Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
The Importance of Nearshore Call Centers in Supporting Robotics and Automation
Robotics and automation are transforming various sectors, including manufacturing, healthcare, and logistics. While these technologies enhance efficiency and reduce costs, they still require human intervention for troubleshooting. Nearshore call centers have emerged as essential support systems, providing real-time assistance for complex technical issues that automated systems cannot resolve. As businesses increasingly adopt automation, the need for effective customer support becomes critical. Nearshore call centers offer a cost-effective solution, delivering high-quality support that enhances customer experience. These centers facilitate smoother communication and faster response times due to their proximity and cultural familiarity, ensuring that customers receive the personalized assistance they need when unexpected issues arise. Looking ahead, companies must balance operational efficiency with customer satisfaction. The role of nearshore call centers will continue to be vital in maintaining service standards, offering multilingual support, and ensuring that customers can easily access help. No further timeline was disclosed at the time of publication.
Business Communications artificial intelligence automation business automation contact centers
SpaceX's Starmind Plans 1 Million AI Satellites Amid Collision Risks
SpaceX has announced its ambitious Starmind project, which aims to deploy 1 million AI satellites in orbits between 500 and 2,000 km. This initiative, confirmed by Elon Musk on June 23, 2026, follows a merger with xAI, valuing the combined entity at $1.25 trillion. The satellites will function as orbital data centers, processing AI workloads powered by solar arrays and linked by optical lasers. The significance of Starmind lies in its potential to add 100 gigawatts of AI compute capacity annually, contingent on the successful operation of the Starship launch system. However, the project raises concerns regarding space debris, as the current orbital environment is already congested, with a 20% increase in collision risk reported since 2024. The European Space Agency has highlighted that the density of debris in low Earth orbit is now comparable to that of active satellites, complicating the operational landscape for new entrants like Starmind. Looking ahead, the first operational orbital AI deployments are targeted for 2028, with test launches expected in early 2027. However, the project faces scrutiny regarding its impact on space debris, as even a 1% failure rate could significantly increase the number of uncontrollable objects in orbit, exacerbating existing risks. No further timeline was disclosed at the time of publication.
AI’s Volatile Power Use Quietly Tests Grid Limits
The rapid growth of artificial intelligence infrastructure is reshaping electricity demand dynamics, posing new challenges for grid operators. As data centers are projected to consume 3 to 4 percent of global electricity by the end of the decade, their energy consumption patterns are becoming increasingly unpredictable. Unlike traditional industrial loads, AI workloads can fluctuate dramatically within milliseconds, driven by synchronized computational tasks and varying user demands. This unpredictability complicates grid management, as it creates abrupt demand spikes that can stress local infrastructure, particularly in regions like Northern Virginia, known as "Data Center Alley," where data centers are concentrated. Utilities, including Dominion Energy, are adjusting their forecasts to account for this rapid growth, but existing regulatory frameworks often fail to address the complexities introduced by high-density compute clusters. These facilities not only require significant power but also generate unique challenges related to thermal management and power quality. As a result, grid operators are exploring new demand response mechanisms and flexible scheduling to mitigate the impact of these fluctuating loads. The Electric Reliability Council of Texas (ERCOT) has acknowledged the implications of large flexible loads for grid stability and planning. As AI infrastructure continues to expand, it is crucial for regulatory and operational frameworks to adapt, focusing not just on total energy consumption but also on the volatility and geographic concentration of demand. Understanding these new consumption patterns will be essential for maintaining grid resilience in the face of evolving energy needs.
Data-centers Artificial-intelligence Electrical-grid Demand-response
Your Next AI Query May Travel Where the Power Is
Nvidia, in collaboration with InfraPartners, Prologis, and the Electric Power Research Institute (EPRI), is set to launch a pilot project later this year to construct approximately 25 micro data centers near utility substations across five U.S. states. This initiative aims to address the growing energy demands of the AI industry, which is projected to consume 9 to 17 percent of the country’s electricity generation by 2030. By strategically locating these small data centers, each with a capacity of 5 to 20 megawatts, the project seeks to enhance flexibility in power consumption and optimize the use of available electricity. The approach involves shifting computational workloads to different substations based on real-time power availability, thereby alleviating pressure on overloaded substations and maximizing overall energy efficiency. With U.S. grid operators typically utilizing only 53 percent of their generation capacity, this strategy could significantly increase the effective power supply for data centers. As AI workloads evolve, particularly in inference tasks that require less intensive computational resources compared to training, the micro data centers can dynamically route workloads to where power is most accessible. The project, termed “distributed inference,” is expected to begin construction by the end of 2026, with the goal of demonstrating a new model for data center operations that aligns with the increasing demand for energy-efficient solutions in the tech industry.
Ai-data-centers Nvidia Epri Power-generation
Fleet-Capable Downward Drilling Robot Launches with 10x Speed Advantage
A new fleet-capable downward drilling robot has been launched, achieving drilling speeds up to 10 times faster than traditional methods. This innovation has significantly reduced construction timelines by an impressive 190 weeks across 26 major projects, marking a substantial advancement in construction technology for data centers. The introduction of this robot is significant for the construction industry, particularly in the data center sector, where efficiency and speed are critical. Media outlets, including Fast Company, have highlighted the robot's potential to transform construction processes, emphasizing its ability to drastically accelerate project timelines and improve overall productivity. Looking ahead, industry professionals are keen to observe the robot's performance in upcoming projects and its impact on construction efficiency. No further timeline was disclosed at the time of publication regarding future deployments or additional features planned for this innovative technology.
IEEE Honors Robotics Pioneer Toshio Fukuda
Toshio Fukuda has been blazing trails for most of his career. He is considered to be one of the most prolific scholars in robotics, writing more than 2,000 research papers and authoring several books on the field. He’s an influential figure thanks to his pioneering work developing biomedical robotic systems, industrial robots, micro-nano robotics, mechatronics, and AI-driven automation.Fukuda launched one of the first robotics conferences, the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It is still popular almost 40 years later.Toshio FukudaEmployerEgypt-Japan University of Science and Technology, in Alexandria TitleProfessor and vice president of research Member gradeLife Fellow Alma matersWaseda University, in Tokyo; University of Tokyo An IEEE Life Fellow, he is a professor emeritus in the department of micro-nano systems engineering and a visiting professor at Nagoya University, in Japan, where he taught for nearly 25 years. Currently, he is a vice president of research at the Egypt-Japan University of Science and Technology, in Alexandria, Egypt.Within IEEE, Fukuda has held top volunteer positions including the organization’s highest office: He served as IEEE president in 2020, becoming the first person of Asian descent to hold the role.He’s a former program director of Japan’s Moonshot program, which by 2050 intends to develop advanced AI robots.Born in Japan, Fukuda has been recognized by the country for his contributions to science with two of its highest awards: the Medal of Honor with a purple ribbon in 2015 and the Order of the Sacred Treasure in 2022.IEEE honored him with this year’s Richard M. Emberson Award for “distinguished service advancing the technical objectives of IEEE, especially in the area of robotics.” The IEEE Board-level award is sponsored by the IEEE Technical Activities Board. Fukuda received the award on 24 April at a ceremony in New York City.As a former IEEE president who has served as a master of ceremonies at several of the organization’s major award events, Fukuda noted that he is more accustomed to bestowing awards than receiving them.“It’s very interesting to be on the receiving end,” he says.The journey into robotics researchAs a teenager, Fukuda spent his summer breaks teaching himself how to build things including transistor radios and steam engines.“It was very nice to have a hands-on hobby and make these kinds of things myself,” he says. His experimentation led him to study engineering.He earned a bachelor’s degree in engineering in 1971 from Waseda University, in Tokyo. He says one of his professors there—Ichiro Kato, regarded as the father of Japanese robotics research—was a good mentor who made a positive impact.Fukuda’s research interests were robotics and mechatronics, a field that combines robotics, electronics, computer science, and control systems.He went on to earn a master’s degree and a doctorate in science from the University of Tokyo, in 1971 and 1977. During those years, he also attended Yale, where he conducted research on advanced control theory in 1973.He reflects fondly on his time at Yale: “It was a very nice environment and a kind of free-thinking atmosphere. It motivated me to study more.”“IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.”While at Yale, Fukuda served as an assistant to his advisor—which led him to consider a career in academia, he says, because he enjoyed the freedom that research work afforded him.But he realized that such freedom comes with a price. University researchers are expected to raise the money that funds their work. He compares researchers to small-business owners who have to bring in money to keep their enterprise afloat.That realization led him to select robotics as his field because he intended to develop technologies useful to industry, he says.After earning his doctorate, he returned to Japan in 1977 to work as a research scientist at the government’s Mechanical Engineering Laboratory, later renamed the National Institute of Advanced Industrial Science and Technology, in Tsukuba.“There was a lot of research going on at the lab, including practical robotics and theory,” he says.He left Japan in 1979 to become a visiting research fellow at the University of Stuttgart, in Germany. During his year there, he studied systems, software problems, and related topics.He returned to Japan and was hired as an associate professor of mechanical engineering at the Tokyo University of Science. He conducted research into practical uses for robots by visiting industrial plants. He decided to develop robots that inspect industrial equipment such as those used in assembly plants, oil refineries, and power stations—places that “can be hostile environments for humans,” he says.His work drew interest from chemical, oil, and utility companies.“I got a lot of money from them for this very practical application, which funded my research,” he says, laughing.Developing popular robotic systemsFukuda grew tired of making those robots, he says, so he switched to creating ones for scientific applications. He developed many techniques, but he probably is best known for his modular, cellular robotic systems (CEBOTs), which he introduced in 1985.He has described how CEBOTs work in numerous papers published in the IEEE Xplore Digital Library.The CEBOT system is composed of a number of autonomous robotic cells that stick together like interlocking Lego plastic bricks, he says.Each cell is a fundamental modular unit that has a function. When a simple task is given, the system can analyze it and generate the structure of the cellular manipulator. The cells connect to and detach from each other through connection mechanisms and cooperate mutually, creating complex structures and configurations.“You start developing from the component-wise to the cell-wise to a small functional unit—and then you come up with clusters that make bigger systems. We can make a society of robot beings like that,” he explained in his oral history published on the Engineering and Technology History Wiki. “It’s a distributed robotic system, a self-organized robotic system, and also an evolutionary robotic system.“It’s also a fault-tolerant robot system because if something is wrong, you just remove those things and make a new one. You keep the system working. That’s a great thing.”Today CEBOTs are used for a variety of tasks such as delivering medication in hospitals, assisting with planting crops, and transporting products in distribution centers. Check out IEEE Spectrum’s Robots Guide for news from the world of robotics.In 1989 Fukuda joined Nagoya University as a professor of mechanical engineering and micro-nano systems engineering. During his 24-year career there, he was director of the university’s Center for Micro-Nano Mechatronics. He developed a long list of technologies at the university, including many for medical applications. He also conducted groundbreaking research into intelligent robotic systems and micro- and nano-robotics.Another technology he is known for is brachiation robots, which he helped develop in 1988. He calls them monkey robots because they’re based on the pendulum-like movement of monkeys swinging from tree to tree. The gravity-based locomotion enables continuous movement.Brachiation robots now are inspecting high-voltage transmission towers and bridges, searching damaged buildings for survivors, and performing maintenance on pipelines and cables.Fukuda retired from the university in 2013 and was named professor emeritus.He didn’t stay retired for long, though. He next held a teaching appointment at Meijo University, in Nagoya, until he left in 2022 to join the Egypt-Japan University.A prominent volunteerHe joined IEEE in 1980 at the encouragement of one of his research advisors, Professor Fumio Harashima, now an IEEE Life Fellow. After attending conferences and reading the organization’s publications, Fukuda says, he looked forward to becoming more involved.“I wanted to know how to organize a conference and how to edit a paper for one of its Transactions,” he says. “I wanted to know what was going on from inside the organization, not just the outside.”In 1988 he was the founding chair and organizer of IROS, in Tokyo. The conference had 330 attendees that year, and was supported by Harashima. Today it is one of the largest and most prestigious conferences on the topic, attracting more than 9,000 people annually. Out of 120,000 conferences, it was the only conference in the Nature Index database for this year, Fukuda says.In 1996 he and other members launched IEEE Transactions on Mechatronics.He was the founding president of the IEEE Nanotechnology Council, which was established in 2002. He is considered a pioneer in nanotechnology research, particularly regarding how it relates to robotics.Over the years, he has held numerous volunteer positions on IEEE editorial boards and committees.He was the 1998–1999 president of the IEEE Robotics and Automation Society, becoming the first non-U.S. member to hold the title.He was director of IEEE Division X (2001–2002 and 2017–2018), which covers intelligent systems, biological engineering, robotics, control systems, and photonic technologies. He served as the 2013–2014 director of IEEE Region 10 (Asia-Pacific).As the 2020 IEEE president, Fukuda saw the organization through the early part of the COVID-19 pandemic. Because of travel restrictions, he realized IEEE should change how it offered its in-person services, specifically educational programs. He encouraged IEEE Educational Activities to develop an online learning platform. The IEEE Learning Network started with just three courses and now offers nearly 2,000 courses, webinars, and learning materials.An award-winning memberThe Emberson Award joins a slew of other recognitions Fukuda has received from IEEE. They include several from the IEEE Robotics and Automation Society: a 2004 Pioneer Award, a 2009 Saridis Leadership Award, and the 2011 Harashima Award for Innovative Technologies. He is also a recipient of the Board-level 2010 IEEE Robotics and Automation Technical Field Award.He says he feels strongly that IEEE should be a diverse organization that is welcoming to all. As IEEE president, he led efforts to devise a diversity, equity, and inclusion program. Several policies, procedures, and bylaws were revised to give members a safe, inclusive place for discourse.“It’s important for IEEE to make everyone feel comfortable,” he says. “DEI programs are important. All people should be equal. IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.“It accepted me, from the Far East. That’s why I like it.”You can learn more about Fukuda and his career from the oral history conducted by the IEEE History Center.
Robotics Robots Ieee-member-news Type-ti Ieee-awards Toshio-fukuda
Small-AI Models Gain Traction Around the World
One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year.The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says.The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. “Our server was in the United States, and just to get the result of a single scan was taking me over 5 minutes.”So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo.More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of “small AI.”Small AI for Global Health Care AccessSmall AI is a far cry from wealthy nations’ colossal large language models (LLMs), hyperscale data centers, multibillion-dollar investments, and debates about AI consciousness. But for millions of people around the world, the only AI that matters, and often the only kind available, is small. (According to a World Bank Report issued in November, only 0.7 percent of internet users in the world’s poorest countries have used ChatGPT, compared to a quarter of all internet users in the most developed nations.)“Most people are discussing AI from the LLM/generative side. But that needs a lot of computing power, electricity, massive data, and skilled people to manage it,” Ajay Banga, president of the World Bank, said last January at the World Economic Forum, in Davos. “Outside the developed world, other than maybe India and China, very few countries have that combination.”By contrast, small AI can deliver useful, even life-saving services to people in areas that have none of those things, Banga said. In India, where the government’s AI plans call for more development of small AI, many such systems are working for farmers.For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.“This is the most important area in AI nowadays,” says Marcelo José Rovai, a professor at the Institute of Engineering and Information Systems at the Federal University of Itajubá, in Brazil, who was involved in all three projects. “It’s growing very fast.”Low-Power, Small-AI Models on Devices Small AI models can run on a variety of low-power devices, including [from left to right] an Arduino Nano 33 BLE Sense, a Seeed Wio Terminal, and an Arduino Portenta.Moez AltayebFor Alonge, Rovai, and other advocates, small AI is not just “a promising trend,” as that November World Bank report calls it. It may be, in the long term, the form of AI that will touch the most lives and remain sustainable after some of the giant models become too costly for most users.“I think the future of AI is not like one giant model, at a center. I think it’s millions of small, precise models deployed at the edge, each one solving like a specific problem, a specific context,” Alonge says. This is partly because much of humanity—including people in parts of rich countries as well as the developing world—lives without access to cutting-edge frontier models. But, he says, it’s also because those models are not sustainable.“If someone is not subsidizing it, most people will not be able to afford those models. So those of us who are said to be small-AI developers are the ones who will have to build for the majority of the world,” Alonge says.There is no strict definition of “small AI,” but people often use the term for language models with at most a few billion parameters. (Compare that to cutting-edge models, which can include more than a trillion.) That’s small enough to run directly on a phone or a Raspberry Pi. That’s what allows these applications to run on devices without a connection to a data center and use only a few watts of power, often supplied by a battery or a solar panel.Despite their small footprint, these models aren’t fundamentally different technology from that of gigantic AI models, Rovai says. Many instances of small language models were created the same way the phone-based version of Alonge’s pharmaceuticals scanner was—by “pruning” large models, or removing the parameters that weren’t involved in the task. The result is a system that’s less capable generally but still very good at the specific job it was pruned for, Rovai says. A lighter version of RxAll’s RxScanner spectrometer sends its results to an AI model run locally on a phone to check that a drug’s molecular signature is genuine.RxAllOther small models are created by “distillation.” They are trained to mimic a large model, until their performance approaches that of their “teacher,” Rovai says. In other cases, a larger model’s precision is reduced, for example, so that a model run on 32-bit architecture can run on 8-bit designs. In situations where the machine learning application is being used to classify data or predict patterns (like an ant infestation), it’s trained from the beginning on a small device, not derived from a larger model at all. Running all these small, specialized systems is becoming easier, Rovai says, for two reasons.The first reason is that hardware is getting better and more capable while using less power, he says. This means more and more phones can run small AI—especially those equipped with neural processing units, which are specialized chips that handle AI tasks like facial recognition and changing the brightness, shadows, or contrast in a photo.In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.The second reason Rovai cites is the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.Rovai illustrated these reasons on a Zoom call, using one of his most recent experiments. Holding up a device, he says, “This is the new Arduino UNO Q—a US $50 device with a Qualcomm chipset. I’m running a language model here, which collects data from sensors and analyzes that data to detect tiny pools of water where mosquitoes might be breeding. It takes 3 watts to run it.”Support for Small-AI DevelopmentConvinced that millions of people are already benefiting from these kinds of applications, the World Bank now actively promotes small AI with grants, mentorship programs, financing, technical advice, and models of government policies that are friendly for small-AI development. For example, in Rwanda, the World Bank is backing a government program to help low-income households get devices that can run AI.All that said, no one claims that large language models are going away entirely. To create a generative AI that can run on a phone or other small device requires the architectural insights, data processing, and results of a larger model, Rovai says. “We need the big models to create these smaller models.” And for all that small AI can benefit people without access to big AI, the technology can’t solve the larger problems of development and digital inequality, Alonge says. Implementing small AI won’t allow nations to escape the challenge of creating an ecosystem to support AI: reliable power, a supply chain that works, and an educational system that develops the talents needed to create AI tools.Though his drug-scanning system can run for days on a phone with no connection, “you still want to be able to enable periodic syncing for updates with new signatures for the medications and analytics,” Alonge says. “And even when you are using batteries, reliable power is important. That phone battery is not going to last forever.”In many parts of the world, the future of small AI isn’t assured, he says. “It works, and many places will eventually need to use it. The question is whether or not the political actors are wise enough to invest in infrastructure to support it long term.”
Small-language-models Artificial-intelligence Llms
$4.1 Billion Deal Shows Why Ferrari and Tesla Are Ditching Copper for a Substitute
$4.1 Billion Deal Shows Why Ferrari and Tesla Are Ditching Copper for a Substitute $4.1 Billion Deal Shows Why Ferrari and Tesla Are Ditching Copper for a Substitute Stjepan Kalinic Sun, July 5, 2026 at 8:31 AM PDT 6 min read RACE.MI TSLA Benzinga and Yahoo Finance LLC may earn commission or revenue on some items through the links below. Substitution is one of the fundamental economic forces. If a product goes up in price, consumers have a direct incentive to switch to a cheaper substitute. While branding power dictates some price flexibility, such calculations are more straightforward for fungible commodities. When copper costs about $15,000 a metric ton, manufacturers have every right to ask – does every wire really need to be copper? With data centers, grid upgrades and green-energy projects tightening supply, the answer from automakers is increasingly no. Aluminum, trading at $3,100 per ton, is being promoted wherever physics allows. Don't Miss: A single bad hire can set a startup back years. Here are the 5 hires founders most often misjudge — and why Still Learning the Market? These 50 Must-Know Terms Can Help You Catch Up Fast Driving Investment and Corporate Consolidation Aside from being much cheaper, the metal is lighter and good enough for many vehicle applications. The appeal to save on weight is just a bonus for range-anxious electric vehicles. Ferrari has used aluminum in bodies, engines, and chassis for years and has recently begun using aluminum power cables in the 296 hybrid and other models. The payoff can be meaningful: wiring weight savings of up to 20%. "We are not choosing aluminum because it's cheaper; we choose the material that has better performance," the firm's communications executive Dario Esposito said per Reuters. Market interest is driving asset transactions, as Alcoa Corp. has just signed a binding agreement to acquire most of South32 Ltd.'s aluminum value chain for $4.1 billion. These include assets in Australia, South Africa and Brazil, but not the Mozal operation in Mozambique. The largest domestic aluminum producer expects the transaction will generate about $900 million in synergies. JPMorgan estimates the aluminum substitution could affect about 2% of global copper demand this year, and potentially as much as 6% by 2030. Trending: Avoid the #1 Investing Mistake: How Your 'Safe' Holdings Could Be Costing You Big Time A Partial Substitute Still, aluminum is not copper with a discount sticker. It is less electrically conductive, meaning cables often must be thicker to carry the same current. Those properties create problems in tight spaces – shared by both data centers and automobiles. For high-performance systems and specialized applications, copper's efficiency still remains ahead. Story Continues Then, there are environmental and geopolitical complications. The final phase of aluminum production is energy-intensive, often generating a much larger carbon footprint than copper. Energy prices have squeezed domestic producers and closed smelters, while trade frictions, including U.S. tariffs, further complicate sourcing. Cable makers provide some guidance on the issue. Xavier Mathieu, VP of Nexans, the second-largest global cable manufacturer, said buyers typically start switching when copper costs about 3.5 times as much as aluminum. The current ratio exceeds 4.2. The math means aluminum will keep swallowing market share where weight and space permit, but copper's performance edge still means it is the hedge, not the heir. Photo by laowaika via Shutterstock Read Next: Skip the Regrets: The Essential Retirement Tips Experts Wish Everyone Knew Earlier. Think you're saving enough for your kids? You might be dangerously off — see why Building Wealth Across More Than Just the Market Building a resilient portfolio means thinking beyond a single asset or market trend. Economic cycles shift, sectors rise and fall, and no one investment performs well in every environment. That's why many investors look to diversify with platforms that provide access to real estate, fixed-income opportunities, precious metals, and even self-directed retirement accounts. By spreading exposure across multiple asset classes, it becomes easier to manage risk, capture steady returns, and create long-term wealth that isn't tied to the fortunes of just one company or industry. Arrived Backed by Jeff Bezos, Arrived Homes makes real estate investing accessible with a low barrier to entry. Investors can buy fractional shares of single-family rentals and vacation homes starting with as little as $100. This allows everyday investors to diversify into real estate, collect rental income, and build long-term wealth without needing to manage properties directly. FarmTogether Farmland has historically held its value through market volatility and delivered returns uncorrelated to stocks and bonds. For accredited investors, FarmTogether offers direct access to high-quality U.S. farmland starting at $15,000 — fully ma
The Lab Mistake That Might Revolutionize Computing
Researchers have made a significant breakthrough in artificial intelligence technology by discovering a new way to create electronic components that mimic the behavior of biological neurons and synapses. This development, which occurred in a laboratory in 2024, could drastically reduce the energy consumption associated with AI applications. Currently, AI systems rely on powerful GPUs housed in data centers, consuming up to 1,000 watts each, which is comparable to household appliances. In contrast, the human brain operates at a fraction of that energy efficiency. The team, led by researchers Mario Lanza and Sebastian Pazos, stumbled upon this innovation while experimenting with metal-oxide-semiconductor field-effect transistors (MOSFETs). They found that by manipulating the bulk terminal of a MOSFET, they could replicate neuron-like behavior, producing sharp current spikes similar to those of biological neurons. This discovery not only allows for the creation of artificial neurons but also enables the development of artificial synapses, leading to a new type of neurosynaptic random-access memory (NSRAM). The implications of this technology are vast, as it could lead to brain-inspired microchips that are more energy-efficient than current GPUs, particularly for smaller-scale AI tasks. The researchers are now focused on refining their models and conducting further simulations to optimize performance. If successful, this innovation could pave the way for a new generation of AI systems that are both powerful and environmentally sustainable.
Neuromorphic-computing Cmos Mosfet Synapse
Who is responsible for M365 data protection? Hornetsecurity offers comprehensive security solutions.
Hornetsecurity has launched its backup solution, "365 Total Backup," specifically designed for Microsoft 365 environments in Japan. This new service features security capabilities such as data storage in data centers that are completely independent from the M365 infrastructure and a self-service recovery option that alleviates the burden on administrators. The introduction of this solution aims to enhance data security and management efficiency for organizations utilizing Microsoft 365.
A New Voyage for CNC Machine Tools: Qiaofeng Aligns with Humanoid Robots
Qiaofeng Intelligent has introduced four high-precision CNC machining centers specifically designed for the rapidly expanding humanoid robot sector. This launch comes in response to the increasing demand for specialized equipment within the industry. The company's transition from general-purpose machinery to dedicated machines underscores its commitment to fostering the growth of advanced manufacturing in China. By focusing on this niche market, Qiaofeng Intelligent aims to enhance production capabilities and support innovation in the field of robotics.
CNC Machine Tools Humanoid Robots AI Cooling Technology Precision Manufacturing
Decentralized Training Can Help Solve AI’s Energy Woes
As the demand for artificial intelligence (AI) continues to surge, concerns over its significant energy consumption and carbon footprint have prompted major tech companies to explore nuclear energy as a sustainable solution. While nuclear-powered data centers remain a future prospect, industry leaders are currently focusing on decentralizing AI model training to address the escalating energy requirements. This approach distributes training tasks across a network of independent nodes, utilizing existing computing resources, such as dormant servers and solar-powered home computers, rather than relying solely on traditional data centers. Companies like Nvidia and Cisco are enhancing their infrastructure to support this decentralized model, allowing for efficient AI training across geographically dispersed data centers. Additionally, platforms like Akash Network are facilitating a "GPU-as-a-Service" model, enabling users with underutilized GPUs to rent out their computing power. On the software side, advancements in federated learning and algorithms like DiLoCo are being implemented to optimize decentralized training while minimizing communication costs and enhancing fault tolerance. These innovations allow for collaborative model training without the need for constant data exchange, thus improving efficiency. Akash Network's Starcluster program aims to convert homes into functional data centers by leveraging solar energy and existing computing devices. This initiative seeks to make participation accessible and is targeting a 2027 launch. By decentralizing AI training, the industry hopes to create a more energy-efficient and environmentally sustainable future for AI development.
Training Ai-energy Data-center Large-language-models
How Robotics Is Transforming the Worker Experience Across 3PL, Manufacturing & E-Commerce
As global supply chains experience a surge in demand, third-party logistics providers (3PLs), manufacturers, and e-commerce fulfillment centers are facing unprecedented pressure. This situation has been exacerbated by ongoing labor shortages, which have led to increased injury rates and highlighted the physically demanding nature of warehouse and factory jobs. The challenges these sectors face are prompting a reevaluation of operational strategies to ensure efficiency and worker safety in an increasingly competitive environment.
Silicon Sensing Produced its 30 Millionth Inertial Sensor
Silicon Sensing Systems Ltd has reached a significant milestone by producing its 30 millionth inertial sensor. Established in 1999, the company has become a key player in the global market, supplying advanced inertial sensors and systems across various sectors, including robotics, industrial production, marine, aerospace, defense, transport, and space. The company's innovative approach centers on high-performance gyroscopes, accelerometers, inertial measurement units (IMUs), and combi-sensors, all leveraging its proprietary micro electro-mechanical systems (MEMS) technology. These products are designed to outperform traditional systems, such as fiber optic gyros (FOG) and dynamically tuned gyros (DTG), by offering superior performance in a more compact and robust form. This achievement underscores Silicon Sensing's commitment to advancing sensor technology and meeting the evolving demands of diverse industries worldwide.
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China’s SAMR Opens Case to Investigate Qualcomm’s Autotalks Acquisition
China’s State Administration for Market Regulation (SAMR) has initiated an investigation into Qualcomm regarding its acquisition of V2X chipmaker Autotalks. The inquiry, announced today, centers on potential violations of the country’s Anti-Monopoly Law, specifically related to Qualcomm's alleged failure to report the concentration of undertakings associated with the deal. Qualcomm had disclosed the acquisition in May 2023, which has now raised concerns about compliance with regulatory requirements in China. The investigation underscores the increasing scrutiny of foreign investments in the Chinese technology sector and reflects the government's commitment to enforcing antitrust regulations.
News FeedRobotics needs a service framework.
RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.
