The Conversation (US)
중도 성향
기타
Detroit’s high property taxes are driving a housing affordability crisis – how can city leaders bring down costs?
Property tax reform in Detroit could boost homeownership and attract new residents and businesses.
🇺🇸 미국 · "PROPER" · 총 131건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 11,748건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 11,746건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 19.0(중도 균형)입니다.
Property tax reform in Detroit could boost homeownership and attract new residents and businesses.
This sponsored article is brought to you by Wetour Robotics. A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. They call for a smarter way to be heard by the machines that already exist. The industry has been building from one side The past three years of Physical AI have been a story of remarkable progress on the robot side of the loop. Companies like Boston Dynamics, Figure, and Unitree have advanced actuators, locomotion, and dexterity to a level that would have seemed implausible a decade ago. Google DeepMind’s Gemini Robotics has redefined what vision-language-action models can do in unstructured settings. The trajectory of the hardware and the foundation models is real, and it is accelerating. But there is another side to this loop, and it has been treated as a solved problem for too long. The interface between humans and machines has defaulted, for 40 years, to three input modalities: screens, buttons, and voice. Each of those assumes the user can stop, look down, and translate intent into structured commands. That assumption breaks the moment the work moves into a real environment. On a turbine. On a dock. On a sidewalk. In any setting where hands are occupied, eyes are committed, or speaking is impractical, the conventional interface stack quietly fails. Spatial Intent Fusion is the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent: Your body is the interface. The bottleneck on the human side of the loop is becoming as important as the one on the machine side. And solving it requires a different question. Not how do we make the robot more capable, but how do we let the human participate in the computing system as naturally as the robot already does. Wetour Robotics’ bet: put the human back into the computing loop Wetour Robotics is betting that the next architectural leap in Physical AI is not about making the robot more capable. It is about making the human a first-class node in the computing network, with the same kind of low-latency, high-fidelity participation that connected devices already enjoy. Wetour Robotics’ engineers frame the problem this way: a wristband that recognizes a gesture is not enough. A camera that recognizes a scene is not enough. The information a human carries about what they are about to do is distributed across multiple channels, including where their body is in space, what their eyes are attending to, and what their muscles are preparing to do, and any single channel observed in isolation is ambiguous. Reconstructing intent reliably means fusing those channels at the operating system level, with latency low enough that the loop feels closed rather than mediated. This approach has a name. Wetour Robotics calls it Spatial Intent Fusion: the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent, fused into a single real-time command for any connected physical device. It is the technical implementation behind a simpler positioning statement the company uses externally: your body is the interface. Orchestra is a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Wetour Robotics The architecture: three layers, four engines, one loop Orchestra is not a single device but a layered platform, designed from the start to be sensor-flexible and actuator-agnostic. The architecture decomposes into three perception layers and four coordination engines. Orchestra itself is the local compute and orchestration core: a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Edge inference is non-negotiable for this application. Full-chain latency from biosignal acquisition to actuator command is held under 100 milliseconds, the envelope inside which closed-loop control feels natural rather than laggy. VisionLink handles visual and spatial perception. Cameras feed into vision models that identify objects, estimate distances, and track environmental context. VisionLink is designed not as a passive recognition layer but as a real-time command generator: its outputs feed directly into Orchestra OS to be fused with biosignal data. Conductor is the biosignal pipeline. It ingests raw surface electromyographic (sEMG) data from a wrist-worn device, classifies temporal patterns into discrete gestures or continuous control signals, and outputs actuator commands. The technically interesting property of sEMG for this use case is that the signal precedes visible motion. Motor unit action potentials appear at the skin surface roughly 50 to 80 milliseconds before a finger completes the corresponding gesture. Wetour Robotics calls this property pre-motion intent sensing, and it is what allows Orchestra to anticipate user intent rather than react to it. On top of the three perception layers, Orchestra OS runs four coordination engines. The Perception Engine ingests and normalizes raw sensor streams. The Intent Engine performs Spatial Intent Fusion across modalities, resolving what the user is trying to do given where they are, what they are looking at, and what their hand is signaling. The Orchestration Engine translates intent into device-specific command sequences for any connected actuator. The Safety Engine arbitrates conflicting commands, enforces operational envelopes, and gates execution against runtime safety conditions. Wetour Robotics The trade-offs we’re honest about No system that bridges the human body and the digital world is finished. Three engineering challenges remain open, and the company addresses each with a deliberate trade-off rather than a claim of having fully solved it. Baseline stability of sEMG under motion. In a stationary user, continuous gesture recognition from sEMG is reliable. Once the user is walking, climbing, or otherwise moving, motion artifacts and electrode drift degrade the signal in ways that are difficult to fully compensate for. Rather than overpromise on continuous control in dynamic settings, Orchestra defaults to a smaller set of robust discrete gestures in complex operating environments, and reserves continuous control modes for contexts where the signal-to-noise ratio supports them. Miniaturization of edge AI compute. Running the Orchestra control loop entirely at the edge requires real on-device inference, which has historically meant trading off between compute capacity, battery life, and form factor. Wetour Robotics’ approach has been a compact carrier board paired with a thermal design and a battery module sized for all-day wearability. The result is a hub that travels with the user rather than tethering them to a desk, and that performs the full perception-to-actuation loop without offloading to the cloud. Heterogeneity of third-party device protocols. The actuator side of the loop is a fragmented landscape. Different manufacturers expose different command interfaces, different communication stacks, and different safety conventions, and a Physical AI operating system has to integrate with all of them. Wetour Robotics uses an AI-agent layer to negotiate connection and protocol translation adaptively, so that Orchestra OS can ingest data from a wide range of devices, run them through neural network models that infer human intent, and emit the right command on the right protocol for the device on the other end. Why this matters, and why it helps the rest of the field The history of computing is a history of interface revolutions. Command lines gave way to graphical user interfaces, which gave way to touch, which gave way to voice. Each transition expanded who could participate in the system and what they could do with it. The next transition is not about a new screen or a new microphone. It is about treating the human body itself as a participant in the computing network, capable of contributing intent at the same speed and fidelity that any other connected node can. The history of computing is a history of interface revolutions. The next transition is not about a new screen or a new microphone — it is about treating the human body itself as a participant in the computing network. This path is not a competitor to the work being done on humanoid robots, foundation models for embodied AI, and dexterous manipulation. It is the missing complement to that work. The hardest open problem for humanoid systems is the data: every natural interaction between a human and the physical world is a potential training signal, and most of those interactions are currently invisible to any computing system. As more humans become first-class nodes in the loop, those interactions become observable, structured, and ultimately useful for training the next generation of embodied AI, including the humanoid robots being developed today. In other words: putting the human back into the computing loop is not just about better interfaces for individual users. It is about generating the kind of grounded, in-the-wild human-machine interaction data that the broader Physical AI ecosystem will need to keep advancing. The robot side and the human side of the loop are not two competing futures. They are two halves of the same one. That is what Wetour Robotics means when it says: Your body is the interface. Learn more at wetourrobotics.com.
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In the late 1940s—when computer engineers were grappling with unreliable hardware and noisy transmission environments—a team of engineers inside a modest lab at the University of Manchester, England, confronted a problem so fundamental that it threatened the viability of digital computing itself. Machines could generate bits, but they could not reliably read them back. The inconsistent reading back of memory data did not initially present itself as a grand theoretical challenge. It showed up as something more mundane: inconsistent computing results. Engineers including Frederic C. Williams, Tom Kilburn, and G. E. (Tommy) Thomas traced the failures not to logic errors but to the physical behavior of the machines themselves. The team devised a technique for keeping a transmitter and a receiver synchronized without relying on a separate clock signal. Their innovation, known as Manchester code or phase encoding, encoded each bit with a transition in the middle of the bit period, effectively embedding timing information directly into the data stream to be a self-clocking signal. So, even if the signal degraded or the timing drifted slightly, the receiver could continually keep time based on those regular transitions. By eliminating the need for separate clocks and reducing synchronization errors, Manchester code made data transfer more robust across cables and circuits. Those qualities later made it a natural fit for technologies such as Ethernet and early data storage systems. Its self-clocking nature helped standardize how machines communicate, and it laid the groundwork for modern networking and digital communication protocols. On 13 April 2026, this breakthrough was honored with an IEEE Milestone plaque during a ceremony at the University of Manchester. Dignitaries from IEEE and the university attended the ceremony. Embedding timing in signals Those 1940s Manchester University engineers were working on systems that fed into the Manchester Mark I, one of the first practical stored-program machines. When troubles arose, they used oscilloscopes to probe signals. They found that electrical pulses did not arrive with consistent timing. Memory signals also blurred over time, making them harder to read, and when long runs of identical bits occurred, the waveform flattened into stretches with no transitions. That led to a crucial insight: The problem was not just detecting whether a signal was high or low; the system also lost track of when to sample the signal. Without reliable timing markers, even correctly formed signals were misread. Bits could effectively be lost or miscounted because the system fell out of sync. At first, the engineers tried to tame the hardware. They experimented with stabilizing circuits and more consistent pulse generation, attempting to impose a regular rhythm on an inherently unstable system. But the fixes proved fragile, and the electronics of the day could not maintain the required precision. So the Manchester group took a different approach. If the hardware could not provide a dependable clock, the signal itself would have to carry one. Instead of representing data as static levels, each bit changed state, with a guaranteed transition in the middle. Embedding timing in the signal reduced erratic behavior. Machines were suddenly able to reliably transmit, store, and read back data—an essential step toward practical stored-program computing. Making signals unmistakable The Manchester code addressed several issues at once. Regular transitions allowed continuous timing recovery. Transitions proved easier to detect than static levels, and long runs of identical bits no longer produced flat, ambiguous waveforms. Rather than fighting the imperfections of early electronics, the design worked with them. From lab curiosity to a global standard What began as a local solution in Manchester shaped digital communication systems for decades, including early Ethernet technology, for which timing and shared-medium communication were central challenges. According to Robert Metcalfe, a member of the team that built the first Ethernet system at Xerox PARC in 1973, he and his colleagues relied on Manchester code. “Manchester code solved a fundamental problem for us: timing,” Metcalfe says, explaining that each bit carried its own clock and removed the need for a global synchronized signal. That self-clocking property wasn’t the only benefit provided by the encoding scheme. On a shared coaxial cable, Manchester encoding did more than provide timing. Each transceiver left the medium undriven—effectively “off”—most of the time, allowing packets from other machines to pass without interference. Even during transmission, a station drove the signal only about half the time, leaving the line undriven during the other half of each bit cycle. This distinction—between a driven signal and an undriven line, rather than simple 1s and 0s—allowed receivers to recover both data and clock timing while also monitoring the cable for other activity. If a transceiver detected a signal when it expected the line to be undriven, the signal indicated that another station was transmitting at the same time. In other words, the system could detect collisions in real time and respond accordingly. The idea has proven durable far beyond local networks. Manchester code is being used aboard the Voyager spacecraft, which are now cruising through interstellar space—underscoring its reliability in extreme environments. The code also has found its way into everyday consumer electronics. Infrared remote controls for televisions and audio equipment commonly rely on Manchester code through protocols such as RC-5, developed by Philips in the early 1980s. The protocol encodes commands as timed infrared signals transmitted by a handset’s integrated circuit and LED, allowing devices to reliably interpret button presses even through noise and signal distortion. Manufacturers across Europe—and many in the United States—adopted the approach, extending Manchester code into the home. Why the Milestone matters An IEEE Milestone designation recognizes technologies with enduring impact. Manchester code qualifies because it solved a foundational timing problem at a critical moment in computing history. Without a way to embed timing in the data itself, early digital systems would have remained fragile and unreliable. Manchester code helped transform them into dependable machines, and it enabled much of today’s digital communication. “Manchester code solved a fundamental problem for us: timing,” —Robert Metcalfe, an Ethernet inventor Key participants at the plaque dedication ceremony included Tom Coughlin, 2024 IEEE president; Duncan Ivison, University of Manchester president and vice chancellor, and Nagham Saeed, chair of the IEEE U.K. and Ireland Section. Talks by Kees Schouhamer Immink (the 2017 IEEE Medal of Honor laureate probably best known for his work that made compact discs and other high-density digital media practical) and Peter Green (Manchester’s deputy dean for the engineering faculty) highlighted the code’s lasting impact on digital data storage and communications. The IEEE Milestone plaque for the Manchester code reads: “At this site in 1948–1949, Manchester code was invented for reliably encoding digital data stored on the Manchester Mark I computer’s magnetic drum. It became a standard for computer magnetic tapes and floppy disks and was used in digital communications, including the Voyager 1 and 2 spacecraft and early Ethernet networks. It found wide use in domestic remote controllers, radio frequency identification (RFID) tags, and many control network standards.” Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments worldwide. The IEEE U.K. and Ireland Section sponsored the nomination.
In two separate photos, split down the middle, the left photo has a man in a leather jacket holding a baseball bat, and another man is wearing body armor and a baseball hat, while in the photo on the right stands a man wearing body armor, a long black jacket, and a holstered sword.
From today's Second Circuit decision in Christian v. Keane, in an opinion by Judge Joseph Bianco, joined by Judge Eunice Lee… The post The Second Amendment, Guns on Private Property, Guns in Parks, and "The Fifth Element" appeared first on Reason.com.
This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia. Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capability to the infrastructure that underpins modern AI research, positioning Melbourne at the convergence of global convening and high-performance digital systems. Consistently ranked among the world’s most livable cities, Melbourne was named Time Out’s Best City in the World in 2026, the first Australian city to hold the title. Melbourne, Australia’s premier conference destination. Tourism Australia More materially for research and innovation, Melbourne is also the nation’s fastest‑growing capital, attracting increasing concentrations of engineering and technology talent, investment and international engagement. Australia’s artificial intelligence (AI) ecosystem is entering a new phase, defined less by isolated initiatives and more by the convergence of compute infrastructure, research intensity and international collaboration. Melbourne sits at this intersection. Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene. Sovereign AI compute, expanding hyperscale data center campuses and a growing pipeline of international research-led conferences are reshaping the city’s research landscape. Together, these elements position Melbourne as a focal point for applied AI research, advanced engineering and data-intensive science. The growing global influence of AI engineering, underscored by NVIDIA CEO Jensen Huang receiving the 2026 IEEE Medal of Honor, reflects the scale of this shift. In Melbourne, these factors form a reinforcing research flywheel linking infrastructure, discovery and collaboration. Rather than focusing on startup density or short-term commercial output, Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene. NVIDIA CEO Jensen Huang received the 2026 IEEE Medal of Honor.IEEE Sovereign AI foundations The most recent cornerstone of Melbourne’s AI capability is MAVERIC (Monash AdVanced Environment for Research and Intelligent Computing), Australia’s largest university-based AI supercomputer. Built and deployed by Monash University in partnership with NVIDIA, Dell Technologies, and CDC Data Centres, MAVERIC has been engineered specifically for large scale AI and data intensive science, with medical research representing a key priority. Indeed, in these regards MAVERIC has been designed to function as a Next Generation Trusted Research Environment thus ensuring that it is state-of-the-art and provides a safe and secure framework for the analysis of large sensitive datasets. Designed to support research projects including cancer and neurodegenerative disease detection, clinical trial analysis and drug discovery through to materials science and engineering, MAVERIC enables Australian researchers to train and evaluate large models domestically while keeping highly sensitive datasets secure and under national jurisdiction. This sovereign design is particularly relevant in fields such as medical research where privacy, regulation or intellectual property constraints limit the use of offshore cloud resources. Monash University Vice-Chancellor and President Professor Sharon Pickering with researchers [left to right] Professor Anton Peleg, Professor Victoria Mar, Professor James Whisstock, Vice-President (Strategy and Major Projects) Teresa Finlayson, and Professor Patrick Kwan.Eamon Gallagher (Australian Financial Review) Technically, the system reflects the latest shifts in high performance AI architecture. Built on NVIDIA GB200 NVL72 platforms and integrated using Dell’s rack scale infrastructure, MAVERIC employs closed loop liquid cooling to reduce water consumption compared with conventional air-cooled systems, aligning large scale compute growth with sustainability objectives while supporting high density, high throughput workloads. Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences commented, “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines. It will seed wonderful new cross-disciplinary collaborations, underpin the work of our best and brightest young researchers and will allow our scientists to continue to make major discoveries that positively impact the Australian and global population more broadly.” “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines.” —Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences Monash University frames MAVERIC not as a standalone asset, but as part of the national research infrastructure, intended to strengthen collaboration across academia, healthcare, government and industry. This approach positions Melbourne at the forefront of sovereign AI enabled research in the region. Data center scale as research infrastructure The infrastructure demands of modern AI research extend well beyond individual systems. Melbourne’s expanding data center footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously. Total data center investment, US$ billions.Source: Data Centres Global Report 2025 In February 2026, CDC Data Centres opened its first Melbourne campus in Brooklyn, with two live facilities and a third in planning. Combined with CDC’s Laverton campus, Melbourne is projected to host more than 800 megawatts of sovereign digital capacity, critical for AI workloads requiring sustained access to high-density power, cooling and secure environments. Parallel investment is underway in Fishermans Bend, where NEXTDC is developing a AUD $2 billion AI and digital infrastructure hub adjacent to the Innovation Precinct. Planned facilities include an AI Factory, a Mission Critical Operations Center and a Technology Center of Excellence, enabling sovereign AI, high-performance computing and cross-sector collaboration across health, defence and finance. Melbourne hosts Australia’s largest cluster of AI firms, with 188 companies, and more than 40 data centers currently operate across Victoria. The Victorian Government has complemented this growth with an initial AUD $5.5 million investment in the Sustainable Data Center Action Plan. Together, these developments reinforce Melbourne’s role as a national and increasingly global hub for high-performance AI infrastructure as model complexity and infrastructure dependency continue to accelerate. Applied AI research at scale Monash University is home to MAVERIC, Australia’s largest university-based AI supercomputer, built and deployed by Monash in partnership with NVIDIA, Dell Technologies, and CDC Data Centres.Monash University Melbourne’s research strength is underpinned by a dense university network with deep capability across AI, data science and engineering. Institutions including Monash University, the University of Melbourne, Deakin University, La Trobe University, RMIT University and Swinburne University of Technology collectively support research across machine learning, robotics, human-computer interaction, extended reality and advanced manufacturing. This concentration fosters applied collaboration where AI intersects with medicine, sustainability, cognitive systems and immersive technologies. For visiting researchers, it provides access not only to academic expertise but also to live infrastructure environments where research can be tested and validated, reinforcing Melbourne’s position as one of the Asia-Pacific’s most integrated AI research ecosystems. Conferences as research accelerators Plenary session at Melbourne Convention and Exhibition Center.Melbourne Convention Bureau Melbourne’s selection as host city for a growing number of international technology conferences reflects the convergence of research capability and infrastructure maturity. In September 2026, Data Center World Australia and The AI Summit Australia will be co-located at the Melbourne Convention and Exhibition Center, bringing together global leaders across AI, digital infrastructure and enterprise technology. The pairing highlights a broader reality: advances in AI are inseparable from the infrastructure that enables them. Melbourne’s expanding data center footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously. Research-led conferences are also expanding Melbourne’s global footprint. ICONIP 2026, hosted by Deakin University, will bring up to 700 researchers in neural networks and machine learning, followed in 2027 by IEEE VR, the leading conference on virtual reality and 3D user interfaces, attracting up to 1,000 delegates. In this context, conferences function not simply as events, but as infrastructure for knowledge transfer, supporting standards exchange, collaboration and system-level learning at global scale. A global platform for advancing research Sovereign compute, data center scale and a strong conference pipeline create a reinforcing cycle, enabling researchers to engage directly with infrastructure and industry well beyond the event itself. By closing the gap between theory and deployment, Melbourne supports deeper technical exchange and more enduring global research networks. This role was recognized in 2025 when the IEEE awarded Melbourne Convention Bureau the 2025 Organisational Supporting Friend of IEEE Member and Geographic Activities (MGA) — the first convention bureau in the Asia Pacific region to receive the acknowledgement as a result of the longstanding partnership with the IEEE Victorian Section. Melbourne Convention Bureau (MCB) representative Fatima Aboudrar, Senior Business Development Manager, with Vijay S. Paul, Immediate Past Chair, IEEE Victorian Section, receiving Supporting Friend Member recognition in 2025. As AI research becomes increasingly dependent on infrastructure scale, sovereign capability, and global collaboration, Melbourne is moving beyond hosting conversations to actively enabling the systems that advance AI and data‑driven research at global scale. Conference support in Melbourne Your browser does not support the video tag. Why host a conference in Melbourne, Australia.Melbourne Convention Bureau This ecosystem is underpinned by Melbourne’s highly accessible city center, where world-class venues, research institutions and industry hubs are located in close proximity. Free public transport and a compact city footprint enable seamless movement from conference floor to real-world application. Melbourne Convention Bureau (MCB) is a not-for-profit state government agency with over 60 years’ experience, that provides IEEE and its members with free support to bring international conferences to Melbourne, Australia. MCB’s support spans early-stage exploration and international bidding through to securing government funding, connecting organizers with venues, accommodation and event suppliers, and providing destination support for conference planning and delivery. Organizations considering a conference in Australia are encouraged to connect with MCB’s dedicated team, which supports IEEE conferences in Melbourne. Enquiries can be directed to info@melbournecb.com.au.
“Why are you here?” Fabrizio Pilo, an electrical engineer, asks me as we sit in an outdoor café near his home in Cagliari, an ancient city on the island of Sardinia. It’s a fair question. I’m a journalist from the United States. I’d just stepped off my flight 2 hours prior and come straight to this meeting, suitcase still stowed in my rental car. I’m here to see three intriguing new energy projects under development in Sardinia. I’d heard there’s strong public resistance to renewable energy, and I want to understand why that is. I tell Pilo, who is vice rector for innovation at the University of Cagliari, that I hope he’ll share some insights before I head out on a reporting trip across the island. (My answer seems to satisfy him, and he kindly gives me an hour of his time). This won’t be the first time that I’m asked to explain my presence on the island. I’d expected it, to some extent; I’m a foreign journalist poking around, after all. What I didn’t expect was the depth of Sardinians’ distrust, not just of journalists, but of any outsider, particularly ones with authority. Over the last few years, developers of wind and solar projects, most of whom aren’t from here, have been absorbing the bulk of this smoldering, communal wariness. Activists Maria Grazia Demontis [left] and Alberto Sala, photographed inside the archaeological monument Giants’ Tomb of Pascarédda, have worked to stop the construction of wind farms by organizing protests and taking legal actions through their organization Gallura Coordination. Luigi Avantaggiato In fact, the resistance is so widespread among Sardinians that over the course of two months in 2024, a grassroots petition to ban new wind and solar projects gathered over 210,000 certified signatures. That’s more than a quarter of Sardinia’s typical voter turnout and represents a cross-party consensus. People stood in long lines in public squares to sign. And it worked: Political leaders responded swiftly with an 18-month moratorium on renewable energy construction. “I’ve never seen so much engagement for anything” in Sardinia, says Elisa Sotgiu, a literary sociologist at the University of Oxford, who was born and raised on the island. “Sardinia has a bunch of problems like enormous unemployment. There’s lots of emigration because there are no jobs. It’s one of the poorest areas in Europe. The area is just decaying,” she says. “And yet the thing people are demonstrating against is renewable energy.” And the opposition continues: A network of mayors has mobilized for the cause. Thousands of people show up at organized protests. Activists vandalize grid equipment. Families are passing down these stories of resistance to their children as a point of pride. Local media outlets are egging it on, frequently publishing misinformation tinged with fearmongering. These aren’t just NIMBY complaints—not in the pejorative sense, at least. The resistance, and the distrust underlying it, is rooted in the island’s complex history, both recent and ancient. It’s based on a past that the Sardinian people carry with them—a past that has seeded a deep sense of suspicion and vulnerability. Resistance, I learn, is part of what it means to be Sardinian. Fabrizio Giulio Luca Pilo, vice rector of innovation at the University of Cagliari, has been working to help Sardinia transition to cleaner, more reliable energy. Luigi Avantaggiato “It is a very sad situation,” Pilo tells me. “There are a lot of economic reasons to do the [energy] transition.” It could attract new companies such as data centers, which would create new jobs, he argues. It could reduce Sardinia’s reliance on imported gas and fuel, making the island more independent. New economic activity on the island might help reverse its population decline, he adds. And while what’s happening on Sardinia is unique, it also represents a larger trend: A growing number of communities around the world are opposing wind- and solar-farm construction, to the consternation of stakeholders. By 2025, nearly one-fourth of the counties in the United States had enacted some impediment to new utility-scale wind and solar energy—up from as few as 15 percent two years earlier, according to a USA Today analysis. In Africa, community pushback successfully canceled major projects such as the 60-megawatt Kinangop Wind Park in Kenya. In India, local pastoralists are challenging the 13-gigawatt Ladakh solar and wind project. And the European Union’s top-down push for renewable energy has created opposition in many communities. Their reasons vary—land-use preferences, generational ethos, government resentment, property values, economic effects, aesthetics—but all of these struggles have this in common: The resisters are passionate and they are often successful in blocking development. This is a looming problem for the energy transition. Unlike large, centralized coal and nuclear power plants, renewable energy is geographically spread out, so it touches far more communities. Sardinia offers one of the clearest cases of what can go wrong when renewable-energy developers and authorities fail to consider the complexities of the local situation on the ground. Why is Sardinia resisting renewable energy? Roughly the size of New Hampshire, Sardinia juts out of the Mediterranean Sea about 200 kilometers west of Italy’s mainland. Technically it’s part of Italy, but Sardinians are quick to point out their island’s autonomous status—a subtle way of saying, “We do things our way.” Its mountains seem to echo the sentiment. With the highest peaks running in a chain along the east side of the island, Sardinia resolutely turns its back to the mainland. At first glance, the island looks like the kind of place that’s ripe for an energy transition. Its two coal plants are aging and are targeted to be shut down to meet climate commitments. It has no nuclear power, nor does it produce its own natural gas. Wind and sun, however, are abundant and could easily meet the energy needs of Sardinia’s sparse population of about 1.5 million. But while the resources may be ready for a transition, the people emphatically are not. When I first arrive in Sardinia and take in its beauty, I assume that the impetus behind the fight against wind and solar farms boils down to how they look. Waves of silicon, metal, and concrete would spoil views of Sardinia’s stunning beaches, rugged mountains, ancient pastures, and idyllic medieval villages, after all. Residents of the city of Orgosolo in 1969 famously stopped the construction of a military firing range on communal grazing land known as Pratobello. Its village walls are still covered in murals advocating social protest and antiauthoritarianism. Luigi Avantaggiato But the island’s aesthetic—and the tourism industry that depends on it—are only part of the equation. The far stronger cultural forces at play are rooted in Sardinia’s past. Over millennia, the island has endured successive invasions from outsiders seeking to exploit the land. These incursions, and Sardinians’ rebellious responses to them, have become an integral part of the island’s identity passed down through generations. The invasions started with the relatively peaceful settlement of the Phoenicians in the 9th and 8th centuries B.C.E. Then came the Romans, the Byzantines, and the Iberians, who conquered with violence, looting, and enslavement. But legend has it that despite the might of these ancient conquerors, pockets of Sardinia sometimes managed to defend themselves. “Not even the Roman empire could conquer the shepherds of the highland regions,” is the oft-repeated tale. Whether that’s true or just an idealization is beside the point; such stories serve as an enormous source of pride and identity. Sardinia exported about 30 percent of the electricity it generated in 2025, largely to Corsica and the Italian mainland via two existing submarine cables. The island is “fiercely proud of its identity…especially in the center of Sardinia, which was the most resistant part,” says Andrea Vargiu, a sociologist at the University of Sassari in Sardinia. “This long history of exploitation is still in our DNA, along with a proud sense of autonomy,” he says. Sardinia’s unification, in the mid-1800s, with what would become the Kingdom of Italy is seen by many as an act of colonization. It didn’t help that Italy then proceeded to exploit Sardinia’s forests and other resources for the benefit of the mainland—a practice that continued through the 20th century, says Vargiu. Sardinian bandits sometimes fought back with their own sense of justice, settling matters through raids, kidnappings, and violence. Their stories live on in Sardinian lore with an almost mythical quality, the brigands admired for their intractability. Pasquale Mereu, mayor of Orgosolo, helped organize the Pratobello 24 movement against renewable energy in Sardinia. Luigi Avantaggiato Italy’s use of the island for military purposes particularly irked locals. In a famous case in 1969, residents of the town of Orgosolo successfully thwarted the construction of a firing range on communal grazing land known as Pratobello. That name has since become synonymous with the defense of one’s territory, and a rallying cry. “Sardinia has always been a land of conquest,” says Pasquale Mereu, mayor of Orgosolo, who spoke with IEEE Spectrum through an interpreter. “We believe that even today we are still a colony of Italy, and I’m not ashamed to say it even though I represent an institution.” A longstanding mural on one of his village’s walls reads: “You are in the territory of Orgosolo; here the people rule supreme and the government obeys.” Sardinia’s History Shapes its Identity Driving around the island and talking to people, I can feel the weight of Sardinia’s history—and people’s propensity for holding onto it. Elaborate heritage festivals occur nearly every autumn weekend in the island’s interior. They’re well attended, multigenerational affairs that aim to keep old traditions alive. In the medieval town of Belvì, men roast chestnuts—marroni—over an open fire in a frying pan the size of a swimming pool and then serve them to the crowd by shoveling them into troughs. They’re delicious. In an adjacent amphitheater, the crowd sways along to costumed performers leading traditional dances. Then there are the Bronze Age stone structures, called nuraghi, that are pretty much everywhere. Built before the violent conquests, these conical towers have come to symbolize a romanticized vision of the heyday of Sardinia’s independence. More than 7,000 of them remain, ranging from unremarkable piles of rocks to complex towers, each one carefully documented on an interactive online map. I visit one of the more intact ones that’s fenced off and requires an admission fee. As I take some video with my phone, an employee asks me who I am and what I’m doing and informs me I’ll need to get permission from the government before posting anything online. This rock hollowed out by erosion and walled up with stones was likely used by shepherds as a shelter near the historic Sardinian village of Tempio Pausania. Luigi Avantaggiato But in interviews with residents, I’m continually reminded of the darker side of Sardinia’s past. People often bring up painful things that happened 50 or 500 years ago. A middle school science teacher named Giannina Serpi, and her husband, Roberto Moro, meet me at a café in the seaside town of Sant’Antioco. When I ask why people are so opposed to renewable energy, they (like many people I interviewed) point to the 1970s. Sheep return from pasture in Bonorva, Sardinia, near the Bonorva wind farm operated by EDF Renewables. Luigi Avantaggiato That decade brought a new kind of exploitation: not by empires or governments, but by technology companies. Petrochemical, aluminum, and other industrial companies from overseas built factories on the island, creating jobs and adjacent businesses. But after a few decades, economic and geopolitical factors led the companies to close the factories, sinking local economies and in some cases leaving behind toxic contamination. In the northern city of Porto Torres, several petrochemical plants, a thermoelectric power plant, and an industrial harbor employed about 8,000 workers in the early 1970s. But the oil crises of that decade took its toll on jobs, and when environmental contamination became evident in the 1990s, employment plunged further. By 2010, most of the petrochemical plants had closed. Studies show that residents of Porto Torres during that time had curiously high rates of death from cancer, although there is no consensus on the cause. Similarly, studies have found higher rates of lead in children in the Portovesme area in the southwest, about a 20-minute drive from where I sit with Serpi and Moro in Sant’Antioco. There, the U.S. aluminum producer Alcoa operated a smelter that employed about 500 people and supported an estimated 1,500 adjacent jobs. But the company shut down the smelter in 2012. Three years earlier, Russian aluminum manufacturer Rusal had idled its Eurallumina factory nearby. The impacts of these events still feel fresh, Serpi explains through a digital translator. She says she teaches this history to her students but doesn’t tell them how to feel about it. “I let them decide,” she says. Energy Colonialism in Sardinia Against this backdrop, renewable-energy developers in the early 2010s began sizing up Sardinia. They were drawn by the cheap land, low population, strong wind, and sun that shines an average of about 300 days a year. EF Solare Italia commissioned an 11-MW solar plant in 2010. Rome-based Enel Green Power began construction of a 90-MW wind farm in Portoscuso the following year. Other developers followed, and they mostly came from elsewhere—mainland Italy, Europe, and later, China. The way many Sardinians saw it, the new plants didn’t bring many long-lasting jobs. Most of the work ended after the design and installation phases, and profits went back to the companies’ headquarters outside of Sardinia, they argued. People called it “energy colonialism” and lauded landowners who refused to sell or lease their property to developers. Pink granite called Ghiandone Limbara was extracted from the Sinnada quarry in northern Sardinia from the late 1970s to 2011. Luigi Avantaggiato The uncle of Oxford’s Sotgiu is one of those landowners. She says that a couple of years ago a solar company asked him if he would allow the installation of an array on his family farm in Logudoro in Sardinia’s interior. “From that, he would have gotten something around €150,000 a year, which is more money than he’s seen in his life,” says Sotgiu. The money could have covered his three kids’ college education, she says. “But he refused.” He had many reasons. For one, switching from sheep grazing to the more passive business of leasing land would have put the fate of his income in the hands of an outsider. “If you deprive a region of any sort of economy that is self-reliant, then it’s really fragile,” says Sotgiu. Her uncle didn’t trust that the income would last, and worried he’d be left with a ruined farm, she says. Plus, his farm has been in the family for generations and one of his sons is interested in continuing the business. “So I understand his pride in saying, ‘No, this is my farm, I don’t care about the money,’” she says. Sardinia has one of the largest carbon footprints per capita in Europe. Despite that kind of grassroots resistance, development continued. In 2023, the Italian government authorized the construction of a 1-GW submarine power cable to connect Sardinia to Sicily and the Italian mainland. When completed, the bidirectional cable, called the Tyrrhenian Link, will increase electricity exchange between the regions, bolster grid reliability, and help grid operators efficiently use more renewable energy. Sardinian activists, however, view the cable as a way to justify even more construction of wind and solar plants, and to export the island’s energy for the benefit of non-Sardinians. The island already exports about 30 percent of its electricity, largely to Corsica and the Italian mainland via two existing submarine cables. The Florinas wind farm, commissioned in 2004, was one of the earliest wind farms built in Sardinia. Luigi Avantaggiato And then came the tipping point. In June 2024, in an effort to meet the European Union’s 2030 renewable energy targets, Italy committed to building more than 80 GW of new wind and solar energy capacity over December 2020 levels. The national government divvied up the burden among its regions and told Sardinia to build its portion, 6.2 GW. The move triggered an onslaught of requests from wind and solar developers wanting to build projects in Sardinia. The queue at one point topped 50 GW of grid-connection requests. That represented more than 700 solar and wind projects, many of which came from companies outside of Sardinia. The southern newspaper L’Unione Sarda ran wild with the numbers. Almost daily, for months, it published stories about the “wind assault.” The call-to-arms posts urged people to protest. “The Attack on the Landscape Does Not Stop; The Threat From Agrivoltaics Is Growing,” read a July 2024 headline. Unsubstantiated articles tried to link wind and solar developers to organized crime. “It was scaremongering,” says Sotgiu. “It was a little dishonest, as I saw it, because they kept exaggerating and scaring people into thinking that we were going to be invaded.” (Representatives of the newspaper declined to comment.) The numbers did scare people. Lost was the fact that a grid-connection request is just the start of a multiyear process that involves permitting and legal review and often ends in withdrawn or downsized projects. Submitting a request is inexpensive, and developers often cast a wide net by entering lots of these queues globally to increase the odds of being accepted. In the end, only a fraction come to fruition. In other words, building all, or even most, of the requested 50 GW was never going to happen. “I tried to explain this” to the public, says an industrial engineer at the University of Cagliari, in Sardinia, who asked to remain anonymous to avoid any detrimental impacts of speaking out. “I went to the regional television station. But it’s difficult with technical information. And the newspaper communication is so bad, and its impact is so strong in the community, that it’s very difficult to change people’s minds,” he says. Pratobello 2024 and Anti-Wind Protests And so the collective angst caused by powerful outsiders, industry, and the state united Sardinians into a singular cause. Faced with what felt like another attempted conquest, they did what their families and community had taught them to do: They resisted. Says Mereu: “This is what we are rebelling against: the idea that Sardinians are few and therefore must put up with everything.” In a nod to the 1969 resistance in Orgosolo, they dubbed the movement “Pratobello 2024.” Activist groups, called “committees,” organized protests, and created social media campaigns and videos. Thousands of people started showing up at planned demonstrations. A lawyer went on a hunger strike. Vandals unscrewed bolts on wind turbine blades and set fire to grid and construction equipment. Italy’s transmission system operator, Terna, had to switch to company cars without logos to avoid being targeted. Students studying the electricity system in a master’s program sponsored by Terna were verbally attacked at an airport, according to a professor at their school who spoke with me about the violence. Celebrities got involved. Italian actress and Bond Girl Caterina Murino met with Sardinia’s president to ask her to reject wind farms. Murino posted on Instagram: “Nobody touch Sardinia!!!!” On Italian national TV, the jazz legend Paolo Fresu performed on trumpet while popular TV host Geppi Cucciari read an impassioned lament about the exploitation of the island. Sardinian author Erre Push penned a graphic novel titled Fàula Birdi about a protagonist who resisted an imposition from outsiders. He wrote it upon the request of the activist group ReCommon, whose mission is to “challenge corporate and state power responsible for the plunder of territories.” Push hopes the book will inspire more people to follow the protagonist’s lead. “Renewables are another imposition like in the past—not to help Sardinians but to help external people like industry managers or founders of companies,” he told me through an interpreter. Concerned about the influx of solar and wind farms being built in Sardinia by outsiders, Roberto Pusceddu, under his pen name Erre Push, published a graphic novel that aimed to inspire young people to resist such impositions. Luigi Avantaggiato Mereu and a network of mayors drafted the petition that gathered so many signatures. The people had spoken. In response, Sardinian politicians passed a law that imposed an 18-month ban on construction of wind and solar projects within 7 km of a nuraghe or other archeological site. It wasn’t a total ban, but it might as well have been. “If you put a circle with a 7-km radius around each archeological site, you cover all of Sardinia,” says Emilio Ghiani, a power systems expert at the University of Cagliari. “In this way, it is impossible to find a place to install a new plant.” The move was like giving the Italian government—and the EU’s clean energy targets—the middle finger. And it sent renewable-energy developers scrambling. One company building an agriphotovoltaic plant raced to bring construction to 30 percent completion, which the new law said was the threshold for being allowed to proceed. The company asked not to be named in this story to avoid trouble. Furious, the government in Rome challenged the Sardinian regional law in Italy’s Constitutional Court, and in January this year it prevailed. In its decision, the court rejected the law, saying that renewable-energy projects should be evaluated case by case. Project development quickly resumed. So did the backlash. A headline in L’Unione Sarda declared: “Enough With Top-Down Decisions Without Consulting Communities.” Sardinia’s Renewable Energy Conflict Where the island goes from here is unclear. There’s a willingness among a portion of the population to move forward with an energy transition. For example, some of Sardinia’s largest cheese makers are powering their operations with renewable energy and installing systems to utilize waste heat for efficiency. But for the most part, the public isn’t budging in its resistance. Researchers are trying to dispel inaccurate information, but regional newspapers seem bent on perpetuating fear. Plus, there are technical issues to work out before a full-scale energy transition can be made. Sardinia’s transmission system was built around the centralized generation of two coal plants; it wasn’t made for the distributed generation of wind and solar plants. Renewables require a more dynamic grid, more energy storage, and a wider range of power sources to compensate for their intermittency. Engineers are working on it, but they’ve got a ways to go. The new Tyrrhenian Link undersea power cable will help with that. By connecting Sardinia, Sicily, and the mainland, the cable creates more flexibility in the system. When wind or solar generation slows in Sardinia, for example, electricity from the mainland can fill in the gap, and vice versa. “It will increase the reliability of the system, and after it’s installed, it will be possible to switch off the old generation plants that use coal,” says Ghiani. In January, Terna finished laying the western section of the cable between Sardinia and Sicily, and in April it completed the eastern section between Sicily and Campania on the mainland. Doing so set a world record for power cable depth, at 2,150 meters below sea level, according to Terna. Italy originally ordered Sardinia’s two coal plants to shut down by 2025 but later extended the deadline to 2038. The link is one of the most innovative high-voltage direct current (HVDC) projects in Europe. It can move up to a gigawatt of power and reverse that power flow nearly instantaneously. By using voltage source converter (VSC) technology, it can also help prevent power-flow problems by regulating frequency and smoothing out oscillations in the grid in real time. And it has black-start capability: In the event of a shutdown, it can help restore the grid without relying on an external electric network. These features are particularly helpful for an isolated network like Sardinia’s. Italy has created new incentives and regulations to build a market for grid-scale energy storage. Having plenty of storage is a key to scaling up renewables because it provides backup power when the wind isn’t blowing or the sun isn’t shining. To this end, Italy created MACSE, an auction that gives storage developers revenue certainty. Its name translates to mechanism for the procurement of electricity storage capacity. The first auction round, in September, successfully awarded 10 GWh. Energy experts in Sardinia are also working with policymakers to change the rules around grid-connection requests. But these kinds of nerdy details don’t grace most household conversations. Industrial Sites Host Energy Storage Something more accessible that the public can get behind is building renewables on Sardinia’s abandoned industrial sites. “To be honest, not everything is so beautiful here. We have a lot of industrial areas where you can place PV panels. We have a lot of rooftops,” electrical engineer Pilo says. “We have unused coal mines.” I visit one such project that’s proceeding with local support—or at least without much opposition. It’s a coal mine near Gonnesa that shut down in 2018 and is now being turned into a data center and a pumped-hydro energy storage system. The plan is to move water through the mine’s vertical geometry via an enclosed membrane—like a soft pipe—and use the flow to turn a turbine that generates electricity. The water then gets pumped back to the surface and stored in pear-shaped vessels above ground. The scheme will help power the data center, which will be built both above and below ground, including in the mine’s largest chambers nearly 500 meters below the Earth’s surface. Energy Vault will remove old mining equipment from the Carbosulcis coal mine near Gonnesa to make way for an underground data center [above]. It will be powered by a pumped-hydro energy storage system that flows through the mine’s vertical geometry and stores water in above-ground tanks [top].Luigi Avantaggiato Energy storage developer Energy Vault is building it, and despite being based in Lugano, Switzerland—that is, not Sardinia—the company seems to have avoided protest. It helps that the mine is owned by Carbosulcis, a Sardinian regional-government-owned company, which is calling the shots on the project. Plus, doing nothing with the mine costs money. The mine closed eight years ago because it wasn’t profitable, but Carbosulcis must continue maintaining it because of its high methane emissions, which require monitoring and ventilation to prevent explosions and leaks. Carbosulcis managers figured that if they’re going to continue putting money and personnel into the mine, they might as well do something useful with it, Luca Manzella, vice president for Europe, Middle East, and Africa at Energy Vault, says as he and I tour the mine. An innovative project in Sardinia’s interior—Energy Dome’s grid-scale carbon dioxide battery—seems to be avoiding protest as well. Built in a gated industrial complex near Ottana, this energy-storage facility looks like a giant bubble—the kind that fits over a stadium or tennis complex. It’s filled with carbon dioxide that is compressed to store 200 MWh of electricity for the grid. Although the bubble is visible from several of the surrounding hillside villages, and although the developer is headquartered on the mainland, there’s little sign of public pushback. Energy Dome began operating its 20-megawatt, long-duration energy-storage facility in July 2025 in Ottana, Sardinia. In partnership with Google, the company this year aims to build replicas of the system on multiple continents.Luigi Avantaggiato Another path forward is through “energy communities.” In this grassroots approach, consumers work together to build their own solar plant or other power generation. Dozens of these communities are already active on the island, according to the Sardinian Electricity Association, a group that provides guidance to consumers. But by far the greatest need is for energy developers and authorities to understand the people and the history of the land on which they want to build. “When Europe or the national government make a law, they have to also consider the background of Sardinian people and why they are so afraid,” says Simone Micheletti, CEO at Futura Group, a renewable-energy developer based in Serramanna, Sardinia. “You cannot apply the same law to Sweden and Sicily. Sometimes you need to understand [the situation] locally,” he says. Decision makers everywhere would be wise to listen. Otherwise, they may suffer the same fate as their counterparts in Sardinia: despised by locals, delayed by politics, and surprised at how badly it all went. Special thanks to Luigi Avantaggiato for interpreting and additional reporting. This story was updated on 13 May, 2026 to correct the percentage of electricity that Sardinia exports.
Just before midnight on Nov. 24, 2025, New Castle County police officers conducting a routine property check in Wilmington’s Canby Park spotted a white Toyota Tacoma parked after hours. What initially appeared to be a standard traffic stop uncovered a detailed terror plot. The suspect — a University of Delaware student — was found in possession of a converted machine gun, more than 100 rounds of ammunition, body armor, and a handwritten notebook mapping out a planned attack on the campus police department, including entry points, escape routes, and the name of a specific officer. When FBI agents interviewed him, The post The Cop on the Corner Is Our First Line of Defense: Local Police and the Surveillance Detection Gap appeared first on War on the Rocks.
This article is brought to you by DAIMON Robotics. This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore. The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data. Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON Robotics Behind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and his team have pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision. We spoke with Prof. Wang about how tactile feedback aims to change dexterous manipulation, how the dataset initiative is foreseen to improve our understanding of robotic hands in natural environments, and where — from hotels to convenience stores in China — he sees touch-enabled robots making their first real-world inroads. Daimon-Infinity is the world’s largest omni-modal dataset for Physical AI, featuring million-hour scale multimodal data, ultra-high-res tactile feedback, data from 80+ real scenarios and 2,000+ human skills, and more.DAIMON Robotics The Dataset Initiative This month, DAIMON Robotics released the largest and most comprehensive robotic manipulation dataset with multiple leading academic institutions and enterprises. Why releasing the dataset now, rather than continuing to focus on product development? What impact will this have on the embodied intelligence industry? DAIMON Robotics has been around for almost two and a half years. We have been committed to developing high-resolution, multimodal tactile sensing devices to perceive the interaction between a robot’s hand (particularly its fingertips) and objects. Our devices have become quite robust. They are now accepted and used by a large segment of users, including academic and research institutes as well as leading humanoid robotics companies. As embodied AI continues to advance, the critical role of data has been clearer. Data scarcity remains a primary bottleneck in robot learning, particularly the lack of physical interaction data, which is essential for robots to operate effectively in the real world. Consequently, data quality, reliability, and cost have become major concerns in both research and commercial development. This is exactly where DAIMON excels. Our vision-based tactile technology captures high-quality, multimodal tactile data. Beyond basic contact forces, it records deformation, slip and friction, material properties and surface textures — enabling a comprehensive reconstruction of physical interactions. Building on our expertise in multimodal fusion, we have developed a robust data processing pipeline that seamlessly integrates tactile feedback with vision, motion trajectories, and natural language, transforming raw inputs into training-ready dataset for machine learning models. Recognizing the industry-wide data gap, we view large-scale data collection not only as our unique competitive advantage, but as a responsibility to the broader community. By building and open-sourcing the dataset, we aim to provide the high-quality “fuel” needed to power embodied AI, ultimately accelerating the real-world deployment of general-purpose robotic foundation models. The robotics industry is highly competitive, and many teams have chosen to focus on data. DAIMON is releasing a large and highly comprehensive cross-embodiment, vision-based tactile multimodal robotic manipulation dataset. How were you able to achieve this? We have a dedicated in-house team focused on expanding our capabilities, including building hardware devices and developing our own large-scale model. Although we are a relatively small company, our core tactile sensing technology and innovative data collection paradigm enable us to build large-scale dataset. Our approach is to broaden our offering. We have built the world’s largest distributed out-of-lab data collection network. Rather than relying on centralized data factories, this lightweight and scalable system allows data to be gathered across diverse real-world environments, enabling us to generate millions of hours of data per year. “To drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.” —Prof. Michael Yu Wang, DAIMON Robotics This dataset is being jointly developed with several institutions worldwide. What roles did they play in its development, and how will the dataset benefit their research and products? Besides China based teams, our partners include leading research groups from universities, such as Northwestern University and the National University of Singapore, as well as top global enterprises like Google DeepMind and China Mobile. Their decision to partner with DAIMON is a strong testament to the value of our tactile-rich dataset. Among the companies involved there are some that have already built their own models but are now incorporating tactile information. By deploying our data collection devices across research, manufacturing and other real-world scenarios, they help us to gather highly practical, application-driven data. In turn, our partners leverage the data to train models tailored to their specific use cases. Furthermore, to drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community. Equipped with Daimon’s visuotactile sensor, the gripper delicately senses contact and precisely controls force to pick up a fragile eggshell.Daimon Robotics From VLA to VTLA: Why Tactile Sensing Changes the Equation The mainstream paradigm in robotics is currently the Vision-Language-Action (VLA) model, but your team has proposed a Vision-Tactile-Language-Action (VTLA) model. Why is it necessary to incorporate tactile sensing? What does it enable robots to achieve, and which tasks are likely to fail without tactile feedback? Over these years of working to make generalist robots capable of performing manipulation tasks, especially dexterous manipulation — not just power grasping or holding an object, but manipulating objects and using tools to impart forces and motion onto parts — we see these robots being used in household as well as industrial assembly settings. It is well established that tactile information is essential for providing feedback about contact states so that robots can guide their hands and fingers to perform reliable manipulation. Without tactile sensing, robots are severely limited. They struggle to locate objects in dark environments, and without slip detection, they can easily drop fragile items like glass. Furthermore, the inability to precisely control force often leads to failed manipulation tasks or, in severe cases, physical damage. Naturally, the VLA approach needs to be enhanced to incorporate tactile information. We expanded the VLA framework to incorporate tactile data, creating the VTLA model. An additional benefit of our tactile sensor is that it is vision-based: We capture visual images of the deformation on the fingertip surface. We capture multiple images in a time sequence that encodes contact information, from which we can infer forces and other contact states. This aligns well with the visual framework that VLA is based upon. Having tactile information in a visual image format makes it naturally suitable for integration into the VLA framework, transforming it into a VTLA system. That is the key advantage: Vision-based tactile sensors provide very high resolution at the pixel level, and this data can be incorporated into the framework, whether it is an end-to-end model or another type of architecture. DAIMON has been known for its vision-based tactile sensors that can pack over 110,000 effective sensing units.DAIMON Robotics The Technology: Monochromatic Vision-based Tactile Sensing You and your team have spent many years deeply engaged in vision-based tactile sensing and have developed the world’s first monochromatic vision-based tactile sensing technology. Why did you choose this technical path? Once we started investigating tactile sensors, we understood our needs. We wanted sensors that closely mimic what we have under our fingertip skin. Physiological studies have well documented the capabilities humans have at their fingertips — knowing what we touch, what kind of material it is, how forces are distributed, and whether it is moving into the right position as our brain controls our hands. We knew that replicating these capabilities on a robot hand’s fingertips would help considerably. When we surveyed existing technologies, we found many types, including vision-based tactile sensors with tri-color optics and other simpler designs. We decided to integrate the best of these into an engineering-robust solution that works well without being overly complicated, keeping cost, reliability, and sensitivity within a satisfactory range, thus ultimately developing a monochromatic vision-based tactile sensing technique. This is fundamentally an engineering approach rather than a purely scientific one, since a great deal of foundational research already existed. With the growing realization of the necessity of tactile data, all of this will advance hand in hand. DAIMON vision-based tactile sensor captures high-quality, multimodal tactile data.DAIMON Robotics Last year, DAIMON launched a multi-dimensional, high-resolution, high-frequency vision-based tactile sensor. Compared with traditional tactile sensors, where does its core advantage lie? Which industries could it potentially transform? The key features of our sensors are the density of distributed force measurement and the deformation we can capture over the area of a fingertip. I believe we have the highest density in terms of sensing units. That is one very important metric. The other is dynamics: the frequency and bandwidth — how quickly we can detect force changes, transmit signals, and process them in real time. Other important aspects are largely engineering-related, such as reliability, drift, durability of the soft surface, and resistance to interference from magnetic, optical, or environmental factors. A growing number of researchers and companies are recognizing the importance of tactile sensing and adopting our technology. I believe the advances in tactile sensing will elevate the entire community and industry to a higher level. One of our potential customers is deploying humanoid robots in a small convenience store, with densely packed shelves where shelf space is at a premium. The robot needs to reach into very tight spaces — tighter than books on a shelf — to pick out an object. Current two-jaw parallel grippers cannot fit into most of these spaces. Observing how humans pick up objects, you clearly need at least three slim fingers to touch and roll the object toward you and secure it. Thus, we are starting to see very specific needs where tactile sensing capabilities are essential. From Academia to Startup After 40 years in academia — founding the HKUST Robotics Institute, earning prestigious honors including IEEE Fellow, and serving as Editor-in-Chief of IEEE TASE — what motivated you to found DAIMON Robotics? I have come a long way. I started learning robotics during my PhD at Carnegie Mellon, where there were truly remarkable groups working on locomotion under Marc Raibert, who founded Boston Dynamics, and on manipulation under my advisor, Matt Mason, a leader in the field. We have been working on dexterous manipulation, not only at Carnegie Mellon, but globally for many years. However, progress has been limited for a long time, especially in building dexterous hands and making them work. Only recently have locomotion robots truly taken off, and only in the last few years have we begun to see major advancements in robot hands. There is clearly room for advancing manipulation capabilities, which would enable robots to do work like humans. While at Hong Kong University of Science and Technology, I saw increasingly greater people entering this area in the form of students and postdoctoral researchers. We wanted to jumpstart our effort by leveraging the available capital and talent resources. Fortunately, one of my postdocs, Dr. Duan Jianghua, has a strong sense for commercial opportunities. Recognizing the rapid growth of robotics market and the unique value that our vision-based tactile sensing technology could bring, together we started DAIMON Robotics, and it has progressed well. The community has grown tremendously in China, Japan, Korea, the U.S., and Europe. Robots equipped with DAIMON technology have been deployed in factory settings. The company aims to enable robots to achieve “embodied intelligence” and close the gap between what they can see and what they can feel.DAIMON Robotics Business Model and Commercial Strategy What is DAIMON’s current business model and strategic focus? What role does the dataset release play in your commercial strategy? We started as a device company focused on making highly capable tactile sensors, especially for robot hands. But as technology and business developed, everyone realized it is not just about one component, rather the entire technology chain: devices, data of adequate quality and quantity, and finally the right framework to build, train, and deploy models on robots in real application environments. Our business strategy is best described as “3D”: Devices, Data, and Deployment. We build devices for data collection, our own ecosystem, and for deploying them in our partners’ potential application domains. This enables the collection of real-world tactile-rich data and complete closed-loop validation. This will become an integral part of the 3D business model. Most startups in this space are following a similar path until eventually some may become more specialized or more tightly integrated with other companies. For now, it is mostly vertical integration. Embodied Skills and the Convergence Moment You’ve introduced the concept of “embodied skills” as essential for humanoid robots to move beyond having just an advanced AI “brain.” What prompted this insight? What new capabilities could embodied skills enable? After the rapid evolution of models and hardware over the past two years, has your definition or roadmap for embodied skills evolved? We have come a long way now see a convergence point where electrical, electronic, and mechatronic hardware technologies have advanced tremendously in last two decades. Robots are now fully electric, do not require hydraulics, because hardware has evolved rapidly. Modern electronics provide tremendous bandwidth with high torques. If we can build intelligence into these systems, we can create truly humanoid robots with the ability to operate in unstructured environments, make decisions, and take actions autonomously. “Our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans.” —Prof. Michael Yu Wang, DAIMON Robotics AI has arrived at exactly the right time. Enormous resources have been invested in AI development, especially large language models, which are now being generalized into world models that enable physical AI capabilities. We would like to see these manifested in real-world systems. While both AI and core hardware technologies continue to evolve, the focus is much clearer now. For example, human-sized robots are preferred in a home environment. This is an exciting domain with a promise of great societal benefit if we can eventually achieve safe, reliable, and cost-effective robots. The Road to Real-World Deployment Today, many robots can deliver impressive demos, yet there remains a gap before they truly enter real-world applications. What could be a potential trigger for real-world deployment? Which scenarios are most likely to achieve large-scale deployment first? I think the road toward large-scale deployment of generalist robots is still long, but we are starting to see signs of feasibility within specific domains. It is very similar to autonomous vehicles, where we are yet to see full deployment of robo-taxis, while we have already started to find mobile robots and smaller vehicles widely deployed in the hospitality industry. Virtually every major hotel in China now has a delivery robot — no arms, just a vehicle that picks up items from the hotel lobby (e.g., food deliveries). The delivery person just loads the food and selects the room number. It is up to the robot thereafter to navigate and reach the guest’s room, which includes using the elevator, to deliver the food. This is already nearly 100 percent deployed in major Chinese hotels. Hotel and restaurant robots are viewed as a model for deploying humanoid robots in specific domains like overnight drugstores and convenience stores. I expect complete deployment in such settings within a short timeframe, followed by other applications. Overall, we can expect autonomous robots, including humanoids, to progressively penetrate specific sectors, delivering value in each and expanding into others. Ultimately, our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans. By seamlessly integrating into our homes and daily lives, they will genuinely benefit and serve humanity. This interview has been edited for length and clarity.
When it comes to AI models, size matters. Even though some artificial-intelligence experts warn that scaling up large language models (LLMs) is hitting diminishing performance returns, companies are still coming out with ever larger AI tools. Meta’s latest Llama release had a staggering 2 trillion parameters that define the model. As models grow in size, their capabilities increase. But so do the energy demands and the time it takes to run the models, which increases their carbon footprint. To mitigate these issues, people have turned to smaller, less capable models and using lower-precision numbers whenever possible for the model parameters. But there is another path that may retain a staggeringly large model’s high performance while reducing the time it takes to run an energy footprint. This approach involves befriending the zeros inside large AI models. For many models, most of the parameters—the weights and activations—are actually zero, or so close to zero that they could be treated as such without losing accuracy. This quality is known as sparsity. Sparsity offers a significant opportunity for computational savings: Instead of wasting time and energy adding or multiplying zeros, these calculations could simply be skipped; rather than storing lots of zeros in memory, one need only store the nonzero parameters. Unfortunately, today’s popular hardware, like multicore CPUs and GPUs, do not naturally take full advantage of sparsity. To fully leverage sparsity, researchers and engineers need to rethink and re-architect each piece of the design stack, including the hardware, low-level firmware, and application software. In our research group at Stanford University, we have developed the first (to our knowledge) piece of hardware that’s capable of calculating all kinds of sparse and traditional workloads efficiently. The energy savings varied widely over the workloads, but on average our chip consumed one-seventieth the energy of a CPU, and performed the computation on average eight times as fast. To do this, we had to engineer the hardware, low-level firmware, and software from the ground up to take advantage of sparsity. We hope this is just the beginning of hardware and model development that will allow for more energy-efficient AI. What is sparsity? Neural networks, and the data that feeds into them, are represented as arrays of numbers. These arrays can be one-dimensional (vectors), two-dimensional (matrices), or more (tensors). A sparse vector, matrix, or tensor has mostly zero elements. The level of sparsity varies, but when zeroes make up more than 50 percent of any type of array, it can stand to benefit from sparsity-specific computational methods. In contrast, an object that is not sparse—that is, it has few zeros compared with the total number of elements—is called dense. Sparsity can be naturally present, or it can be induced. For example, a social-network graph will be naturally sparse. Imagine a graph where each node (point) represents a person, and each edge (a line segment connecting the points) represents a friendship. Since most people are not friends with one another, a matrix representing all possible edges will be mostly zeros. Other popular applications of AI, such as other forms of graph learning and recommendation models, contain naturally occurring sparsity as well. Beyond naturally occurring sparsity, sparsity can also be induced within an AI model in several ways. Two years ago, a team at Cerebras showed that one can set up to 70 to 80 percent of parameters in an LLM to zero without losing any accuracy. Cerebras demonstrated these results specifically on Meta’s open-source Llama 7B model, but the ideas extend to other LLM models like ChatGPT and Claude. The case for sparsity Sparse computation’s efficiency stems from two fundamental properties: the ability to compress away zeros and the convenient mathematical properties of zeros. Both the algorithms used in sparse computation and the hardware dedicated to them leverage these two basic ideas. First, sparse data can be compressed, making it more memory efficient to store “sparsely”—that is, in something called a sparse data type. Compression also makes it more energy efficient to move data when dealing with large amounts of it. This is best understood by an example. Take a four-by-four matrix with three nonzero elements. Traditionally, this matrix would be stored in memory as is, taking up 16 spaces. This matrix can also be compressed into a sparse data type, getting rid of the zeros and saving only the nonzero elements. In our example, this results in 13 memory spaces as opposed to 16 for the dense, uncompressed version. These savings in memory increase with increased sparsity and matrix size. In addition to the actual data values, compressed data also requires metadata. The row and column locations of the nonzero elements also must be stored. This is usually thought of as a “fibertree”: The row labels containing nonzero elements are listed and linked to the column labels of the nonzero elements, which are then linked to the values stored in those elements. In memory, things get a bit more complicated still: The row and column labels for each nonzero value must be stored as well as the “segments” that indicate how many such labels to expect, so the metadata and data can be clearly delineated from one another. In a dense, noncompressed matrix data type, values can be accessed either one at a time or in parallel, and their locations can be calculated directly with a simple equation. However, accessing values in sparse, compressed data requires looking up the coordinates of the row index and using that information to “indirectly” look up the coordinates of the column index before finally reaching the value. Depending on the actual locations of the sparse data values, these indirect lookups can be extremely random, making the computation data-dependent and requiring the allocation of memory lookups on the fly. Second, two mathematical properties of zero let software and hardware skip a lot of computation. Multiplying any number by zero will result in a zero, so there’s no need to actually do the multiplication. Adding zero to any number will always return that number, so there’s no need to do the addition either. In matrix-vector multiplication, one of the most common operations in AI workloads, all computations except those involving two nonzero elements can simply be skipped. Take, for example, the four-by-four matrix from the previous example and a vector of four numbers. In dense computation, each element of the vector must be multiplied by the corresponding element in each row and then added together to compute the final vector. In this case, that would take 16 multiplication operations and 16 additions (or four accumulations). In sparse computation, only the nonzero elements of the vector need be considered. For each nonzero vector element, indirect lookup can be used to find any corresponding nonzero matrix element, and only those need to be multiplied and added. In the example shown here, only two multiplication steps will be performed, instead of 16. The trouble with GPUs and CPUs Unfortunately, modern hardware is not well suited to accelerating sparse computation. For example, say we want to perform a matrix-vector multiplication. In the simplest case, in a single CPU core, each element in the vector would be multiplied sequentially and then written to memory. This is slow, because we can do only one multiplication at a time. So instead people use CPUs with vector support or GPUs. With this hardware, all elements would be multiplied in parallel, greatly speeding up the application. Now, imagine that both the matrix and vector contain extremely sparse data. The vectorized CPU and GPU would spend most of their efforts multiplying by zero, performing completely ineffectual computations. Newer generations of GPUs are capable of taking some advantage of sparsity in their hardware, but only a particular kind, called structured sparsity. Structured sparsity assumes that two out of every four adjacent parameters are zero. However, some models benefit more from unstructured sparsity—the ability for any parameter (weight or activation) to be zero and compressed away, regardless of where it is and what it is adjacent to. GPUs can run unstructured sparse computation in software, for example, through the use of the cuSparse GPU library. However, the support for sparse computations is often limited, and the GPU hardware gets underutilized, wasting energy-intensive computations on overhead. Petra Péterffy When doing sparse computations in software, modern CPUs may be a better alternative to GPU computation, because they are designed to be more flexible. Yet, sparse computations on the CPU are often bottlenecked by the indirect lookups used to find nonzero data. CPUs are designed to “prefetch” data based on what they expect they’ll need from memory, but for randomly sparse data, that process often fails to pull in the right stuff from memory. When that happens, the CPU must waste cycles calling for the right data. Apple was the first to speed up these indirect lookups by supporting a method called an array-of-pointers access pattern in the prefetcher of their A14 and M1 chips. Although innovations in prefetching make Apple CPUs more competitive for sparse computation, CPU architectures still have fundamental overheads that a dedicated sparse computing architecture would not, because they need to handle general-purpose computation. Other companies have been developing hardware that accelerates sparse machine learning as well. These include Cerebras’s Wafer Scale Engine and Meta’s Training and Inference Accelerator (MTIA). The Wafer Scale Engine, and its corresponding sparse programming framework, have shown incredibly sparse results of up to 70 percent sparsity on LLMs. However, the company’s hardware and software solutions support only weight sparsity, not activation sparsity, which is important for many applications. The second version of the MTIA claims a sevenfold sparse compute performance boost over the MTIA v1. However, the only publicly available information regarding sparsity support in the MTIA v2 is for matrix multiplication, not for vectors or tensors. Although matrix multiplications take up the majority of computation time in most modern ML models, it’s important to have sparsity support for other parts of the process. To avoid switching back and forth between sparse and dense data types, all of the operations should be sparse. Onyx Instead of these halfway solutions, our team at Stanford has developed a hardware accelerator, Onyx, that can take advantage of sparsity from the ground up, whether it’s structured or unstructured. Onyx is the first programmable accelerator to support both sparse and dense computation; it’s capable of accelerating key operations in both domains. To understand Onyx, it is useful to know what a coarse-grained reconfigurable array (CGRA) is and how it compares with more familiar hardware, like CPUs and field-programmable gate arrays (FPGAs). CPUs, CGRAs, and FPGAs represent a trade-off between efficiency and flexibility. Each individual logic unit of a CPU is designed for a specific function that it performs efficiently. On the other hand, since each individual bit of an FPGA is configurable, these arrays are extremely flexible, but very inefficient. The goal of CGRAs is to achieve the flexibility of FPGAs with the efficiency of CPUs. CGRAs are composed of efficient and configurable units, typically memory and compute, that are specialized for a particular application domain. This is the key benefit of this type of array: Programmers can reconfigure the internals of a CGRA at a high level, making it more efficient than an FPGA but more flexible than a CPU. The Onyx chip, built on a coarse-grained reconfigurable array (CGRA), is the first (to our knowledge) to support both sparse and dense computations. Olivia Hsu Onyx is composed of flexible, programmable processing element (PE) tiles and memory (MEM) tiles. The memory tiles store compressed matrices and other data formats. The processing element tiles operate on compressed matrices, eliminating all unnecessary and ineffectual computation. The Onyx compiler handles conversion from software instructions to CGRA configuration. First, the input expression—for instance, a sparse vector multiplication—is translated into a graph of abstract memory and compute nodes. In this example, there are memories for the input vectors and output vectors, a compute node for finding the intersection between nonzero elements, and a compute node for the multiplication. The compiler figures out how to map the abstract memory and compute nodes onto MEMs and PEs on the CGRA, and then how to route them together so that they can transfer data between them. Finally, the compiler produces the instruction set needed to configure the CGRA for the desired purpose. Since Onyx is programmable, engineers can map many different operations, such as vector-vector element multiplication, or the key tasks in AI, like matrix-vector or matrix-matrix multiplication, onto the accelerator. We evaluated the efficiency gains of our hardware by looking at the product of energy used and the time it took to compute, called the energy-delay product (EDP). This metric captures the trade-off of speed and energy. Minimizing just energy would lead to very slow devices, and minimizing speed would lead to high-area, high-power devices. Onyx achieves up to 565 times as much energy-delay product over CPUs (we used a 12-core Intel Xeon CPU) that utilize dedicated sparse libraries. Onyx can also be configured to accelerate regular, dense applications, similar to the way a GPU or TPU would. If the computation is sparse, Onyx is configured to use sparse primitives, and if the computation is dense, Onyx is reconfigured to take advantage of parallelism, similar to how GPUs function. This architecture is a step toward a single system that can accelerate both sparse and dense computations on the same silicon. Just as important, Onyx enables new algorithmic thinking. Sparse acceleration hardware will not only make AI more performance- and energy efficient but also enable researchers and engineers to explore new algorithms that have the potential to dramatically improve AI. The future with sparsity Our team is already working on next-generation chips built off of Onyx. Beyond matrix multiplication operations, machine learning models perform other types of math, like nonlinear layers, normalization, the softmax function, and more. We are adding support for the full range of computations on our next-gen accelerator and within the compiler. Since sparse machine learning models may have both sparse and dense layers, we are also working on integrating the dense and sparse accelerator architecture more efficiently on the chip, allowing for fast transformation between the different data types. We’re also looking at ways to manage memory constraints by breaking up the sparse data more effectively so we can run computations on several sparse accelerator chips. We are also working on systems that can predict the performance of accelerators such as ours, which will help in designing better hardware for sparse AI. Longer term, we’re interested in seeing whether high degrees of sparsity throughout AI computation will catch on with more model types, and whether sparse accelerators become adopted at a larger scale. Building the hardware to unstructured sparsity and optimally take advantage of zeros is just the beginning. With this hardware in hand, AI researchers and engineers will have the opportunity to explore new models and algorithms that leverage sparsity in novel and creative ways. We see this as a crucial research area for managing the ever-increasing runtime, costs, and environmental impact of AI.