staff provide better care for everyone.CLICK HERE TO GET THE FOX NEWS APPHow would you feel if a robot, not a human, delivered your medication during a hospital stay? Let us know by writing us at Cyberguy.com/Contact.For more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/Newsletter.Ask Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved. Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. What’s next for AI and math The modern world is built on mathematics. Math lets us model complex systems such as the way air flows around an aircraft, the way financial markets fluctuate, and the way blood flows through the heart. Mathematicians have used computers for decades, but the new vision is that AI might help them crack problems that were previously uncrackable.   However, there’s a huge difference between AI that can solve the kinds of problems set in high school—math that the latest generation of models has already mastered—and AI that could (in theory) solve the kinds of problems that professional mathematicians spend careers chipping away at. Here are three ways to understand that gulf.  —Will Douglas HeavenThis story is from our What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Inside the effort to tally AI’s energy appetite —James O’Donnell After working on it for months, my colleague Casey Crownhart and I finally saw our story on AI’s energy and emissions burden go live last week.  The initial goal sounded simple: Calculate how much energy is used when we interact with a chatbot, then tally that up to understand why leaders in tech and politics are so keen to harness unprecedented levels of electricity to power AI and reshape our energy grids in the process.It was, of course, not so simple. After speaking with dozens of researchers, we realized that the common understanding of AI’s energy appetite is full of holes. I encourage you to read the full story, which has some incredible graphics to help you understand this topic. But here are three takeaways I have after the project. This story originally appeared in The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here, and check out the rest of our Power Hungry package about AI here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk has turned on Trump He called Trump’s domestic policy agenda a “disgusting abomination.” (NYT $)+ House Speaker Mike Johnson has, naturally, hit back. (Insider $) 2 NASA is in crisisIts budget has been cut by a quarter, and now its new leader has had his nomination revoked. (New Scientist $)+ What’s next for NASA’s giant moon rocket? (MIT Technology Review)3 Here’s how Big Tech plans to wield AITo build ‘everything apps’ that keep you inside their ecosystem, forever. (The Atlantic $)+ The trouble is, the experience isn’t always slick enough, as Google has discovered with its ‘Ask Photos’ feature. (The Verge $)+ How to fight your instinct to blindly trust AI. (WP $)4 Meta has signed a 20-year deal to buy nuclear power It’s the latest in a race to try to keep up with AI’s surging energy demands. (ABC)+ Can nuclear power really fuel the rise of AI? (MIT Technology Review) 5 Extreme heat takes a huge toll on people’s mental healthIt’s yet another issue we’re failing to prepare for, as summers get hotter and hotter. (Scientific American $)+ The quest to protect farmworkers from extreme heat. (MIT Technology Review) 6 China’s robotaxi companies are planning to expand in the Middle East And they’re getting a warmer welcome than in the US or Europe. (WSJ $)+ China’s EV giants are also betting big on humanoid robots. (MIT Technology Review)7 AI will supercharge hackersThe full impact of new AI techniques is yet to be felt, but experts say it’s only a matter of time. (Wired $)+ Five ways criminals are using AI. (MIT Technology Review)8 It’s an exciting time to be working on Alzheimer’s treatments 12 of them are moving to the final phase of clinical trials this year. (The Economist $)+ The innovation that gets an Alzheimer’s drug through the blood-brain barrier. (MIT Technology Review)9 Workers are being subjected to more and more surveillanceNot just in the gig economy either—’bossware’ is increasingly appearing in offices too. (Rest of World) 10 Noughties nostalgia is rife on TikTokIt was a pretty fun decade, to be fair. (The Guardian) Quote of the day  “This is scientific heaven. Or it used to be.” —Tom Rapoport, a 77-year-old Harvard Medical School professor from Germany, expresses his sadness about Trump’s cuts to US science funding to the New York Times.  One more thing OLCF What’s next for the world’s fastest supercomputers When the Frontier supercomputer came online in 2022, it marked the dawn of so-called exascale computing, with machines that can execute an exaflop—or a quintillion (1018) floating point operations a second.Since then, scientists have geared up to make more of these blazingly fast computers: several exascale machines are due to come online in the US and Europe.But speed itself isn’t the endgame. Researchers hope to pursue previously unanswerable questions about nature—and to design new technologies in areas from transportation to medicine. Read the full story. —Sophia Chen We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + If tracking tube trains in London is your thing, you’ll love this live map.+ Take a truly bonkers trip down memory lane, courtesy of these FBI artifacts.+ Netflix’s Frankenstein looks pretty intense.+ Why landlines are so darn spooky

By CHRIS McGOWAN This image of Jupiter from NASA’s James Webb Space Telescope’s NIRCam (Near-Infrared Camera) shows stunning details of the majestic planet in infrared light. (Image courtesy of NASA, ESA and CSA) Special effects have been used for decades to depict space exploration, from visits to planets and moons to zero gravity and spaceships – one need only think of the landmark 2001: A Space Odyssey (1968). Since that era, visual effects have increasingly grown in realism and importance. VFX have been used for entertainment and for scientific purposes, outreach to the public and astronaut training in virtual reality. Compelling images and videos can bring data to life. NASA’s Scientific Visualization Studio (SVS) produces visualizations, animations and images to help scientists tell stories of their research and make science more approachable and engaging. A.J. Christensen is a senior visualization designer for the NASA Scientific Visualization Studio (SVS) at the Goddard Space Flight Center in Greenbelt, Maryland. There, he develops data visualization techniques and designs data-driven imagery for scientific analysis and public outreach using Hollywood visual effects tools, according to NASA. SVS visualizations feature datasets from Earth-and space-based instrumentation, scientific supercomputer models and physical statistical distributions that have been analyzed and processed by computational scientists. Christensen’s specialties include working with 3D volumetric data, using the procedural cinematic software Houdini and science topics in Heliophysics, Geophysics and Astrophysics. He previously worked at the National Center for Supercomputing Applications’ Advanced Visualization Lab where he worked on more than a dozen science documentary full-dome films as well as the IMAX films Hubble 3D and A Beautiful Planet – and he worked at DNEG on the movie Interstellar, which won the 2015 Best Visual Effects Academy Award. This global map of CO2 was created by NASA’s Scientific Visualization Studio using a model called GEOS, short for the Goddard Earth Observing System. GEOS is a high-resolution weather reanalysis model, powered by supercomputers, that is used to represent what was happening in the atmosphere. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) “The NASA Scientific Visualization Studio operates like a small VFX studio that creates animations of scientific data that has been collected or analyzed at NASA. We are one of several groups at NASA that create imagery for public consumption, but we are also a part of the scientific research process, helping scientists understand and share their data through pictures and video.” —A.J. Christensen, Senior Visualization Designer, NASA Scientific Visualization Studio (SVS) About his work at NASA SVS, Christensen comments, “The NASA Scientific Visualization Studio operates like a small VFX studio that creates animations of scientific data that has been collected or analyzed at NASA. We are one of several groups at NASA that create imagery for public consumption, but we are also a part of the scientific research process, helping scientists understand and share their data through pictures and video. This past year we were part of NASA’s total eclipse outreach efforts, we participated in all the major earth science and astronomy conferences, we launched a public exhibition at the Smithsonian Museum of Natural History called the Earth Information Center, and we posted hundreds of new visualizations to our publicly accessible website: svs.gsfc.nasa.gov.” This is the ‘beauty shot version’ of Perpetual Ocean 2: Western Boundary Currents. The visualization starts with a rotating globe showing ocean currents. The colors used to color the flow in this version were chosen to provide a pleasing look. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) The Gulf Stream and connected currents. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Venus, our nearby “sister” planet, beckons today as a compelling target for exploration that may connect the objects in our own solar system to those discovered around nearby stars. (Image courtesy of NASA’s Goddard Space Flight Center) WORKING WITH DATA While Christensen is interpreting the data from active spacecraft and making it usable in different forms, such as for science and outreach, he notes, “It’s not just spacecraft that collect data. NASA maintains or monitors instruments on Earth too – on land, in the oceans and in the air. And to be precise, there are robots wandering around Mars that are collecting data, too.” He continues, “Sometimes the data comes to our team as raw telescope imagery, sometimes we get it as a data product that a scientist has already analyzed and extracted meaning from, and sometimes various sensor data is used to drive computational models and we work with the models’ resulting output.” Jupiter’s moon Europa may have life in a vast ocean beneath its icy surface. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) HOUDINI AND OTHER TOOLS “Data visualization means a lot of different things to different people, but many people on our team interpret it as a form of filmmaking,” Christensen says. “We are very inspired by the approach to visual storytelling that Hollywood uses, and we use tools that are standard for Hollywood VFX. Many professionals in our area – the visualization of 3D scientific data – were previously using other animation tools but have discovered that Houdini is the most capable of understanding and manipulating unusual data, so there has been major movement toward Houdini over the past decade.” Satellite imagery from NASA’s Solar Dynamics Observatory (SDO) shows the Sun in ultraviolet light colorized in light brown. Seen in ultraviolet light, the dark patches on the Sun are known as coronal holes and are regions where fast solar wind gushes out into space. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Christensen explains, “We have always worked with scientific software as well – sometimes there’s only one software tool in existence to interpret a particular kind of scientific data. More often than not, scientific software does not have a GUI, so we’ve had to become proficient at learning new coding environments very quickly. IDL and Python are the generic data manipulation environments we use when something is too complicated or oversized for Houdini, but there are lots of alternatives out there. Typically, we use these tools to get the data into a format that Houdini can interpret, and then we use Houdini to do our shading, lighting and camera design, and seamlessly blend different datasets together.” While cruising around Saturn in early October 2004, Cassini captured a series of images that have been composed into this large global natural color view of Saturn and its rings. This grand mosaic consists of 126 images acquired in a tile-like fashion, covering one end of Saturn’s rings to the other and the entire planet in between. (Image courtesy of ASA/JPL/Space Science Institute) The black hole Gargantua and the surrounding accretion disc from the 2014 movie Interstellar. (Image courtesy of DNEG and Paramount Pictures) Another visualization of the black hole Gargantua. (Image courtesy of DNEG and Paramount Pictures) INTERSTELLAR & GARGANTUA Christensen recalls working for DNEG on Interstellar (2014). “When I first started at DNEG, they asked me to work on the giant waves on Miller’s ocean planet [in the film]. About a week in, my manager took me into the hall and said, ‘I was looking at your reel and saw all this astronomy stuff. We’re working on another sequence with an accretion disk around a black hole that I’m wondering if we should put you on.’ And I said, ‘Oh yeah, I’ve done lots of accretion disks.’ So, for the rest of my time on the show, I was working on the black hole team.” He adds, “There are a lot of people in my community that would be hesitant to label any big-budget movie sequence as a scientific visualization. The typical assumption is that for a Hollywood movie, no one cares about accuracy as long as it looks good. Guardians of the Galaxy makes it seem like space is positively littered with nebulae, and Star Wars makes it seem like asteroids travel in herds. But the black hole Gargantua in Interstellar is a good case for being called a visualization. The imagery you see in the movie is the direct result of a collaboration with an expert scientist, Dr. Kip Thorne, working with the DNEG research team using the actual Einstein equations that describe the gravity around a black hole.” Thorne is a Nobel Prize-winning theoretical physicist who taught at Caltech for many years. He has reached wide audiences with his books and presentations on black holes, time travel and wormholes on PBS and BBC shows. Christensen comments, “You can make the argument that some of the complexity around what a black hole actually looks like was discarded for the film, and they admit as much in the research paper that was published after the movie came out. But our team at NASA does that same thing. There is no such thing as an objectively ‘true’ scientific image – you always have to make aesthetic decisions around whether the image tells the science story, and often it makes more sense to omit information to clarify what’s important. Ultimately, Gargantua taught a whole lot of people something new about science, and that’s what a good scientific visualization aims to do.” The SVS produces an annual visualization of the Moon’s phase and libration comprising 8,760 hourly renderings of its precise size, orientation and illumination. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) FURTHER CHALLENGES The sheer size of the data often encountered by Christensen and his peers is a challenge. “I’m currently working with a dataset that is 400GB per timestep. It’s so big that I don’t even want to move it from one file server to another. So, then I have to make decisions about which data attributes to keep and which to discard, whether there’s a region of the data that I can cull or downsample, and I have to experiment with data compression schemes that might require me to entirely re-design the pipeline I’m using for Houdini. Of course, if I get rid of too much information, it becomes very resource-intensive to recompute everything, but if I don’t get rid of enough, then my design process becomes agonizingly slow.” SVS also works closely with its NASA partner groups Conceptual Image Lab (CIL) and Goddard Media Studios (GMS) to publish a diverse array of content. Conceptual Image Lab focuses more on the artistic side of things – producing high-fidelity renders using film animation and visual design techniques, according to NASA. Where the SVS primarily focuses on making data-based visualizations, CIL puts more emphasis on conceptual visualizations – producing animations featuring NASA spacecraft, planetary observations and simulations, according to NASA. Goddard Media Studios, on the other hand, is more focused towards public outreach – producing interviews, TV programs and documentaries. GMS continues to be the main producers behind NASA TV, and as such, much of their content is aimed towards the general public. An impact crater on the moon. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Image of Mars showing a partly shadowed Olympus Mons toward the upper left of the image. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Mars. Hellas Basin can be seen in the lower right portion of the image. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Mars slightly tilted to show the Martian North Pole. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Christensen notes, “One of the more unique challenges in this field is one of bringing people from very different backgrounds to agree on a common outcome. I work on teams with scientists, communicators and technologists, and we all have different communities we’re trying to satisfy. For instance, communicators are generally trying to simplify animations so their learning goal is clear, but scientists will insist that we add text and annotations on top of the video to eliminate ambiguity and avoid misinterpretations. Often, the technologist will have to say we can’t zoom in or look at the data in a certain way because it will show the data boundaries or data resolution limits. Every shot is a negotiation, but in trying to compromise, we often push the boundaries of what has been done before, which is exciting.”

NVIDIA is working with companies worldwide to build out AI factories — speeding the training and deployment of next-generation AI applications that use the latest advancements in training and inference. The NVIDIA Blackwell architecture is built to meet the heightened performance requirements of these new applications. In the latest round of MLPerf Training — the 12th since the benchmark’s introduction in 2018 — the NVIDIA AI platform delivered the highest performance at scale on every benchmark and powered every result submitted on the benchmark’s toughest large language model (LLM)-focused test: Llama 3.1 405B pretraining. The NVIDIA platform was the only one that submitted results on every MLPerf Training v5.0 benchmark — underscoring its exceptional performance and versatility across a wide array of AI workloads, spanning LLMs, recommendation systems, multimodal LLMs, object detection and graph neural networks. The at-scale submissions used two AI supercomputers powered by the NVIDIA Blackwell platform: Tyche, built using NVIDIA GB200 NVL72 rack-scale systems, and Nyx, based on NVIDIA DGX B200 systems. In addition, NVIDIA collaborated with CoreWeave and IBM to submit GB200 NVL72 results using a total of 2,496 Blackwell GPUs and 1,248 NVIDIA Grace CPUs. On the new Llama 3.1 405B pretraining benchmark, Blackwell delivered 2.2x greater performance compared with previous-generation architecture at the same scale. On the Llama 2 70B LoRA fine-tuning benchmark, NVIDIA DGX B200 systems, powered by eight Blackwell GPUs, delivered 2.5x more performance compared with a submission using the same number of GPUs in the prior round. These performance leaps highlight advancements in the Blackwell architecture, including high-density liquid-cooled racks, 13.4TB of coherent memory per rack, fifth-generation NVIDIA NVLink and NVIDIA NVLink Switch interconnect technologies for scale-up and NVIDIA Quantum-2 InfiniBand networking for scale-out. Plus, innovations in the NVIDIA NeMo Framework software stack raise the bar for next-generation multimodal LLM training, critical for bringing agentic AI applications to market. These agentic AI-powered applications will one day run in AI factories — the engines of the agentic AI economy. These new applications will produce tokens and valuable intelligence that can be applied to almost every industry and academic domain. The NVIDIA data center platform includes GPUs, CPUs, high-speed fabrics and networking, as well as a vast array of software like NVIDIA CUDA-X libraries, the NeMo Framework, NVIDIA TensorRT-LLM and NVIDIA Dynamo. This highly tuned ensemble of hardware and software technologies empowers organizations to train and deploy models more quickly, dramatically accelerating time to value. The NVIDIA partner ecosystem participated extensively in this MLPerf round. Beyond the submission with CoreWeave and IBM, other compelling submissions were from ASUS, Cisco, Dell Technologies, Giga Computing, Google Cloud, Hewlett Packard Enterprise, Lambda, Lenovo, Nebius, Oracle Cloud Infrastructure, Quanta Cloud Technology and Supermicro. Learn more about MLPerf benchmarks.

Key Takeaways Dell and Nvidia will together provide architecture for the next set of supercomputers, named Doudna, for the US Department of Energy. Dell will focus more on sustainable hardware and cooling systems, while Nvidia will provide its AI architecture, including the Vera Rubin AI chips. The US Department of Energy wants to focus more on the sustainable development of AI, hence the choice of environment-conscious companies. The US Department of Energy said that Dell’s next batch of supercomputers will be delivered with Nvidia’s ‘Vera Rubin’ AI chips, marking the beginning of a new era of AI dominance in research. The said systems are expected to be 10x faster than the current batch of supercomputers, which HP provided. The supercomputer will be named ‘Doudna,’ after Jennifer Doudna, a Nobel Prize winner who made key contributions in CRISPR gene-editing. Supercomputers have been instrumental in key scientific discoveries in the last few decades and also played a big role in the design and maintenance of the U.S. nuclear weapons arsenal. And now, with the introduction of artificial intelligence, we’re heading towards a new decade of faster and more efficient scientific research. It (supercomputers) is the foundation of scientific discovery for our country. It is also a foundation for economic and technological leadership. And with that, national security – Nvidia CEO, Jensen Huang Dell Going All in on AI This isn’t the first time Dell and Nvidia have come together to develop newer AI solutions. Back in March 2024, Dell announced the Dell AI Factory with NVIDIA, an end-to-end enterprise AI solution designed for businesses.  This joint venture used Dell’s infrastructure, such as servers, storage, and networking, combined with NVIDIA’s AI architecture and technologies such as GPUs, DPUs, and AI software. Image Credit – Dell For instance, the Dell PowerEdge server uses NVIDIA’s full AI stack to provide enterprises with solutions required for a wide range of AI applications, including speech recognition, cybersecurity, recommendation systems, and language-based services. The demand for Dell’s AI servers has also increased, reaching $12.1B in the first quarter of 2025, with a total backlog of $14.4B, which suggests a strong future demand and order book. The company has set a bold profit forecast between $28.5B and $29.5B, as against the analysts’ prediction of $ 25.05 B. With Doudna, Dell is well-positioned to lead the next generation of supercomputers in AI research, invention, and discoveries. Focus on Energy Efficiency Seagate has warned about the unprecedented increase in demand for AI data storage in the coming few years, which is a significant challenge to the sustainability of AI data centers. Global data volume is expected to increase threefold by 2028.  Image Credit – DIGITIMES Asian The data storage industry currently produces only 1-2 zettabytes (1 zettabyte equals 1 trillion gigabytes) of storage annually, which is much lower than what would be required in the next 4-5 years. At the same time, Goldman Sachs predicts that power requirements will also go up by 165% by 2030 due to increasing demand for AI data centers. This calls for a more sustainable approach for the supercomputing industry as well.  Dell will use its proprietary technologies, such as Direct Liquid Cooling, the PowerCool eRDHx, and Smart Flow design in the Doudna, ensuring energy efficiency. Direct Liquid Cooling (DLC) increased computing density by supporting more cores per rack, which reduces cooling costs by as much as 45%. Dell’s PowerCool eRDHx is a self-contained airflow design that can capture 100% of the heat generated by IT systems. This reduces the dependency on expensive chillers, as eRDHx can work in usual temperatures of 32 and 36 degrees Celsius, leading to 60% savings in cooling energy costs. Lastly, the Dell Smart Flow design improves airflow within IT components and reduces the fan power by 52%. This leads to better performance with fewer cooling requirements. Besides this, Dell plans to incorporate Leak Sense Technology. If a coolant leak occurs, the system’s leak sensor will log an alert in the iDRAC system so that swift action can be taken. As per a report titled ‘Energy and AI’ by the IEA, the data center electricity demand will increase to 945 terawatt-hours (TWh) by 2030. For comparison, this is more than the total electricity consumption of Japan today. The US alone will consume more electricity in 2030 for processing data than for manufacturing all energy-intensive goods combined, including aluminum, steel, cement, and chemicals. Therefore, the need to develop sustainable AI data centers and supercomputers cannot be highlighted enough. Dell’s technology-focused, sustainable approach can be a pivotal point in how efficiently we use AI in the next decade. The US Department of Energy’s choice of Dell also seems to be a conscious move to shift towards companies that give importance to sustainability and can vouch for the long-term viability of research-intensive AI setups. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

In April, Paul Graham, the founder of the tech startup accelerator Y Combinator, sent a tweet in response to former YC president and current OpenAI CEO Sam Altman. Altman had just bid a public goodbye to GPT-4 on X, and Graham had a follow-up question.  “If you had [GPT-4’s model weights] etched on a piece of metal in the most compressed form,” Graham wrote, referring to the values that determine the model’s behavior, “how big would the piece of metal have to be? This is a mostly serious question. These models are history, and by default digital data evaporates.”  There is no question that OpenAI pulled off something historic with its release of ChatGPT 3.5 in 2022. It set in motion an AI arms race that has already changed the world in a number of ways and seems poised to have an even greater long-term effect than the short-term disruptions to things like education and employment that we are already beginning to see. How that turns out for humanity is something we are still reckoning with and may be for quite some time. But a pair of recent books both attempt to get their arms around it with accounts of what two leading technology journalists saw at the OpenAI revolution.  In Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, Karen Hao tells the story of the company’s rise to power and its far-reaching impact all over the world. Meanwhile, The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, by the Wall Street Journal’s Keach Hagey, homes in more on Altman’s personal life, from his childhood through the present day, in order to tell the story of OpenAI. Both paint complex pictures and show Altman in particular as a brilliantly effective yet deeply flawed creature of Silicon Valley—someone capable of always getting what he wants, but often by manipulating others.  Hao, who was formerly a reporter with MIT Technology Review, began reporting on OpenAI while at this publication and remains an occasional contributor. One chapter of her book grew directly out of that reporting. And in fact, as Hao says in the acknowledgments of Empire of AI, some of her reporting for MIT Technology Review, a series on AI colonialism, “laid the groundwork for the thesis and, ultimately, the title of this book.” So you can take this as a kind of disclaimer that we are predisposed to look favorably on Hao’s work.  With that said, Empire of AI is a powerful work, bristling not only with great reporting but also with big ideas. This comes across in service to two main themes.  The first is simple: It is the story of ambition overriding ethics. The history of OpenAI as Hao tells it (and as Hagey does too) is very much a tale of a company that was founded on the idealistic desire to create a safety-focused artificial general intelligence but instead became more interested in winning. This is a story we’ve seen many times before in Big Tech. See Theranos, which was going to make diagnostics easier, or Uber, which was founded to break the cartel of “Big Taxi.” But the closest analogue might be Google, which went from “Don’t be evil” to (at least in the eyes of the courts) illegal monopolist. For that matter, consider how Google went from holding off on releasing its language model as a consumer product out of an abundance of caution to rushing a chatbot out the door to catch up with and beat OpenAI. In Silicon Valley, no matter what one’s original intent, it always comes back to winning.   The second theme is more complex and forms the book’s thesis about what Hao calls AI colonialism. The idea is that the large AI companies act like traditional empires, siphoning wealth from the bottom rungs of society in the forms of labor, creative works, raw materials, and the like to fuel their ambition and enrich those at the top of the ladder. “I’ve found only one metaphor that encapsulates the nature of what these AI power players are: empires,” she writes. “During the long era of European colonialism, empires seized and extracted resources that were not their own and exploited the labor of the people they subjugated to mine, cultivate, and refine those resources for the empires’ enrichment.” She goes on to chronicle her own growing disillusionment with the industry. “With increasing clarity,” she writes, “I realized that the very revolution promising to bring a better future was instead, for people on the margins of society, reviving the darkest remnants of the past.”  To document this, Hao steps away from her desk and goes out into the world to see the effects of this empire as it sprawls across the planet. She travels to Colombia to meet with data labelers tasked with teaching AI what various images show, one of whom she describes sprinting back to her apartment for the chance to make a few dollars. She documents how workers in Kenya who performed data-labeling content moderation for OpenAI came away traumatized by seeing so much disturbing material. In Chile she documents how the industry extracts precious resources—water, power, copper, lithium—to build out data centers.  She lands on the ways people are pushing back against the empire of AI across the world. Hao draws lessons from New Zealand, where Maori people are attempting to save their language using a small language model of their own making. Trained on volunteers’ voice recordings and running on just two graphics processing units, or GPUs, rather than the thousands employed by the likes of OpenAI, it’s meant to benefit the community, not exploit it.  Hao writes that she is not against AI. Rather: “What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed will ever emerge from—a vision of the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project … [The New Zealand model] shows us another way. It imagines how AI could be exactly the opposite. Models can be small and task-specific, their training data contained and knowable, ridding the incentives for widespread exploitative and psychologically harmful labor practices and the all-consuming extractivism of producing and running massive supercomputers.”  Hagey’s book is more squarely focused on Altman’s ambition, which she traces back to his childhood. Yet interestingly, she also  zeroes in on the OpenAI CEO’s attempt to create an empire. Indeed, “Altman’s departure from YC had not slowed his civilization-building ambitions,” Hagey writes. She goes on to chronicle how Altman, who had previously mulled a run for governor of California, set up experiments with income distribution via Tools for Humanity, the parent company of Worldcoin. She quotes Altman saying of it, “I thought it would be interesting to see … just how far technology could accomplish some of the goals that used to be done by nation-states.”  Overall, The Optimist is the more straightforward business biography of the two. Hagey has packed it full with scoops and insights and behind-the-scenes intrigue. It is immensely readable as a result, especially in the second half, when OpenAI really takes over the story. Hagey also seems to have been given far more access to Altman and his inner circles, personal and professional, than Hao did, and that allows for a fuller telling of the CEO’s story in places. For example, both writers cover the tragic story of Altman’s sister Annie, her estrangement from the family, and her accusations in particular about suffering sexual abuse at the hands of Sam (something he and the rest of the Altman family vehemently deny). Hagey’s telling provides a more nuanced picture of the situation, with more insight into family dynamics.  Hagey concludes by describing Altman’s reckoning with his role in the long arc of human history and what it will mean to create a “superintelligence.” His place in that sweep is something that clearly has consumed the CEO’s thoughts. When Paul Graham asked about preserving GPT-4, for example, Altman had a response at the ready. He replied that the company had already considered this, and that the sheet of metal would need to be 100 meters square.

The NAACP is calling on local officials to halt operations at Colossus, the “supercomputer” facility operated by Elon Musk’s xAI in South Memphis. As reported in NBC News, leaders from the civil rights group sent a letter Thursday to the Shelby County Health Department and Memphis Light Gas and Water criticizing the organizations’ “lackadaisical approach to the operation of this dirty data center” and calling on them to “issue an emergency order for xAI to stop operations completely” — or if there’s no order, to at least cite and stop the company from allegedly violating clean air laws. The letter expressed particular concerns around the gas turbines that xAI runs to power Colossus. The company has applied for a permit to continue operating 15 gas turbines at the facility, although the NAACP said authorities have “allowed xAI to operate at least 35 gas turbines without any permitting” over the past year. City officials have previously said xAI did not need permits for the turbines’ first year of use. These turbines reportedly emit hazardous air pollutants, including formaldehyde, at levels exceeding EPA limits. The NAACP’s letter also pointed to the turbines’ nitrogen-oxide emissions. Noting that the Colossus facility is located near South Memphis’ Boxtown neighborhood, which the letter described as a “historically Black community,” the NAACP said the location perpetuates “the trend of industries adding pollution to communities who do not cause the problem.” “Instead of [the Shelby County Health Department] working to reduce health issues known in the area including that cancer risks are already four times the national average, it has allowed xAI to operate above the law,” the NAACP added. The NAACP’s letter is addressed to Shelby County Health Department Director Michelle Taylor, as well as Memphis Light Gas and Water’s commissioners; Taylor is leaving her role in Shelby County to become the commissioner of the Baltimore City Health Department. Techcrunch event Save now through June 4 for TechCrunch Sessions: AI Save $300 on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW TechCrunch has reached out to the NAACP and xAI for comment. A spokesperson for Memphis Light Gas and Water told NBC News that it had not yet received the NAACP letter.

A person using ChatGPT on their phone (Kurt "CyberGuy" Knutsson) Welcome to Fox News’ Artificial Intelligence newsletter with the latest AI technology advancements.IN TODAY'S NEWSLETTER:- Scammers can exploit your data from just one ChatGPT search- Business Insider embraces AI while laying off 21% of workforce- Nvidia, Dell partner with Trump admin to make next-gen supercomputerGUARD YOUR DATA: ChatGPT and other large language models (LLMs) have become amazing helpers for everyday tasks. Whether it's summarizing complex ideas, designing a birthday card or even planning your apartment's layout, you can get impressive results with just a simple prompt.NEWS BREAK: Business Insider announced Thursday that the company will be shrinking the size of its newsroom and making layoffs, impacting over a fifth of its staff. Business Insider CEO Barbara Peng said in an internal memo obtained by Fox News Digital that the company is "fully embracing AI," as 70% of the company’s staff currently uses Enterprise ChatGPT, with a goal of 100%.  Sen. Elizabeth Warren and progressives are taking issue with what they see as Nvidia's outsized influence in the AI chip market. (Justin Sullivan/Getty Images)HIGH TECH: Nvidia and Dell on Thursday announced a breakthrough supercomputer powered by artificial intelligence (AI) will launch next year to help drive research at the Department of Energy (DOE).SETTING THE PACE: Pace University uses AI and scannable QR codes to read graduates' names. Passengers arrive to Terminal B at Newark Liberty International Airport in Newark, New Jersey on May 7, 2025. (KENA BETANCUR/AFP via Getty Images)A-'EYE' IN THE SKY: Transportation Secretary Sean Duffy recently announced that artificial intelligence (AI) is being used to detect and address air traffic risks, following a slew of near-misses and fatal plane crashes across the country.'PROFOUND TRANSFORMATION': Nvidia, a leader in the artificial intelligence (AI) space, saw shares rise 3% in after-hours trading following the announcement. The earnings report showed that first-quarter net income was up 26% from a year ago at nearly $19 billion, with revenue rising to $44 billion, up 69% from last year. Apple logo (Kurt "CyberGuy" Knutsson)'AGRI-FICIAL' INTELLIGENCE: John Deere is turning to artificial intelligence to help farmers address labor shortages and enable them to handle other tasks associated with their business.APPLE’S AI RECKONING': OpenAI has just made a move that's turning heads across the tech world. The company is acquiring io, the AI device startup founded by Jony Ive, for nearly $6.5 billion. This isn't your typical business deal. It's a collaboration between Sam Altman, who leads OpenAI, and the designer responsible for some of Apple's most iconic products, including the iPhone and Apple Watch.STANDING TALL AGAIN: For Caroline Laubach, being a Wandercraft test pilot is about more than just trying out new technology. It's about reclaiming a sense of freedom and connection that many wheelchair users miss. Laubach, a spinal stroke survivor and full-time wheelchair user, has played a key role in demonstrating the personal AI-powered prototype exoskeleton's development, and her experience highlights just how life-changing this device can be. A man using an exoskeleton to walk. (Wandercraft)BOT BLUNDER: Google’s artificial intelligence chatbot is being slammed for "anti-American" claims about the supposed White supremacist origins of Memorial Day.FOLLOW FOX NEWS ON SOCIAL MEDIASIGN UP FOR OUR OTHER NEWSLETTERSDOWNLOAD OUR APPSWATCH FOX NEWS ONLINEFox News GoSTREAM FOX NATIONFox NationStay up to date on the latest AI technology advancements and learn about the challenges and opportunities AI presents now and for the future with Fox News here. This article was written by Fox News staff.

The new supercomputer shows the increasing desire of government labs to adopt more technologies from commercial artificial intelligence systems.

What just happened? The Department of Energy has announced plans for a new supercomputer designed to significantly accelerate research across a wide range of scientific fields. The initiative highlights the growing convergence between commercial AI development and the computational demands of cutting-edge scientific discovery. The advanced system, to be housed at Lawrence Berkeley National Laboratory and scheduled to become operational in 2026, will be named "Doudna" in honor of Nobel laureate Jennifer Doudna, whose groundbreaking work on CRISPR gene editing has revolutionized molecular biology. Dell Technologies has been selected to deliver the Doudna supercomputer, marking a significant shift in the landscape of government-funded high-performance computing. While companies like Hewlett Packard Enterprise have traditionally dominated this space, Dell's successful bid signals a new chapter. "A big win for Dell," said Addison Snell, CEO of Intersect360 Research, in an interview with The New York Times, noting the company's historically limited presence in this domain. Dell executives explained that the Doudna project enabled them to move beyond the longstanding practice of building custom systems for individual laboratories. Instead, they focused on developing a flexible platform capable of serving a broad array of users. "This market had shifted into some form of autopilot. What we did was disengage the autopilot," said Paul Perez, senior vice president and technology fellow at Dell. The Perlmutter supercomputer at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. A defining feature of Doudna will be its use of Nvidia's Vera Rubin platform, engineered to combine the strengths of traditional scientific simulations with the power of modern AI. Unlike previous Department of Energy supercomputers, which relied on processors from Intel or AMD, Doudna will incorporate a general-purpose Arm-based CPU from Nvidia, paired with the company's Rubin AI chips designed specifically for artificial intelligence and simulation workloads. // Related Stories The architecture aims to meet the needs of the laboratory's 11,000 users, who increasingly depend on both high-precision modeling and rapid AI-driven data analysis. Jensen Huang, founder and CEO of Nvidia, described the new system with enthusiasm. "Doudna is a time machine for science – compressing years of discovery into days," he said, adding that it will let "scientists delve deeper and think bigger to seek the fundamental truths of the universe." In terms of performance, Doudna is expected to be over 10 times faster than the lab's current flagship system, making it the Department of Energy's most powerful resource for training AI models and conducting advanced simulations. Jonathan Carter, associate lab director for computing sciences at Berkeley Lab, said the system's architecture was shaped by the evolving needs of researchers – many of whom are now using AI to augment simulations in areas like geothermal energy and quantum computing. Doudna's design reflects a broader shift in supercomputing. Traditional systems have prioritized 64-bit calculations for maximum numerical accuracy, but modern AI workloads often benefit from lower-precision operations (such as 16-bit or 8-bit) that enable faster processing speeds. Dion Harris, Nvidia's head of data center product marketing, noted that the flexibility to combine different levels of precision opens new frontiers for scientific research. The supercomputer will also be tightly integrated with the Energy Sciences Network, allowing researchers nationwide to stream data directly into Doudna for real-time analysis. Sudip Dosanjh, director of the National Energy Research Scientific Computing Center, described the new system as "designed to accelerate a broad set of scientific workflows."